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Appl Clin Inform 2022; 13(01): 161-179
DOI: 10.1055/s-0041-1742218
Review Article

Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature

Brian J. Douthit
1   Tennessee Valley Healthcare System, U.S. Department of Veterans Affairs; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, United States
,
Rachel L. Walden
2   Annette and Irwin Eskind Family Biomedical Library, Vanderbilt University, Nashville, Tennessee, United States
,
Kenrick Cato
3   Department of Emergency Medicine, Columbia University School of Nursing, New York, New York, United States
,
Cynthia P. Coviak
4   Professor Emerita of Nursing, Grand Valley State University, Allendale, Michigan, United States
,
Christopher Cruz
5   Global Health Technology and Informatics, Chevron, San Ramon, California, United States
,
Fabio D'Agostino
6   Department of Medicine and Surgery, Saint Camillus International University of Health Sciences, Rome, Italy
,
Thompson Forbes
7   College of Nursing, East Carolina University, Greenville, North California, United States
,
Grace Gao
8   Department of Nursing, St Catherine University, Saint Paul, Minnesota, United States
,
Theresa A. Kapetanovic
9   College of Nursing, East Carolina University, Greenville, North California, United States
,
Mikyoung A. Lee
10   College of Nursing, Texas Woman's University, Denton, Texas, United States
,
Lisiane Pruinelli
11   School of Nursing, University of Minnesota, Minneapolis, Minnesota, United States
,
Mary A. Schultz
12   Department of Nursing, California State University, San Bernardino, California, United States
,
Ann Wieben
13   School of Nursing, University of Wisconsin-Madison, Wisconsin, United States
,
Alvin D. Jeffery
14   School of Nursing, Vanderbilt University; Tennessee Valley Healthcare System, U.S. Department of Veterans Affairs, Nashville, Tennessee, United States
› Author Affiliations Funding This study was funded by the Agency for Healthcare Research and Quality and the Patient-Centered Outcomes Research Institute (grant: K12 HS026395) as well as the resources and use of facilities at the Department of Veterans Affairs, Tennessee Valley Healthcare System.
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Abstract

Background The term “data science” encompasses several methods, many of which are considered cutting edge and are being used to influence care processes across the world. Nursing is an applied science and a key discipline in health care systems in both clinical and administrative areas, making the profession increasingly influenced by the latest advances in data science. The greater informatics community should be aware of current trends regarding the intersection of nursing and data science, as developments in nursing practice have cross-professional implications.

Objectives This study aimed to summarize the latest (calendar year 2020) research and applications of nursing-relevant patient outcomes and clinical processes in the data science literature.

Methods We conducted a rapid review of the literature to identify relevant research published during the year 2020. We explored the following 16 topics: (1) artificial intelligence/machine learning credibility and acceptance, (2) burnout, (3) complex care (outpatient), (4) emergency department visits, (5) falls, (6) health care–acquired infections, (7) health care utilization and costs, (8) hospitalization, (9) in-hospital mortality, (10) length of stay, (11) pain, (12) patient safety, (13) pressure injuries, (14) readmissions, (15) staffing, and (16) unit culture.

Results Of 16,589 articles, 244 were included in the review. All topics were represented by literature published in 2020, ranging from 1 article to 59 articles. Numerous contemporary data science methods were represented in the literature including the use of machine learning, neural networks, and natural language processing.

Conclusion This review provides an overview of the data science trends that were relevant to nursing practice in 2020. Examinations of such literature are important to monitor the status of data science's influence in nursing practice.

Keywords

data analytics - artificial intelligence - nursing research - outcome and process assessment

Protection of Human and Animal Subject

This research does not involve human subjects.


Supplementary Material



Publication History

Received: 24 June 2021

Accepted: 12 December 2021

Article published online:
09 February 2022

© 2022. Thieme. All rights reserved.

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 

  • References

  • 1 Johnson KE, McMorris BJ, Raynor LA, Monsen KA. What big size you have! Using effect sizes to determine the impact of public health nursing interventions. Appl Clin Inform 2013; 4 (03) 434-444
    Article in Thieme ConnectPubMedGoogle Scholar
  • 2 Roosan D, Del Fiol G, Butler J. et al. Feasibility of population health analytics and data visualization for decision support in the infectious diseases domain: a pilot study. Appl Clin Inform 2016; 7 (02) 604-623
    Article in Thieme ConnectPubMedGoogle Scholar
  • 3 Zhou Y, Zhao L, Zhou N. et al. Predictive big data analytics using the UK biobank data. Sci Rep 2019; 9 (01) 6012
    CrossrefPubMedGoogle Scholar
  • 4 Center for Nursing Informatics. 2019 Nursing Knowledge: Big Data Science Conference. Accessed December 24, 2021 at: https://nursing.umn.edu/centers/center-nursing-informatics/previous-conferences/2019-nursing-knowledge-big-data-science
    PubMedGoogle Scholar
  • 5 Grant MJ, Booth A. A typology of reviews: an analysis of 14 review types and associated methodologies. Health Info Libr J 2009; 26 (02) 91-108
    CrossrefPubMedGoogle Scholar
  • 6 Jones TL. Outcome measurement in nursing: imperatives, ideals, history, and challenges. Online J Issues Nurs 2016; 21 (02) 1
    CrossrefPubMedGoogle Scholar
  • 7 Heslop L, Lu S, Xu X. Nursing-sensitive indicators: a concept analysis. J Adv Nurs 2014; 70 (11) 2469-2482
    CrossrefPubMedGoogle Scholar
  • 8 Ouzzani M, Hammady H, Fedorowicz Z, Elmagarmid A. Rayyan-a web and mobile app for systematic reviews. Syst Rev 2016; 5 (01) 210
    CrossrefPubMedGoogle Scholar
  • 9 Choi K, Choi SJ, Kim ES. Computer-Aided Diagonosis for Colorectal Cancer using Deep Learning with Visual Explanations. Annu Int Conf IEEE Eng Med Biol Soc 2020; 2020: 1156-1159
    PubMedGoogle Scholar
  • 10 Hatwell J, Gaber MM, Atif Azad RM. Ada-WHIPS: explaining AdaBoost classification with applications in the health sciences. BMC Med Inform Decis Mak 2020; 20 (01) 250
    CrossrefPubMedGoogle Scholar
  • 11 Mensa E, Colla D, Dalmasso M. et al. Violence detection explanation via semantic roles embeddings. BMC Med Inform Decis Mak 2020; 20 (01) 263
    CrossrefPubMedGoogle Scholar
  • 12 Hanson A, Haddad LM. Nursing Rights of Medication Administration. In: StatPearls. Treasure Island, FL: StatPearls; 2021
    Google Scholar
  • 13 Malley A, Kenner C, Kim T, Blakeney B. The role of the nurse and the preoperative assessment in patient transitions. AORN J 2015; 102 (02) 181.e1-181.e9
    CrossrefPubMedGoogle Scholar
  • 14 Farid M, Purdy N, Neumann WP. Using system dynamics modelling to show the effect of nurse workload on nurses' health and quality of care. Ergonomics 2020; 63 (08) 952-964
    CrossrefPubMedGoogle Scholar
  • 15 Silva E, Aguiar J, Reis LP, Sá JOE, Gonçalves J, Carvalho V. Stress among Portuguese medical students: the EuStress solution. J Med Syst 2020; 44 (02) 45
    CrossrefPubMedGoogle Scholar
  • 16 Bodenheimer T, Sinsky C. From triple to quadruple aim: care of the patient requires care of the provider. Ann Fam Med 2014; 12 (06) 573-576
    CrossrefPubMedGoogle Scholar
  • 17 Jones A, Bronskill SE, Seow H, Junek M, Feeny D, Costa AP. Associations between continuity of primary and specialty physician care and use of hospital-based care among community-dwelling older adults with complex care needs. PLoS One 2020; 15 (06) e0234205
    CrossrefPubMedGoogle Scholar
  • 18 Knox S, Downer B, Haas A, Middleton A, Ottenbacher KJ. Dementia severity associated with increased risk of potentially preventable readmissions during home health care. J Am Med Dir Assoc 2020; 21 (04) 519-524.e3
    CrossrefPubMedGoogle Scholar
  • 19 Osakwe ZT, Sosina OA, Agu N, Fleur-Calixte RS. Home health agency factors associated with acute care hospitalization and emergency department use. Home Healthc Now 2020; 38 (02) 92-97
    CrossrefPubMedGoogle Scholar
  • 20 Chase JD, Russell D, Huang L, Hanlon A, O'Connor M, Bowles KH. Relationships between race/ethnicity and health care utilization among older post-acute home health care patients. J Appl Gerontol 2020; 39 (02) 201-213
    CrossrefPubMedGoogle Scholar
  • 21 Courtright KR, Jordan L, Murtaugh CM. et al. Risk factors for long-term mortality and patterns of end-of-life care among medicare sepsis survivors discharged to home health care. JAMA Netw Open 2020; 3 (02) e200038-e200038
    CrossrefPubMedGoogle Scholar
  • 22 Griffith KN, Prentice JC, Mohr DC, Conlin PR. Predicting 5- and 10-year mortality risk in older adults with diabetes. Diabetes Care 2020; 43 (08) 1724-1731
    CrossrefPubMedGoogle Scholar
  • 23 Manz CR, Chen J, Liu M. et al. Validation of a machine learning algorithm to predict 180-day mortality for outpatients with cancer. JAMA Oncol 2020; 6 (11) 1723-1730
    CrossrefPubMedGoogle Scholar
  • 24 Schluter PJ, Askew DA, Jamieson HA, Arnold EP. Urinary and fecal incontinence are independently associated with falls risk among older women and men with complex needs: A national population study. Neurourol Urodyn 2020; 39 (03) 945-953
    CrossrefPubMedGoogle Scholar
  • 25 Ahlström G, Axmon A, Sandberg M, Hultqvist J. Specialist psychiatric health care utilization among older people with intellectual disability - predictors and comparisons with the general population: a national register study. BMC Psychiatry 2020; 20 (01) 70
    CrossrefPubMedGoogle Scholar
  • 26 Schultebraucks K, Shalev AY, Michopoulos V. et al. A validated predictive algorithm of post-traumatic stress course following emergency department admission after a traumatic stressor. Nat Med 2020; 26 (07) 1084-1088
    CrossrefPubMedGoogle Scholar
  • 27 Kuo YH, Chan NB, Leung JMY. et al. An integrated approach of machine learning and systems thinking for waiting time prediction in an emergency department. Int J Med Inform 2020; 139: 104143
    CrossrefPubMedGoogle Scholar
  • 28 Kogan E, Twyman K, Heap J, Milentijevic D, Lin JH, Alberts M. Assessing stroke severity using electronic health record data: a machine learning approach. BMC Med Inform Decis Mak 2020; 20 (01) 8
    CrossrefPubMedGoogle Scholar
  • 29 Tsiklidis EJ, Sims C, Sinno T, Diamond SL. Using the National Trauma Data Bank (NTDB) and machine learning to predict trauma patient mortality at admission. PLoS One 2020; 15 (11) e0242166
    CrossrefPubMedGoogle Scholar
  • 30 Schuler A, O'Súilleabháin L, Rinetti-Vargas G. et al. Assessment of value of neighborhood socioeconomic status in models that use electronic health record data to predict health care use rates and mortality. JAMA Netw Open 2020; 3 (10) e2017109
    CrossrefPubMedGoogle Scholar
  • 31 Plante TB, Blau AM, Berg AN. et al. Development and external validation of a machine learning tool to rule out covid-19 among adults in the emergency department using routine blood tests: a large, multicenter, real-world study. J Med Internet Res 2020; 22 (12) e24048
    CrossrefPubMedGoogle Scholar
  • 32 Klang E, Barash Y, Soffer S. et al. Promoting head CT exams in the emergency department triage using a machine learning model. Neuroradiology 2020; 62 (02) 153-160
    CrossrefPubMedGoogle Scholar
  • 33 Li J, Li BY, Wei ZJ, Zhao YZ, Li TS. Application research on gated recurrent unit deep learning prediction and graded early warning of emergency department visits based on meteorological environmental data. Biomed Environ Sci 2020; 33 (10) 817-820
    PubMedGoogle Scholar
  • 34 Jang SB, Lee SH, Lee DE. et al. Deep-learning algorithms for the interpretation of chest radiographs to aid in the triage of COVID-19 patients: A multicenter retrospective study. PLoS One 2020; 15 (11) e0242759
    CrossrefPubMedGoogle Scholar
  • 35 Jang DH, Kim J, Jo YH. et al. Developing neural network models for early detection of cardiac arrest in emergency department. Am J Emerg Med 2020; 38 (01) 43-49
    CrossrefPubMedGoogle Scholar
  • 36 Huang Y, Xu C, Ji M, Xiang W, He D. Medical service demand forecasting using a hybrid model based on ARIMA and self-adaptive filtering method. BMC Med Inform Decis Mak 2020; 20 (01) 237
    CrossrefPubMedGoogle Scholar
  • 37 Fernandes M, Mendes R, Vieira SM. et al. Predicting Intensive Care Unit admission among patients presenting to the emergency department using machine learning and natural language processing. PLoS One 2020; 15 (03) e0229331
    CrossrefPubMedGoogle Scholar
  • 38 Topaz M, Woo K, Ryvicker M, Zolnoori M, Cato K. Home healthcare clinical notes predict patient hospitalization and emergency department visits. Nurs Res 2020; 69 (06) 448-454
    CrossrefPubMedGoogle Scholar
  • 39 Langer T, Favarato M, Giudici R. et al. Development of machine learning models to predict RT-PCR results for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in patients with influenza-like symptoms using only basic clinical data. Scand J Trauma Resusc Emerg Med 2020; 28 (01) 113
    CrossrefPubMedGoogle Scholar
  • 40 Lee YS, Choi EJ, Kim YH, Park HA. Factors influencing falls in high- and low-risk patients in a tertiary hospital in Korea. J Patient Saf 2020; 16 (04) e376-e382
    CrossrefPubMedGoogle Scholar
  • 41 Lindberg DS, Prosperi M, Bjarnadottir RI. et al. Identification of important factors in an inpatient fall risk prediction model to improve the quality of care using EHR and electronic administrative data: a machine-learning approach. Int J Med Inform 2020; 143: 104272
    CrossrefPubMedGoogle Scholar
  • 42 Moskowitz G, Egorova NN, Hazan A, Freeman R, Reich DL, Leipzig RM. Using electronic health records to enhance predictions of fall risk in inpatient settings. Jt Comm J Qual Patient Saf 2020; 46 (04) 199-206
    PubMedGoogle Scholar
  • 43 Stubbs B, Perara G, Koyanagi A. et al. Risk of hospitalized falls and hip fractures in 22,103 older adults receiving mental health care vs 161,603 controls: a large cohort study. J Am Med Dir Assoc 2020; 21 (12) 1893-1899
    CrossrefPubMedGoogle Scholar
  • 44 Tago M, Katsuki NE, Oda Y, Nakatani E, Sugioka T, Yamashita SI. New predictive models for falls among inpatients using public ADL scale in Japan: A retrospective observational study of 7,858 patients in acute care setting. PLoS One 2020; 15 (07) e0236130
    CrossrefPubMedGoogle Scholar
  • 45 Womack JA, Murphy TE, Bathulapalli H. et al. Serious falls in middle-aged veterans: development and validation of a predictive risk model. J Am Geriatr Soc 2020; 68 (12) 2847-2854
    CrossrefPubMedGoogle Scholar
  • 46 Ye C, Li J, Hao S. et al. Identification of elders at higher risk for fall with statewide electronic health records and a machine learning algorithm. Int J Med Inform 2020; 137: 104105
    CrossrefPubMedGoogle Scholar
  • 47 Zhao M, Li S, Xu Y, Su X, Jiang H. Developing a scoring model to predict the risk of injurious falls in elderly patients: a retrospective case-control study in multicenter acute hospitals. Clin Interv Aging 2020; 15: 1767-1778
    CrossrefPubMedGoogle Scholar
  • 48 Buisseret F, Catinus L, Grenard R. et al. Timed up and go and six-minute walking tests with wearable inertial sensor: one step further for the prediction of the risk of fall in elderly nursing home people. Sensors (Basel) 2020; 20 (11) E3207
    CrossrefPubMedGoogle Scholar
  • 49 Cella A, De Luca A, Squeri V. et al. Development and validation of a robotic multifactorial fall-risk predictive model: a one-year prospective study in community-dwelling older adults. PLoS One 2020; 15 (06) e0234904
    CrossrefPubMedGoogle Scholar
  • 50 Hsu YC, Weng HH, Kuo CY, Chu TP, Tsai YH. Prediction of fall events during admission using eXtreme gradient boosting: a comparative validation study. Sci Rep 2020; 10 (01) 16777
    CrossrefPubMedGoogle Scholar
  • 51 Imoto D, Sawada K, Horii M. et al. Factors associated with falls in Japanese polio survivors. Disabil Rehabil 2020; 42 (13) 1814-1818
    CrossrefPubMedGoogle Scholar
  • 52 Jung H, Park HA, Hwang H. Improving prediction of fall risk using electronic health record data with various types and sources at multiple times. Comput Inform Nurs 2020; 38 (03) 157-164
    CrossrefPubMedGoogle Scholar
  • 53 Rehman RZU, Zhou Y, Del Din S. et al. Gait analysis with wearables can accurately classify fallers from non-fallers: a step toward better management of neurological disorders. Sensors (Basel) 2020; 20 (23) E6992
    CrossrefPubMedGoogle Scholar
  • 54 Suzuki M, Yamamoto R, Ishiguro Y, Sasaki H, Kotaki H. Deep learning prediction of falls among nursing home residents with Alzheimer's disease. Geriatr Gerontol Int 2020; 20 (06) 589-594
    CrossrefPubMedGoogle Scholar
  • 55 Casilari E, Lora-Rivera R, García-Lagos F. A study on the application of convolutional neural networks to fall detection evaluated with multiple public datasets. Sensors (Basel) 2020; 20 (05) E1466
    CrossrefPubMedGoogle Scholar
  • 56 Charles A, Buckinx F, Locquet M. et al. Prediction of adverse outcomes in nursing home residents according to intrinsic capacity proposed by the World Health Organization. J Gerontol A Biol Sci Med Sci 2020; 75 (08) 1594-1599
    CrossrefPubMedGoogle Scholar
  • 57 Chen PL, Lin HY, Ong JR, Ma HP. Development of a fall-risk assessment profile for community-dwelling older adults by using the National Health Interview Survey in Taiwan. BMC Public Health 2020; 20 (01) 234
    CrossrefPubMedGoogle Scholar
  • 58 Lee SK, Ahn J, Shin JH, Lee JY. Application of machine learning methods in nursing home research. Int J Environ Res Public Health 2020; 17 (17) E6234
    CrossrefPubMedGoogle Scholar
  • 59 Magnuszewski L, Swietek M, Kasiukiewicz A, Kuprjanowicz B, Baczek J, Beata Wojszel Z. Health, functional and nutritional determinants of falls experienced in the previous year-a cross-sectional study in a geriatric ward. Int J Environ Res Public Health 2020; 17 (13) E4768
    CrossrefPubMedGoogle Scholar
  • 60 Umegaki H, Makino T, Uemura K. et al. Falls in community-dwelling prefrail older adults. Health Soc Care Community 2020; 28 (01) 110-115
    CrossrefPubMedGoogle Scholar
  • 61 Martinez M, De Leon PL. Falls risk classification of older adults using deep neural networks and transfer learning. IEEE J Biomed Health Inform 2020; 24 (01) 144-150
    CrossrefPubMedGoogle Scholar
  • 62 Zhao J, Liang G, Huang H. et al. Identification of risk factors for falls in postmenopausal women: a systematic review and meta-analysis. Osteoporos Int 2020; 31 (10) 1895-1904
    CrossrefPubMedGoogle Scholar
  • 63 Manis DR, McArthur C, Costa AP. Associations with rates of falls among home care clients in Ontario, Canada: a population-based, cross-sectional study. BMC Geriatr 2020; 20 (01) 80
    CrossrefPubMedGoogle Scholar
  • 64 Bucher BT, Shi J, Ferraro JP. et al. Portable automated surveillance of surgical site infections using natural language processing: development and validation. Ann Surg 2020; 272 (04) 629-636
    CrossrefPubMedGoogle Scholar
  • 65 Chang B, Sun Z, Peiris P, Huang ES, Benrashid E, Dillavou ED. Deep learning-based risk model for best management of closed groin incisions after vascular surgery. J Surg Res 2020; 254: 408-416
    CrossrefPubMedGoogle Scholar
  • 66 Mancini A, Vito L, Marcelli E. et al. Machine learning models predicting multidrug resistant urinary tract infections using “DsaaS”. BMC Bioinformatics 2020; 21 (Suppl. 10) 347
    CrossrefPubMedGoogle Scholar
  • 67 Mull HJ, Stolzmann KL, Shin MH, Kalver E, Schweizer ML, Branch-Elliman W. Novel method to flag cardiac implantable device infections by integrating text mining with structured data in the veterans health administration's electronic medical record. JAMA Netw Open 2020; 3 (09) e2012264-e2012264
    CrossrefPubMedGoogle Scholar
  • 68 Zachariah P, Sanabria E, Liu J, Cohen B, Yao D, Larson E. Novel strategies for predicting healthcare-associated infections at admission: implications for nursing care. Nurs Res 2020; 69 (05) 399-403
    CrossrefPubMedGoogle Scholar
  • 69 McHaney-Lindstrom M, Hebert C, Miller H, Moffatt-Bruce S, Root E. Network analysis of intra-hospital transfers and hospital onset clostridium difficile infection. Health Info Libr J 2020; 37 (01) 26-34
    CrossrefPubMedGoogle Scholar
  • 70 Meng L, Li J, He Y. et al. The risk factors analysis and establishment of an early warning model for healthcare-associated infections after pediatric cardiac surgery: A STROBE-compliant observational study. Medicine (Baltimore) 2020; 99 (49) e23324
    CrossrefPubMedGoogle Scholar
  • 71 Park JI, Bliss DZ, Chi CL, Delaney CW, Westra BL. Knowledge discovery with machine learning for hospital-acquired catheter-associated urinary tract infections. Comput Inform Nurs 2020; 38 (01) 28-35
    CrossrefPubMedGoogle Scholar
  • 72 Cai Y, Zhang W, Zhang R, Cui X, Fang J. Combined use of three machine learning modeling methods to develop a ten-gene signature for the diagnosis of ventilator-associated pneumonia. Med Sci Monit 2020; 26: e919035
    PubMedGoogle Scholar
  • 73 Chen CY, Lin WC, Yang HY. Diagnosis of ventilator-associated pneumonia using electronic nose sensor array signals: solutions to improve the application of machine learning in respiratory research. Respir Res 2020; 21 (01) 45
    CrossrefPubMedGoogle Scholar
  • 74 Poissy J, Damonti L, Bignon A. et al; FUNGINOS, Allfun French Study Groups. Risk factors for candidemia: a prospective matched case-control study. Crit Care 2020; 24 (01) 109
    CrossrefPubMedGoogle Scholar
  • 75 Ames CP, Smith JS, Gum JL. et al; European Spine Study Group and International Spine Study Group. Utilization of predictive modeling to determine episode of care costs and to accurately identify catastrophic cost nonwarranty outlier patients in adult spinal deformity surgery: a step toward bundled payments and risk sharing. Spine 2020; 45 (05) E252-E265
    CrossrefPubMedGoogle Scholar
  • 76 Coleman BC, Fodeh S, Lisi AJ. et al. Exploring supervised machine learning approaches to predicting Veterans Health Administration chiropractic service utilization. Chiropr Man Therap 2020; 28 (01) 47
    CrossrefPubMedGoogle Scholar
  • 77 de Carvalho LSF, Gioppato S, Fernandez MD. et al. Machine learning improves the identification of individuals with higher morbidity and avoidable health costs after acute coronary syndromes. Value Health 2020; 23 (12) 1570-1579
    CrossrefPubMedGoogle Scholar
  • 78 Hyer JM, Paredes AZ, White S, Ejaz A, Pawlik TM. Assessment of utilization efficiency using machine learning techniques: A study of heterogeneity in preoperative healthcare utilization among super-utilizers. Am J Surg 2020; 220 (03) 714-720
    CrossrefPubMedGoogle Scholar
  • 79 Lerner J, Ruppenkamp J, Etter K. et al. Preoperative behavioral health, opioid, and antidepressant utilization and 2-year costs after spinal fusion-revelations from cluster analysis. Spine 2020; 45 (02) E90-E98
    CrossrefPubMedGoogle Scholar
  • 80 Luo L, Li J, Lian S. et al. Using machine learning approaches to predict high-cost chronic obstructive pulmonary disease patients in China. Health Informatics J 2020; 26 (03) 1577-1598
    CrossrefPubMedGoogle Scholar
  • 81 Mazumdar M, Lin JJ, Zhang W. et al. Comparison of statistical and machine learning models for healthcare cost data: a simulation study motivated by Oncology Care Model (OCM) data. BMC Health Serv Res 2020; 20 (01) 350
    CrossrefPubMedGoogle Scholar
  • 82 Mortani Barbosa Jr. EJ, Kelly K. Statistical modeling can determine what factors are predictive of appropriate follow-up in patients presenting with incidental pulmonary nodules on CT. Eur J Radiol 2020; 128: 109062
    CrossrefPubMedGoogle Scholar
  • 83 Ng SHX, Rahman N, Ang IYH. et al. Characterising and predicting persistent high-cost utilisers in healthcare: a retrospective cohort study in Singapore. BMJ Open 2020; 10 (01) e031622
    CrossrefPubMedGoogle Scholar
  • 84 Wong ES, Schuttner L, Reddy A. Does machine learning improve prediction of VA primary care reliance?. Am J Manag Care 2020; 26 (01) 40-44
    CrossrefPubMedGoogle Scholar
  • 85 Xie Y, Nguyen QD, Hamzah H. et al. Artificial intelligence for teleophthalmology-based diabetic retinopathy screening in a national programme: an economic analysis modelling study. Lancet Digit Health 2020; 2 (05) e240-e249
    CrossrefPubMedGoogle Scholar
  • 86 Chen S, Bergman D, Miller K, Kavanagh A, Frownfelter J, Showalter J. Using applied machine learning to predict healthcare utilization based on socioeconomic determinants of care. Am J Manag Care 2020; 26 (01) 26-31
    CrossrefPubMedGoogle Scholar
  • 87 Chien I, Enrique A, Palacios J. et al. A machine learning approach to understanding patterns of engagement with internet-delivered mental health interventions. JAMA Netw Open 2020; 3 (07) e2010791
    CrossrefPubMedGoogle Scholar
  • 88 Zonta G, Malagù C, Gherardi S. et al. Clinical validation results of an innovative non-invasive device for colorectal cancer preventive screening through fecal exhalation analysis. Cancers (Basel) 2020; 12 (06) 1471
    CrossrefPubMedGoogle Scholar
  • 89 Hong JC, Eclov NCW, Dalal NH. et al. System for high-intensity evaluation during radiation therapy (SHIELD-RT): a prospective randomized study of machine learning-directed clinical evaluations during radiation and chemoradiation. J Clin Oncol 2020; 38 (31) 3652-3661
    CrossrefPubMedGoogle Scholar
  • 90 Santilli AML, Jamzad A, Janssen NNY. et al. Perioperative margin detection in basal cell carcinoma using a deep learning framework: a feasibility study. Int J CARS 2020; 15 (05) 887-896
    CrossrefPubMedGoogle Scholar
  • 91 Rozenblum R, Rodriguez-Monguio R, Volk LA. et al. Using a machine learning system to identify and prevent medication prescribing errors: a clinical and cost analysis evaluation. Jt Comm J Qual Patient Saf 2020; 46 (01) 3-10
    PubMedGoogle Scholar
  • 92 Guntuku SC, Schwartz HA, Kashyap A. et al. Variability in language used on social media prior to hospital visits. Sci Rep 2020; 10 (01) 4346
    CrossrefPubMedGoogle Scholar
  • 93 Chong LR, Tsai KT, Lee LL, Foo SG, Chang PC. Artificial intelligence predictive analytics in the management of outpatient mri appointment no-shows. AJR Am J Roentgenol 2020; 215 (05) 1155-1162
    CrossrefPubMedGoogle Scholar
  • 94 Akbarzadeh Khorshidi H, Hassani-Mahmooei B, Haffari G. An interpretable algorithm on post-injury health service utilization patterns to predict injury outcomes. J Occup Rehabil 2020; 30 (03) 331-342
    CrossrefPubMedGoogle Scholar
  • 95 Lauffenburger JC, Mahesri M, Choudhry NK. Not there yet: using data-driven methods to predict who becomes costly among low-cost patients with type 2 diabetes. BMC Endocr Disord 2020; 20 (01) 125
    CrossrefPubMedGoogle Scholar
  • 96 Wilson FA, Zallman L, Pagán JA. et al. Comparison of use of health care services and spending for unauthorized immigrants vs authorized immigrants or US citizens using a machine learning model. JAMA Netw Open 2020; 3 (12) e2029230
    CrossrefPubMedGoogle Scholar
  • 97 Benitez AE, Musinguzi N, Bangsberg DR. et al. Super learner analysis of real-time electronically monitored adherence to antiretroviral therapy under constrained optimization and comparison to non-differentiated care approaches for persons living with HIV in rural Uganda. J Int AIDS Soc 2020; 23 (03) e25467
    CrossrefPubMedGoogle Scholar
  • 98 Yu AT, Shakya R, Adhikari B. et al. A cluster-based, spatial-sampling method for assessing household healthcare utilization patterns in resource-limited settings. Clin Infect Dis 2020; 71 (Suppl. 03) S239-S247
    CrossrefPubMedGoogle Scholar
  • 99 Assaf D, Gutman Y, Neuman Y. et al. Utilization of machine-learning models to accurately predict the risk for critical COVID-19. Intern Emerg Med 2020; 15 (08) 1435-1443
    CrossrefPubMedGoogle Scholar
  • 100 Brown AW, Lee M, Lennon RJ, Niewczyk PM. Functional performance and discharge setting predict outcomes 3 months after rehabilitation hospitalization for stroke. J Stroke Cerebrovasc Dis 2020; 29 (05) 104746
    CrossrefPubMedGoogle Scholar
  • 101 Cholankeril G, Podboy A, Aivaliotis VI. et al. Association of digestive symptoms and hospitalization in patients with SARS-CoV-2 infection. Am J Gastroenterol 2020; 115 (07) 1129-1132
    CrossrefPubMedGoogle Scholar
  • 102 Hao B, Sotudian S, Wang T. et al. Early prediction of level-of-care requirements in patients with COVID-19. eLife 2020; 9: 9
    CrossrefPubMedGoogle Scholar
  • 103 Jehi L, Ji X, Milinovich A. et al. Development and validation of a model for individualized prediction of hospitalization risk in 4,536 patients with COVID-19. PLoS One 2020; 15 (08) e0237419
    CrossrefPubMedGoogle Scholar
  • 104 Russell D, Luth EA, Ryvicker M, Bowles KH, Prigerson HG. Live discharge from hospice due to acute hospitalization: the role of neighborhood socioeconomic characteristics and race/ethnicity. Med Care 2020; 58 (04) 320-328
    CrossrefPubMedGoogle Scholar
  • 105 Schrauben SJ, Chen HY, Lin E. et al; CRIC Study Investigators. Hospitalizations among adults with chronic kidney disease in the United States: a cohort study. PLoS Med 2020; 17 (12) e1003470
    CrossrefPubMedGoogle Scholar
  • 106 Steurer MA, Costello J, Baer RJ. et al. Cohort study of respiratory hospital admissions, air quality and sociodemographic factors in preterm infants born in California. Paediatr Perinat Epidemiol 2020; 34 (02) 130-138
    CrossrefPubMedGoogle Scholar
  • 107 Temkin-Greener H, Wang S, Caprio T, Mukamel DB, Cai S. Obesity among nursing home residents: association with potentially avoidable hospitalizations. J Am Med Dir Assoc 2020; 21 (09) 1331-1335
    CrossrefPubMedGoogle Scholar
  • 108 Williams BA, Geba D, Cordova JM, Shetty SS. A risk prediction model for heart failure hospitalization in type 2 diabetes mellitus. Clin Cardiol 2020; 43 (03) 275-283
    CrossrefPubMedGoogle Scholar
  • 109 Wu SS, Bellantoni M, Weiner JP. Geriatric syndrome risk factors among hospitalized postacute Medicare patients. Am J Manag Care 2020; 26 (10) e319-e326
    CrossrefPubMedGoogle Scholar
  • 110 Azar KMJ, Shen Z, Romanelli RJ. et al. Disparities in outcomes among COVID-19 patients in a large health care system in California. Health Aff (Millwood) 2020; 39 (07) 1253-1262
    CrossrefPubMedGoogle Scholar
  • 111 Wang A, Kho AN, French DD. Association of the Robert Wood Johnson Foundations' social determinants of health and Medicare hospitalisations for ischaemic strokes: a cross-sectional data analysis. Open Heart 2020; 7 (01) e001189
    CrossrefPubMedGoogle Scholar
  • 112 Rough K, Dai AM, Zhang K. et al. Predicting inpatient medication orders from electronic health record data. Clin Pharmacol Ther 2020; 108 (01) 145-154
    CrossrefPubMedGoogle Scholar
  • 113 Ioannou GN, Locke E, Green P. et al. Risk factors for hospitalization, mechanical ventilation, or death among 10 131 US veterans with SARS-CoV-2 infection. JAMA Netw Open 2020; 3 (09) e2022310
    CrossrefPubMedGoogle Scholar
  • 114 Phillips AZ, Rodriguez HPUS. U.S. county “food swamp” severity and hospitalization rates among adults with diabetes: A nonlinear relationship. Soc Sci Med 2020; 249: 112858
    CrossrefPubMedGoogle Scholar
  • 115 Colón-Emeric CS, Huang J, Pieper CF, Bettger JP, Roth DL, Sheehan OC. Cost trajectories as a measure of functional resilience after hospitalization in older adults. Aging Clin Exp Res 2020; 32 (12) 2595-2601
    CrossrefPubMedGoogle Scholar
  • 116 Zulman DM, Maciejewski ML, Grubber JM. et al. Patient-reported social and behavioral determinants of health and estimated risk of hospitalization in high-risk veterans affairs patients. JAMA Netw Open 2020; 3 (10) e2021457
    CrossrefPubMedGoogle Scholar
  • 117 Abujaber A, Fadlalla A, Gammoh D, Abdelrahman H, Mollazehi M, El-Menyar A. Prediction of in-hospital mortality in patients on mechanical ventilation post traumatic brain injury: machine learning approach. BMC Med Inform Decis Mak 2020; 20 (01) 336
    CrossrefPubMedGoogle Scholar
  • 118 Abujaber A, Fadlalla A, Gammoh D, Abdelrahman H, Mollazehi M, El-Menyar A. Prediction of in-hospital mortality in patients with post traumatic brain injury using National Trauma Registry and Machine Learning Approach. Scand J Trauma Resusc Emerg Med 2020; 28 (01) 44
    CrossrefPubMedGoogle Scholar
  • 119 Adjei Boakye E, Osazuwa-Peters N, Chen B. et al. Multilevel associations between patient- and hospital-level factors and in-hospital mortality among hospitalized patients with head and neck cancer. JAMA Otolaryngol Head Neck Surg 2020; 146 (05) 444-454
    CrossrefPubMedGoogle Scholar
  • 120 Allenbach Y, Saadoun D, Maalouf G. et al; DIMICOVID. Development of a multivariate prediction model of intensive care unit transfer or death: A French prospective cohort study of hospitalized COVID-19 patients. PLoS One 2020; 15 (10) e0240711
    CrossrefPubMedGoogle Scholar
  • 121 Ahmed N, Greenberg P. Early risk stratification of in hospital mortality following a ground level fall in geriatric patients with normal physiological parameters. Am J Emerg Med 2020; 38 (12) 2531-2535
    CrossrefPubMedGoogle Scholar
  • 122 Araujo GN, Silveira AD, Scolari FL. et al. Admission bedside lung ultrasound reclassifies mortality prediction in patients with ST-segment-elevation myocardial infarction. Circ Cardiovasc Imaging 2020; 13 (06) e010269
    CrossrefPubMedGoogle Scholar
  • 123 Altschul DJ, Unda SR, Benton J. et al. A novel severity score to predict inpatient mortality in COVID-19 patients. Sci Rep 2020; 10 (01) 16726
    CrossrefPubMedGoogle Scholar
  • 124 Awad A, Bader-El-Den M, McNicholas J, Briggs J, El-Sonbaty Y. Predicting hospital mortality for intensive care unit patients: Time-series analysis. Health Informatics J 2020; 26 (02) 1043-1059
    CrossrefPubMedGoogle Scholar
  • 125 Ayers B, Wood K, Gosev I, Prasad S. Predicting survival after extracorporeal membrane oxygenation by using machine learning. Ann Thorac Surg 2020; 110 (04) 1193-1200
    CrossrefPubMedGoogle Scholar
  • 126 Bacchi S, Oakden-Rayner L, Menon DK, Jannes J, Kleinig T, Koblar S. Stroke prognostication for discharge planning with machine learning: a derivation study. J Clin Neurosci 2020; 79: 100-103
    CrossrefPubMedGoogle Scholar
  • 127 Baxter SL, Klie AR, Saseendrakumar BR, Ye GY, Hogarth M, Nemati S. Predicting mortality in critical care patients with fungemia using structured and unstructured data. Annu Int Conf IEEE Eng Med Biol Soc 2020; 2020: 5459-5463
    PubMedGoogle Scholar
  • 128 Bertsimas D, Lukin G, Mingardi L. et al; Hellenic COVID-19 Study Group. COVID-19 mortality risk assessment: an international multi-center study. PLoS One 2020; 15 (12) e0243262
    CrossrefPubMedGoogle Scholar
  • 129 Burdick H, Pino E, Gabel-Comeau D. et al. Effect of a sepsis prediction algorithm on patient mortality, length of stay and readmission: a prospective multicentre clinical outcomes evaluation of real-world patient data from US hospitals. BMJ Health Care Inform 2020; 27 (01) e100109
    CrossrefPubMedGoogle Scholar
  • 130 Brajer N, Cozzi B, Gao M. et al. Prospective and external evaluation of a machine learning model to predict in-hospital mortality of adults at time of admission. JAMA Netw Open 2020; 3 (02) e1920733
    CrossrefPubMedGoogle Scholar
  • 131 Castro VM, McCoy TH, Perlis RH. Laboratory findings associated with severe illness and mortality among hospitalized individuals with coronavirus disease 2019 in Eastern Massachusetts. JAMA Netw Open 2020; 3 (10) e2023934
    CrossrefPubMedGoogle Scholar
  • 132 Cutti S, Klersy C, Favalli V. et al. A multidimensional approach of surgical mortality assessment and stratification (SMATT score). Sci Rep 2020; 10 (01) 10964
    CrossrefPubMedGoogle Scholar
  • 133 Datta S, Loftus TJ, Ruppert MM. et al. Added value of intraoperative data for predicting postoperative complications: the MySurgeryRisk PostOp Extension. J Surg Res 2020; 254: 350-363
    CrossrefPubMedGoogle Scholar
  • 134 Du X, Min J, Shah CP, Bishnoi R, Hogan WR, Lemas DJ. Predicting in-hospital mortality of patients with febrile neutropenia using machine learning models. Int J Med Inform 2020; 139: 104140
    CrossrefPubMedGoogle Scholar
  • 135 Faisal M, Scally A, Howes R, Beatson K, Richardson D, Mohammed MA. A comparison of logistic regression models with alternative machine learning methods to predict the risk of in-hospital mortality in emergency medical admissions via external validation. Health Informatics J 2020; 26 (01) 34-44
    CrossrefPubMedGoogle Scholar
  • 136 Gao N, Qi X, Dang Y. et al. Establishment and validation of a risk model for prediction of in-hospital mortality in patients with acute ST-elevation myocardial infarction after primary PCI. BMC Cardiovasc Disord 2020; 20 (01) 513
    CrossrefPubMedGoogle Scholar
  • 137 Haug N, Deischinger C, Gyimesi M, Kautzky-Willer A, Thurner S, Klimek P. High-risk multimorbidity patterns on the road to cardiovascular mortality. BMC Med 2020; 18 (01) 44
    CrossrefPubMedGoogle Scholar
  • 138 Iriondo M, Thio M, Del Río R, Baucells BJ, Bosio M, Figueras-Aloy J. Prediction of mortality in very low birth weight neonates in Spain. PLoS One 2020; 15 (07) e0235794
    CrossrefPubMedGoogle Scholar
  • 139 Kang MW, Kim J, Kim DK. et al. Machine learning algorithm to predict mortality in patients undergoing continuous renal replacement therapy. Crit Care 2020; 24 (01) 42
    CrossrefPubMedGoogle Scholar
  • 140 Knight SR, Ho A, Pius R. et al; ISARIC4C investigators. Risk stratification of patients admitted to hospital with covid-19 using the ISARIC WHO Clinical Characterisation Protocol: development and validation of the 4C Mortality Score. BMJ 2020; 370: m3339
    CrossrefPubMedGoogle Scholar
  • 141 Kerckhoffs MC, Brinkman S, de Keizer N. et al. The performance of acute versus antecedent patient characteristics for 1-year mortality prediction during intensive care unit admission: a national cohort study. Crit Care 2020; 24 (01) 330
    CrossrefPubMedGoogle Scholar
  • 142 Ko H, Chung H, Kang WS. et al. An artificial intelligence model to predict the mortality of COVID-19 patients at hospital admission time using routine blood samples: development and validation of an ensemble model. J Med Internet Res 2020; 22 (12) e25442
    CrossrefPubMedGoogle Scholar
  • 143 Laosa O, Pedraza L, Álvarez-Bustos A, Carnicero JA, Rodriguez-Artalejo F, Rodriguez-Mañas L. Rapid assessment at hospital admission of mortality risk from COVID-19: the role of functional status. J Am Med Dir Assoc 2020; 21 (12) 1798-1802
    CrossrefPubMedGoogle Scholar
  • 144 Kong G, Lin K, Hu Y. Using machine learning methods to predict in-hospital mortality of sepsis patients in the ICU. BMC Med Inform Decis Mak 2020; 20 (01) 251
    CrossrefPubMedGoogle Scholar
  • 145 Lei Lei E, Ladha K, Mueller B. et al. Noncardiac determinants of death and intensive care morbidity in adult congenital heart disease surgery. J Thorac Cardiovasc Surg 2020; 159 (06) 2407-2415
    CrossrefPubMedGoogle Scholar
  • 146 Li J, Chen Y, Chen S. et al. Derivation and validation of a prognostic model for predicting in-hospital mortality in patients admitted with COVID-19 in Wuhan, China: the PLANS (platelet lymphocyte age neutrophil sex) model. BMC Infect Dis 2020; 20 (01) 959
    CrossrefPubMedGoogle Scholar
  • 147 Li C, Ren Q, Wang Z, Wang G. Early prediction of in-hospital mortality in acute pancreatitis: a retrospective observational cohort study based on a large multicentre critical care database. BMJ Open 2020; 10 (12) e041893
    CrossrefPubMedGoogle Scholar
  • 148 Lindenmeyer CC, Kim A, Sanghi V. et al. The EMALT score: an improved model for prediction of early mortality in liver transplant recipients. J Intensive Care Med 2020; 35 (08) 781-788
    CrossrefPubMedGoogle Scholar
  • 149 Matsumoto K, Nohara Y, Soejima H, Yonehara T, Nakashima N, Kamouchi M. Stroke prognostic scores and data-driven prediction of clinical outcomes after acute ischemic stroke. Stroke 2020; 51 (05) 1477-1483
    CrossrefPubMedGoogle Scholar
  • 150 Medvedev MM, Brotherton H, Gai A. et al. Development and validation of a simplified score to predict neonatal mortality risk among neonates weighing 2000 g or less (NMR-2000): an analysis using data from the UK and The Gambia. Lancet Child Adolesc Health 2020; 4 (04) 299-311
    CrossrefPubMedGoogle Scholar
  • 151 Menezes RC, Ferreira IBB, Carmo TA. et al. Are prognostic tools losing accuracy? Development and performance of a novel age-calibrated severity scoring system for critically ill patients. PLoS One 2020; 15 (11) e0240793
    CrossrefPubMedGoogle Scholar
  • 152 Mohamadlou H, Panchavati S, Calvert J. et al. Multicenter validation of a machine-learning algorithm for 48-h all-cause mortality prediction. Health Informatics J 2020; 26 (03) 1912-1925
    CrossrefPubMedGoogle Scholar
  • 153 Mohapatra A, Liang NL, Makaroun MS, Schermerhorn ML, Farber A, Eslami MH. Risk factors for mortality after endovascular repair for blunt thoracic aortic injury. J Vasc Surg 2020; 71 (03) 768-773
    CrossrefPubMedGoogle Scholar
  • 154 Muttalib F, Clavel V, Yaeger LH, Shah V, Adhikari NKJ. Performance of pediatric mortality prediction models in low- and middle-income countries: a systematic review and meta-analysis. J Pediatr 2020; 225: 182-192.e2
    CrossrefPubMedGoogle Scholar
  • 155 Morris RS, Milia D, Glover J. et al. Predictors of elderly mortality after trauma: A novel outcome score. J Trauma Acute Care Surg 2020; 88 (03) 416-424
    CrossrefPubMedGoogle Scholar
  • 156 Rac H, Gould AP, Bookstaver PB, Justo JA, Kohn J, Al-Hasan MN. Evaluation of early clinical failure criteria for gram-negative bloodstream infections. Clin Microbiol Infect 2020; 26 (01) 73-77
    CrossrefPubMedGoogle Scholar
  • 157 Siddiqi TJ, Usman MS, Khan MS. et al. Systematic review and meta-analysis of current risk models in predicting short-term mortality after transcatheter aortic valve replacement. EuroIntervention 2020; 15 (17) 1497-1505
    CrossrefPubMedGoogle Scholar
  • 158 Shin JH, Kunisawa S, Imanaka Y. New outcome-specific comorbidity scores excelled in predicting in-hospital mortality and healthcare charges in administrative databases. J Clin Epidemiol 2020; 126: 141-153
    CrossrefPubMedGoogle Scholar
  • 159 Sinha I, Aluthge DP, Chen ES, Sarkar IN, Ahn SH. Machine learning offers exciting potential for predicting postprocedural outcomes: a framework for developing random forest models in IR. J Vasc Interv Radiol 2020; 31 (06) 1018-1024.e4
    CrossrefPubMedGoogle Scholar
  • 160 Soong JTY, Rolph G, Poots AJ, Bell D. Validating a methodology to measure frailty syndromes at hospital level utilising administrative data. Clin Med (Lond) 2020; 20 (02) 183-188
    CrossrefPubMedGoogle Scholar
  • 161 St-Louis E, Hassamal R, Razek T, Baird R, Poenaru D, Hardcastle TC. Validation of the PRESTO score in injured children in a South-African quaternary trauma center. J Pediatr Surg 2020; 55 (07) 1245-1248
    CrossrefPubMedGoogle Scholar
  • 162 Stolz E, Rásky É, Jagsch C. Frailty index predicts geriatric psychiatry inpatient mortality: a case-control study. Psychogeriatrics 2020; 20 (04) 469-472
    CrossrefPubMedGoogle Scholar
  • 163 Sukmark T, Lumlertgul N, Praditpornsilpa K, Tungsanga K, Eiam-Ong S, Srisawat N. THAI-ICU score as a simplified severity score for critically ill patients in a resource limited setting: Result from SEA-AKI study group. J Crit Care 2020; 55: 56-63
    CrossrefPubMedGoogle Scholar
  • 164 Takada T, Hoogland J, Yano T. et al. Added value of inflammatory markers to vital signs to predict mortality in patients suspected of severe infection. Am J Emerg Med 2020; 38 (07) 1389-1395
    CrossrefPubMedGoogle Scholar
  • 165 Thorsen-Meyer HC, Nielsen AB, Nielsen AP. et al. Dynamic and explainable machine learning prediction of mortality in patients in the intensive care unit: a retrospective study of high-frequency data in electronic patient records. Lancet Digit Health 2020; 2 (04) e179-e191
    CrossrefPubMedGoogle Scholar
  • 166 Tuxun M, Zhao Q, Xiang Y. et al. Predicting value of white cell count and total bilirubin on clinical outcomes in patients with ST-elevation myocardial infarction following percutaneous coronary intervention: a cohort study. BMJ Open 2020; 10 (02) e031227
    CrossrefPubMedGoogle Scholar
  • 167 Tsai KY, Hsieh KY, Ou SY, Chou FH, Chou YM. Comparison of elixhauser and charlson methods for discriminative performance in mortality risk in patients with schizophrenic disorders. Int J Environ Res Public Health 2020; 17 (07) E2450
    CrossrefPubMedGoogle Scholar
  • 168 Vaid A, Somani S, Russak AJ. et al. Machine learning to predict mortality and critical events in a cohort of patients with COVID-19 in New York City: model development and validation. J Med Internet Res 2020; 22 (11) e24018
    CrossrefPubMedGoogle Scholar
  • 169 Wang K, Zuo P, Liu Y. et al. Clinical and laboratory predictors of in-hospital mortality in patients with coronavirus disease-2019: a cohort study in Wuhan, China. Clin Infect Dis 2020; 71 (16) 2079-2088
    CrossrefPubMedGoogle Scholar
  • 170 Wong DJN, Harris S, Sahni A. et al; SNAP-2: EPICCS collaborators. Developing and validating subjective and objective risk-assessment measures for predicting mortality after major surgery: an international prospective cohort study. PLoS Med 2020; 17 (10) e1003253
    CrossrefPubMedGoogle Scholar
  • 171 Yu R, Zheng Y, Zhang R, Jiang Y, Poon CCY. Using a multi-task recurrent neural network with attention mechanisms to predict hospital mortality of patients. IEEE J Biomed Health Inform 2020; 24 (02) 486-492
    CrossrefPubMedGoogle Scholar
  • 172 Zelis N, Buijs J, de Leeuw PW, van Kuijk SMJ, Stassen PM. A new simplified model for predicting 30-day mortality in older medical emergency department patients: The rise up score. Eur J Intern Med 2020; 77: 36-43
    CrossrefPubMedGoogle Scholar
  • 173 Zhang D, Yin C, Zeng J, Yuan X, Zhang P. Combining structured and unstructured data for predictive models: a deep learning approach. BMC Med Inform Decis Mak 2020; 20 (01) 280
    CrossrefPubMedGoogle Scholar
  • 174 Diktas H, Uysal S, Erdem H. et al. A novel id-iri score: development and internal validation of the multivariable community acquired sepsis clinical risk prediction model. Eur J Clin Microbiol Infect Dis 2020; 39 (04) 689-701
    CrossrefPubMedGoogle Scholar
  • 175 Rahmatinejad Z, Tohidinezhad F, Reihani H. et al. Prognostic utilization of models based on the APACHE II, APACHE IV, and SAPS II scores for predicting in-hospital mortality in emergency department. Am J Emerg Med 2020; 38 (09) 1841-1846
    CrossrefPubMedGoogle Scholar
  • 176 Castiñeira D, Schlosser KR, Geva A. et al. Adding continuous vital sign information to static clinical data improves the prediction of length of stay after intubation: a data-driven machine learning approach. Respir Care 2020; 65 (09) 1367-1377
    CrossrefPubMedGoogle Scholar
  • 177 Biron DR, Sinha I, Kleiner JE. et al. A novel machine learning model developed to assist in patient selection for outpatient total shoulder arthroplasty. J Am Acad Orthop Surg 2020; 28 (13) e580-e585
    CrossrefPubMedGoogle Scholar
  • 178 Sheikhalishahi S, Balaraman V, Osmani V. Benchmarking machine learning models on multi-centre eICU critical care dataset. PLoS One 2020; 15 (07) e0235424
    CrossrefPubMedGoogle Scholar
  • 179 Schulz EB, Phillips F, Waterbright S. Case-mix adjusted postanaesthesia care unit length of stay and business intelligence dashboards for feedback to anaesthetists. Br J Anaesth 2020; 125 (06) 1079-1087
    CrossrefPubMedGoogle Scholar
  • 180 Zhang X, Qiu H, Liu S, Li J, Zhou M. Prediction of prolonged length of stay for stroke patients on admission for inpatient rehabilitation based on the International Classification of Functioning, Disability, and Health (ICF) generic set: a study from 50 centers in China. Med Sci Monit 2020; 26: e918811
    PubMedGoogle Scholar
  • 181 Jalali A, Lonsdale H, Do N. et al. Deep learning for improved risk prediction in surgical outcomes. Sci Rep 2020; 10 (01) 9289
    CrossrefPubMedGoogle Scholar
  • 182 Anis HK, Strnad GJ, Klika AK. et al; Cleveland Clinic OME Arthroplasty Group. Developing a personalized outcome prediction tool for knee arthroplasty. Bone Joint J 2020; 102-B (09) 1183-1193
    CrossrefPubMedGoogle Scholar
  • 183 Goldstein BA, Cerullo M, Krishnamoorthy V. et al. Development and performance of a clinical decision support tool to inform resource utilization for elective operations. JAMA Netw Open 2020; 3 (11) e2023547
    CrossrefPubMedGoogle Scholar
  • 184 Chen CH, Hsieh JG, Cheng SL, Lin YL, Lin PH, Jeng JH. Early short-term prediction of emergency department length of stay using natural language processing for low-acuity outpatients. Am J Emerg Med 2020; 38 (11) 2368-2373
    CrossrefPubMedGoogle Scholar
  • 185 Kirchebner J, Günther MP, Sonnweber M, King A, Lau S. Factors and predictors of length of stay in offenders diagnosed with schizophrenia - a machine-learning-based approach. BMC Psychiatry 2020; 20 (01) 201
    CrossrefPubMedGoogle Scholar
  • 186 Gholi Zadeh Kharrat F, Shydeo Brandão Miyoshi N, Cobre J, Mazzoncini De Azevedo-Marques J, Mazzoncini de Azevedo-Marques P, Cláudio Botazzo Delbem A. Feature sensitivity criterion-based sampling strategy from the optimization based on phylogram analysis (Fs-OPA) and Cox regression applied to mental disorder datasets. PLoS One 2020; 15 (07) e0235147
    CrossrefPubMedGoogle Scholar
  • 187 Ma X, Si Y, Wang Z, Wang Y. Length of stay prediction for ICU patients using individualized single classification algorithm. Comput Methods Programs Biomed 2020; 186: 105224
    CrossrefPubMedGoogle Scholar
  • 188 Curiati PK, Gil-Junior LA, Morinaga CV, Ganem F, Curiati JAE, Avelino-Silva TJ. Predicting hospital admission and prolonged length of stay in older adults in the emergency department: the PRO-AGE scoring system. Ann Emerg Med 2020; 76 (03) 255-265
    CrossrefPubMedGoogle Scholar
  • 189 Alsinglawi B, Alnajjar F, Mubin O. et al. Predicting length of stay for cardiovascular hospitalizations in the intensive care unit: machine learning approach. Annu Int Conf IEEE Eng Med Biol Soc 2020; 2020: 5442-5445
    PubMedGoogle Scholar
  • 190 Wilk M, Marsh DWR, De Freitas S, Prowle J. Predicting length of stay in hospital using electronic records available at the time of admission. Stud Health Technol Inform 2020; 270: 377-381
    PubMedGoogle Scholar
  • 191 Doctoroff L, Herzig SJ. Predicting patients at risk for prolonged hospital stays. Med Care 2020; 58 (09) 778-784
    CrossrefPubMedGoogle Scholar
  • 192 Wolff J, Gary A, Jung D. et al. Predicting patient outcomes in psychiatric hospitals with routine data: a machine learning approach. BMC Med Inform Decis Mak 2020; 20 (01) 21
    CrossrefPubMedGoogle Scholar
  • 193 Bacchi S, Gluck S, Tan Y. et al. Prediction of general medical admission length of stay with natural language processing and deep learning: a pilot study. Intern Emerg Med 2020; 15 (06) 989-995
    CrossrefPubMedGoogle Scholar
  • 194 Rahman MA, Honan B, Glanville T, Hough P, Walker K. Using data mining to predict emergency department length of stay greater than 4 hours: Derivation and single-site validation of a decision tree algorithm. Emerg Med Australas 2020; 32 (03) 416-421
    CrossrefPubMedGoogle Scholar
  • 195 Sun LY, Bader Eddeen A, Ruel M, MacPhee E, Mesana TG. Derivation and validation of a clinical model to predict intensive care unit length of stay after cardiac surgery. J Am Heart Assoc 2020; 9 (21) e017847
    CrossrefPubMedGoogle Scholar
  • 196 McIsaac DI, Harris EP, Hladkowicz E. et al. Prospective comparison of preoperative predictive performance between 3 leading frailty instruments. Anesth Analg 2020; 131 (01) 263-272
    CrossrefPubMedGoogle Scholar
  • 197 Barroso J, Wakaizumi K, Reckziegel D. et al. Prognostics for pain in osteoarthritis: Do clinical measures predict pain after total joint replacement?. PLoS One 2020; 15 (01) e0222370
    CrossrefPubMedGoogle Scholar
  • 198 Cheng R, Alexandridi NA, Smith RM. et al. Fully automated patellofemoral MRI segmentation using holistically nested networks: Implications for evaluating patellofemoral osteoarthritis, pain, injury, pathology, and adolescent development. Magn Reson Med 2020; 83 (01) 139-153
    CrossrefPubMedGoogle Scholar
  • 199 de Heer EW, Palacios JE, Adèr HJ, van Marwijk HWJ, Tylee A, van der Feltz-Cornelis CM. Chest pain, depression and anxiety in coronary heart disease: Consequence or cause? A prospective clinical study in primary care. J Psychosom Res 2020; 129: 109891
    CrossrefPubMedGoogle Scholar
  • 200 Fritz RL, Wilson M, Dermody G, Schmitter-Edgecombe M, Cook DJ. Automated smart home assessment to support pain management: multiple methods analysis. J Med Internet Res 2020; 22 (11) e23943
    CrossrefPubMedGoogle Scholar
  • 201 Hott A, Brox JI, Pripp AH, Juel NG, Liavaag S. Predictors of pain, function, and change in patellofemoral pain. Am J Sports Med 2020; 48 (02) 351-358
    CrossrefPubMedGoogle Scholar
  • 202 Juwara L, Arora N, Gornitsky M, Saha-Chaudhuri P, Velly AM. Identifying predictive factors for neuropathic pain after breast cancer surgery using machine learning. Int J Med Inform 2020; 141: 104170
    CrossrefPubMedGoogle Scholar
  • 203 Kwon SB, Ku Y, Han HU, Lee MC, Kim HC, Ro DH. A machine learning-based diagnostic model associated with knee osteoarthritis severity. Sci Rep 2020; 10 (01) 15743
    CrossrefPubMedGoogle Scholar
  • 204 Mieronkoski R, Syrjälä E, Jiang M. et al. Developing a pain intensity prediction model using facial expression: a feasibility study with electromyography. PLoS One 2020; 15 (07) e0235545
    CrossrefPubMedGoogle Scholar
  • 205 Parga JJ, Lewin S, Lewis J. et al. Defining and distinguishing infant behavioral states using acoustic cry analysis: is colic painful?. Pediatr Res 2020; 87 (03) 576-580
    CrossrefPubMedGoogle Scholar
  • 206 Pérez-Benito FJ, Conejero JA, Sáez C. et al. Subgrouping factors influencing migraine intensity in women: a semi-automatic methodology based on machine learning and information geometry. Pain Pract 2020; 20 (03) 297-309
    CrossrefPubMedGoogle Scholar
  • 207 Russo MA, Georgius P, Pires AS. et al. Novel immune biomarkers in complex regional pain syndrome. J Neuroimmunol 2020; 347: 577330
    CrossrefPubMedGoogle Scholar
  • 208 Sörös P, Bantel C. Chronic noncancer pain is not associated with accelerated brain aging as assessed by structural magnetic resonance imaging in patients treated in specialized outpatient clinics. Pain 2020; 161 (03) 641-650
    CrossrefPubMedGoogle Scholar
  • 209 Tanaka R, Hirohama K, Kurashige Y. et al. Prediction models considering psychological factors to identify pain relief in conservative treatment of people with knee osteoarthritis: a multicenter, prospective cohort study. J Orthop Sci 2020; 25 (04) 618-626
    CrossrefPubMedGoogle Scholar
  • 210 Thiam P, Kestler HA, Schwenker F. Two-stream attention network for pain recognition from video sequences. Sensors (Basel) 2020; 20 (03) E839
    CrossrefPubMedGoogle Scholar
  • 211 Deffland M, Spies C, Weiss B. et al. Effects of pain, sedation and delirium monitoring on clinical and economic outcome: a retrospective study. PLoS One 2020; 15 (09) e0234801
    CrossrefPubMedGoogle Scholar
  • 212 Mutubuki EN, Luitjens MA, Maas ET. et al. Predictive factors of high societal costs among chronic low back pain patients. Eur J Pain 2020; 24 (02) 325-337
    CrossrefPubMedGoogle Scholar
  • 213 Wang J, Wei M, Zhang L. et al. An autoencoder-based approach to predict subjective pain perception from high-density evoked EEG potentials. Annu Int Conf IEEE Eng Med Biol Soc 2020; 2020: 1507-1511
    PubMedGoogle Scholar
  • 214 Wang R, Xu K, Feng H, Chen W. Hybrid RNN-ANN based deep physiological network for pain recognition. Annu Int Conf IEEE Eng Med Biol Soc 2020; 2020: 5584-5587
    PubMedGoogle Scholar
  • 215 Xin X, Lin X, Yang S, Zheng X. Pain intensity estimation based on a spatial transformation and attention CNN. PLoS One 2020; 15 (08) e0232412
    CrossrefPubMedGoogle Scholar
  • 216 Kong Y, Posada-Quintero HF, Chon KH. Pain detection using a smartphone in real time. Annu Int Conf IEEE Eng Med Biol Soc 2020; 2020: 4526-4529
    PubMedGoogle Scholar
  • 217 Brown TT, Lee W. The FUTUREPAIN study: validating a questionnaire to predict the probability of having chronic pain 7-10 years into the future. PLoS One 2020; 15 (08) e0237508
    CrossrefPubMedGoogle Scholar
  • 218 Dewitt B, Jalal H, Hanmer J. Computing PROPr utility scores for PROMIS profile instruments. Value Health 2020; 23 (03) 370-378
    CrossrefPubMedGoogle Scholar
  • 219 Diaz Maffini M, Aguirre Ojea F, Manzotti M. Automatic detection of vital signs in clinical notes of the outpatient settings. Stud Health Technol Inform 2020; 270: 1211-1212
    PubMedGoogle Scholar
  • 220 Liew BXW, Rugamer D, Stocker A, De Nunzio AM. Classifying neck pain status using scalar and functional biomechanical variables - development of a method using functional data boosting. Gait Posture 2020; 76: 146-150
    CrossrefPubMedGoogle Scholar
  • 221 Petersen T, Juhl CB, Fournier GL. Patients with persistent low back pain and nerve root involvement: to operate, or not to operate, that is the question. Spine 2020; 45 (07) 483-490
    CrossrefPubMedGoogle Scholar
  • 222 Tu Y, Zeng F, Lan L. et al. An fMRI-based neural marker for migraine without aura. Neurology 2020; 94 (07) e741-e751
    CrossrefPubMedGoogle Scholar
  • 223 Alambo A, Andrew R, Gollarahalli S. et al. Measuring pain in sickle cell disease using clinical text. Annu Int Conf IEEE Eng Med Biol Soc 2020; 2020: 5838-5841
    PubMedGoogle Scholar
  • 224 Liang C, Zhou S, Yao B, Hood D, Gong Y. Toward systems-centered analysis of patient safety events: Improving root cause analysis by optimized incident classification and information presentation. Int J Med Inform 2020; 135: 104054
    CrossrefPubMedGoogle Scholar
  • 225 Liu N, Chen CB, Kumara S. Semi-supervised learning algorithm for identifying high-priority drug-drug interactions through adverse event reports. IEEE J Biomed Health Inform 2020; 24 (01) 57-68
    CrossrefPubMedGoogle Scholar
  • 226 Shiner B, Neily J, Mills PD, Watts BV. Identification of inpatient falls using automated review of text-based medical records. J Patient Saf 2020; 16 (03) e174-e178
    CrossrefPubMedGoogle Scholar
  • 227 Wong ZS-Y, So HY, Kwok BSC, Lai MWS, Sun DTF. Medication-rights detection using incident reports: a natural language processing and deep neural network approach. Health Informatics J 2020; 26 (03) 1777-1794
    CrossrefPubMedGoogle Scholar
  • 228 Evans HP, Anastasiou A, Edwards A. et al. Automated classification of primary care patient safety incident report content and severity using supervised machine learning (ML) approaches. Health Informatics J 2020; 26 (04) 3123-3139
    CrossrefPubMedGoogle Scholar
  • 229 Yang J, Wang L, Phadke NA. et al. Development and validation of a deep learning model for detection of allergic reactions using safety event reports across hospitals. JAMA Netw Open 2020; 3 (11) e2022836
    CrossrefPubMedGoogle Scholar
  • 230 Baernholdt M, Yan G, Hinton ID, Cramer E, Dunton N. Effect of preventive care interventions on pressure ulcer rates in a national sample of rural and urban nursing units: Longitudinal associations over 4 years. Int J Nurs Stud 2020; 105: 103455
    CrossrefPubMedGoogle Scholar
  • 231 Brindle T. Incidence and variables predictive of pressure injuries in patients undergoing ventricular assist device and total artificial heart surgeries: an 8-year retrospective cohort study. Adv Skin Wound Care 2020; 33 (12) 651-660
    CrossrefPubMedGoogle Scholar
  • 232 Cox J, Schallom M, Jung C. Identifying risk factors for pressure injury in adult critical care patients. Am J Crit Care 2020; 29 (03) 204-213
    CrossrefPubMedGoogle Scholar
  • 233 Hu YH, Lee YL, Kang MF, Lee PJ. Constructing inpatient pressure injury prediction models using machine learning techniques. Comput Inform Nurs 2020; 38 (08) 415-423
    CrossrefPubMedGoogle Scholar
  • 234 Kenneweg KA, Welch MC, Welch PJ. A 9-year retrospective evaluation of 102 pressure ulcer reconstructions. J Wound Care 2020; 29 (09) 48-56
    CrossrefPubMedGoogle Scholar
  • 235 Seibert J, Barch D, Bernacet A. et al. Examining social risk factors in a pressure ulcer quality measure for three post-acute care settings. Adv Skin Wound Care 2020; 33 (03) 156-163
    CrossrefPubMedGoogle Scholar
  • 236 Sprigle S, McNair D, Sonenblum S. Pressure ulcer risk factors in persons with mobility-related disabilities. Adv Skin Wound Care 2020; 33 (03) 146-154
    CrossrefPubMedGoogle Scholar
  • 237 Artico M, Piredda M, D'Angelo D. et al. Prevalence, incidence and associated factors of pressure injuries in hospices: a multicentre prospective longitudinal study. Int J Nurs Stud 2020; 111: 103760
    CrossrefPubMedGoogle Scholar
  • 238 Choi BK, Kim MS, Kim SH. Risk prediction models for the development of oral-mucosal pressure injuries in intubated patients in intensive care units: A prospective observational study. J Tissue Viability 2020; 29 (04) 252-257
    CrossrefPubMedGoogle Scholar
  • 239 Osis SL, Diccini S. Incidence and risk factors associated with pressure injury in patients with traumatic brain injury. Int J Nurs Pract 2020; 26 (03) e12821
    CrossrefPubMedGoogle Scholar
  • 240 Matar G, Lina JM, Kaddoum G. Artificial neural network for in-bed posture classification using bed-sheet pressure sensors. IEEE J Biomed Health Inform 2020; 24 (01) 101-110
    CrossrefPubMedGoogle Scholar
  • 241 Santosa A, Puspitasari N, Isnaini NUR. A path analysis study of factors influencing decubitus in a geriatric nursing home: a preliminary study. Family Med Primary Care Rev 2020; 22 (01) 67-70
    CrossrefPubMedGoogle Scholar
  • 242 Macron A, Pillet H, Doridam J. et al. Is a simplified Finite Element model of the gluteus region able to capture the mechanical response of the internal soft tissues under compression?. Clin Biomech (Bristol, Avon) 2020; 71: 92-100
    CrossrefPubMedGoogle Scholar
  • 243 Brom H, Brooks Carthon JM, Ikeaba U, Chittams J. Leveraging electronic health records and machine learning to tailor nursing care for patients at high risk for readmissions. J Nurs Care Qual 2020; 35 (01) 27-33
    CrossrefPubMedGoogle Scholar
  • 244 Jeong IC, Healy R, Bao B. et al. Assessment of patient ambulation profiles to predict hospital readmission, discharge location, and length of stay in a cardiac surgery progressive care unit. JAMA Netw Open 2020; 3 (03) e201074
    CrossrefPubMedGoogle Scholar
  • 245 Li CY, Karmarkar A, Kuo YF. et al. A comparison of three methods in categorizing functional status to predict hospital readmission across post-acute care. PLoS One 2020; 15 (05) e0232017
    CrossrefPubMedGoogle Scholar
  • 246 Shah N, Konchak C, Chertok D. et al. Clinical Analytics Prediction Engine (CAPE): development, electronic health record integration and prospective validation of hospital mortality, 180-day mortality and 30-day readmission risk prediction models. PLoS One 2020; 15 (08) e0238065
    CrossrefPubMedGoogle Scholar
  • 247 Zhang Y, Zhang Y, Sholle E. et al. Assessing the impact of social determinants of health on predictive models for potentially avoidable 30-day readmission or death. PLoS One 2020; 15 (06) e0235064
    CrossrefPubMedGoogle Scholar
  • 248 Saleh SN, Makam AN, Halm EA, Nguyen OK. Can we predict early 7-day readmissions using a standard 30-day hospital readmission risk prediction model?. BMC Med Inform Decis Mak 2020; 20 (01) 227
    CrossrefPubMedGoogle Scholar
  • 249 Hyer JM, White S, Cloyd J. et al. Can we improve prediction of adverse surgical outcomes? development of a surgical complexity score using a novel machine learning technique. J Am Coll Surg 2020; 230 (01) 43-52.e1
    CrossrefPubMedGoogle Scholar
  • 250 Barbieri S, Kemp J, Perez-Concha O. et al. Benchmarking deep learning architectures for predicting readmission to the ICU and describing patients-at-risk. Sci Rep 2020; 10 (01) 1111
    CrossrefPubMedGoogle Scholar
  • 251 Sanson G, Marino C, Valenti A, Lucangelo U, Berlot G. Is my patient ready for a safe transfer to a lower-intensity care setting? Nursing complexity as an independent predictor of adverse events risk after ICU discharge. Heart Lung 2020; 49 (04) 407-414
    CrossrefPubMedGoogle Scholar
  • 252 Sun CLF, Zuccarelli E, Zerhouni EGA. et al. Predicting coronavirus disease 2019 infection risk and related risk drivers in nursing homes: a machine learning approach. J Am Med Dir Assoc 2020; 21 (11) 1533-1538.e6
    CrossrefPubMedGoogle Scholar
  • 253 Rathlev NK, Visintainer P, Schmidt J, Hettler J, Albert V, Li H. Patient characteristics and clinical process predictors of patients leaving without being seen from the emergency department. West J Emerg Med 2020; 21 (05) 1218-1226
    CrossrefPubMedGoogle Scholar
  • 254 Bradshaw S, Buenning B, Powell A, Teasley S, Olney A, Lee B. Retrospective chart review: readmission prediction ability of the high acuity readmission risk pediatric screen (HARRPS) tool. J Pediatr Nurs 2020; 51: 49-56
    CrossrefPubMedGoogle Scholar
  • 255 Burlison JD, Quillivan RR, Kath LM. et al. A multilevel analysis of U.S. hospital patient safety culture relationships with perceptions of voluntary event reporting. J Patient Saf 2020; 16 (03) 187-193
    CrossrefPubMedGoogle Scholar
  • 256 Haddad LM, Annamaraju P, Toney-Butler TJ. Nursing Shortage. In: StatPearls. Treasure Island, FL: StatPearls; 2021
    Google Scholar
  • 257 Divo MJ, Martinez CH, Mannino DM. Ageing and the epidemiology of multimorbidity. Eur Respir J 2014; 44 (04) 1055-1068
    CrossrefPubMedGoogle Scholar
  • 258 Grange ES, Neil EJ, Stoffel M. et al. Responding to COVID-19: the UW medicine information technology services experience. Appl Clin Inform 2020; 11 (02) 265-275
    Article in Thieme ConnectPubMedGoogle Scholar