Download PDF
Thromb Haemost 2024; 124(11): 1040-1052
DOI: 10.1055/a-2299-4758
Stroke, Systemic or Venous Thromboembolism

Machine Learning-Based Predictive Models for Patients with Venous Thromboembolism: A Systematic Review

Vasiliki Danilatou 
1   School of Medicine, European University of Cyprus, Nicosia, Cyprus
2   Healthcare Division, Sphynx Technology Solutions, Nicosia, Cyprus
,
Dimitrios Dimopoulos 
3   School of Engineering, Department of Information and Communication Systems Engineering, University of the Aegean, North Aegean, Greece
,
Theodoros Kostoulas
3   School of Engineering, Department of Information and Communication Systems Engineering, University of the Aegean, North Aegean, Greece
,
James Douketis
4   Department of Medicine, McMaster University, Hamilton, Canada
5   Department of Medicine, St. Joseph's Healthcare Hamilton, Ontario, Canada
› Author Affiliations Funding This work was partially funded by RETENTION project, a European Union's Horizon 2020 research and innovation program under Grant Agreement No 965343.
› Further Information
Also available at
    Permissions and Reprints


Abstract

Background Venous thromboembolism (VTE) is a chronic disorder with a significant health and economic burden. Several VTE-specific clinical prediction models (CPMs) have been used to assist physicians in decision-making but have several limitations. This systematic review explores if machine learning (ML) can enhance CPMs by analyzing extensive patient data derived from electronic health records. We aimed to explore ML-CPMs' applications in VTE for risk stratification, outcome prediction, diagnosis, and treatment.

Methods Three databases were searched: PubMed, Google Scholar, and IEEE electronic library. Inclusion criteria focused on studies using structured data, excluding non-English publications, studies on non-humans, and certain data types such as natural language processing and image processing. Studies involving pregnant women, cancer patients, and children were also excluded. After excluding irrelevant studies, a total of 77 studies were included.

Results Most studies report that ML-CPMs outperformed traditional CPMs in terms of receiver operating area under the curve in the four clinical domains that were explored. However, the majority of the studies were retrospective, monocentric, and lacked detailed model architecture description and external validation, which are essential for quality audit. This review identified research gaps and highlighted challenges related to standardized reporting, reproducibility, and model comparison.

Conclusion ML-CPMs show promise in improving risk assessment and individualized treatment recommendations in VTE. Apparently, there is an urgent need for standardized reporting and methodology for ML models, external validation, prospective and real-world data studies, as well as interventional studies to evaluate the impact of artificial intelligence in VTE.

Keywords

machine learning - artificial intelligence - venous thromboembolism - electronic health record - clinical prediction models

Supplementary Material



Publication History

Received: 21 August 2023

Accepted: 03 April 2024

Accepted Manuscript online:
04 April 2024

Article published online:
13 May 2024

© 2024. Thieme. All rights reserved.

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 

  • References

  • 1 Raskob GE, Angchaisuksiri P, Blanco AN. et al; ISTH Steering Committee for World Thrombosis Day. Thrombosis: a major contributor to global disease burden. Arterioscler Thromb Vasc Biol 2014; 34 (11) 2363-2371
    CrossrefPubMedGoogle Scholar
  • 2 Barco S, Woersching AL, Spyropoulos AC, Piovella F, Mahan CE. European Union-28: an annualised cost-of-illness model for venous thromboembolism. Thromb Haemost 2016; 115 (04) 800-808
    Article in Thieme ConnectPubMedGoogle Scholar
  • 3 Grosse SD, Nelson RE, Nyarko KA, Richardson LC, Raskob GE. The economic burden of incident venous thromboembolism in the United States: a review of estimated attributable healthcare costs. Thromb Res 2016; 137: 3-10
    CrossrefPubMedGoogle Scholar
  • 4 Goldhaber SZ, Bounameaux H. Pulmonary embolism and deep vein thrombosis. Lancet 2012; 379 (9828) 1835-1846
    CrossrefPubMedGoogle Scholar
  • 5 Kürkciyan I, Meron G, Sterz F. et al. Pulmonary embolism as a cause of cardiac arrest: presentation and outcome. Arch Intern Med 2000; 160 (10) 1529-1535
    CrossrefPubMedGoogle Scholar
  • 6 Wells PS, Ginsberg JS, Anderson DR. et al. Use of a clinical model for safe management of patients with suspected pulmonary embolism. Ann Intern Med 1998; 129 (12) 997-1005
    CrossrefPubMedGoogle Scholar
  • 7 Wicki J, Perneger TV, Junod AF, Bounameaux H, Perrier A. Assessing clinical probability of pulmonary embolism in the emergency ward: a simple score. Arch Intern Med 2001; 161 (01) 92-97
    CrossrefPubMedGoogle Scholar
  • 8 Spyropoulos AC, Anderson Jr FA, FitzGerald G. et al; IMPROVE Investigators. Predictive and associative models to identify hospitalized medical patients at risk for VTE. Chest 2011; 140 (03) 706-714
    CrossrefPubMedGoogle Scholar
  • 9 Decousus H, Tapson VF, Bergmann JF. et al; IMPROVE Investigators. Factors at admission associated with bleeding risk in medical patients: findings from the IMPROVE investigators. Chest 2011; 139 (01) 69-79
    CrossrefPubMedGoogle Scholar
  • 10 Tosetto A, Iorio A, Marcucci M. et al. Predicting disease recurrence in patients with previous unprovoked venous thromboembolism: a proposed prediction score (DASH). J Thromb Haemost 2012; 10 (06) 1019-1025
    CrossrefPubMedGoogle Scholar
  • 11 Poli D, Palareti G. Assessing recurrence risk following acute venous thromboembolism: use of algorithms. Curr Opin Pulm Med 2013; 19 (05) 407-412
    CrossrefPubMedGoogle Scholar
  • 12 Cowley LE, Farewell DM, Maguire S, Kemp AM. Methodological standards for the development and evaluation of clinical prediction rules: a review of the literature. Diagn Progn Res 2019; 3: 16
    CrossrefPubMedGoogle Scholar
  • 13 Ranganathan P, Pramesh CS, Aggarwal R. Common pitfalls in statistical analysis: logistic regression. Perspect Clin Res 2017; 8 (03) 148-151
    CrossrefPubMedGoogle Scholar
  • 14 Chen LM, Martin CM, Morrison TL, Sibbald WJ. Interobserver variability in data collection of the APACHE II score in teaching and community hospitals. Crit Care Med 1999; 27 (09) 1999-2004
    CrossrefPubMedGoogle Scholar
  • 15 Kennedy G, Gallego B. Clinical prediction rules: a systematic review of healthcare provider opinions and preferences. Int J Med Inform 2019; 123: 1-10
    CrossrefPubMedGoogle Scholar
  • 16 Mondal B. Recent trends and advances in artificial intelligence and internet of things. In: Balas V, Kumar R, Srivastava R. eds. Artificial Intelligence: State of the Art. Vol 172. Berlin: Springer; 2020: 389-425
    CrossrefGoogle Scholar
  • 17 Bajgain B, Lorenzetti D, Lee J, Sauro K. Determinants of implementing artificial intelligence-based clinical decision support tools in healthcare: a scoping review protocol. BMJ Open 2023; 13 (02) e068373
    CrossrefPubMedGoogle Scholar
  • 18 Lip GYH, Tran G, Genaidy A, Marroquin P, Estes C, Landsheft J. Improving dynamic stroke risk prediction in non-anticoagulated patients with and without atrial fibrillation: comparing common clinical risk scores and machine learning algorithms. Eur Heart J Qual Care Clin Outcomes 2022; 8 (05) 548-556
    CrossrefPubMedGoogle Scholar
  • 19 Mishra A, Ashraf MZ. Using artificial intelligence to manage thrombosis research, diagnosis, and clinical management. Semin Thromb Hemost 2020; 46 (04) 410-418
    Article in Thieme ConnectPubMedGoogle Scholar
  • 20 Bhavnani SP, Parakh K, Atreja A. et al. 2017 Roadmap for innovation-ACC health policy statement on healthcare transformation in the era of digital health, big data, and precision health: a report of the American College of Cardiology Task Force on Health Policy Statements and Systems of Care. J Am Coll Cardiol 2017; 70 (21) 2696-2718
    CrossrefPubMedGoogle Scholar
  • 21 Frederix I, Caiani EG, Dendale P. et al. ESC e-cardiology working group position paper: overcoming challenges in digital health implementation in cardiovascular medicine. Eur J Prev Cardiol 2019; 26 (11) 1166-1177
    CrossrefPubMedGoogle Scholar
  • 22 Wells PS, Anderson DR, Rodger M. et al. Excluding pulmonary embolism at the bedside without diagnostic imaging: management of patients with suspected pulmonary embolism presenting to the emergency department by using a simple clinical model and d-dimer. Ann Intern Med 2001; 135 (02) 98-107
    CrossrefPubMedGoogle Scholar
  • 23 Le Gal G, Righini M, Roy PM. et al. Prediction of pulmonary embolism in the emergency department: the revised Geneva score. Ann Intern Med 2006; 144 (03) 165-171
    CrossrefPubMedGoogle Scholar
  • 24 Hendriksen JMT, Geersing GJ, Lucassen WAM. et al. Diagnostic prediction models for suspected pulmonary embolism: systematic review and independent external validation in primary care. BMJ 2015; 351: h4438
    CrossrefPubMedGoogle Scholar
  • 25 Trihan JE, Adam M, Jidal S. et al. Performance of the Wells score in predicting deep vein thrombosis in medical and surgical hospitalized patients with or without thromboprophylaxis: the R-WITT study. Vasc Med 2021; 26 (03) 288-296
    CrossrefPubMedGoogle Scholar
  • 26 Caprini JA, Arcelus JI, Hasty JH, Tamhane AC, Fabrega F. Clinical assessment of venous thromboembolic risk in surgical patients. Semin Thromb Hemost 1991; 17 (Suppl. 03) 304-312
    PubMedGoogle Scholar
  • 27 Kawaler E, Cobian A, Peissig P, Cross D, Yale S, Craven M. Learning to predict post-hospitalization VTE risk from EHR data. AMIA Annu Symp Proc 2012; 2012: 436-445
    PubMedGoogle Scholar
  • 28 Fei Y, Hu J, Li WQ, Wang W, Zong GQ. Artificial neural networks predict the incidence of portosplenomesenteric venous thrombosis in patients with acute pancreatitis. J Thromb Haemost 2017; 15 (03) 439-445
    CrossrefPubMedGoogle Scholar
  • 29 Christie SA, Conroy AS, Callcut RA, Hubbard AE, Cohen MJ. Dynamic multi-outcome prediction after injury: applying adaptive machine learning for precision medicine in trauma. PLoS One 2019; 14 (04) e0213836
    CrossrefPubMedGoogle Scholar
  • 30 Nafee T, Gibson CM, Travis R. et al. Machine learning to predict venous thrombosis in acutely ill medical patients. Res Pract Thromb Haemost 2020; 4 (02) 230-237
    CrossrefPubMedGoogle Scholar
  • 31 Alzghoul BN, Reddy R, Chizinga M. et al. Pulmonary embolism in acute asthma exacerbation: clinical characteristics, prediction model and hospital outcomes. Lung 2020; 198 (04) 661-669
    CrossrefPubMedGoogle Scholar
  • 32 Park JI, Kim D, Lee JA, Zheng K, Amin A. Personalized risk prediction for 30-day readmissions with venous thromboembolism using machine learning. J Nurs Scholarsh 2021; 53 (03) 278-287
    CrossrefPubMedGoogle Scholar
  • 33 Hou L, Hu L, Gao W. et al. Construction of a risk prediction model for hospital-acquired pulmonary embolism in hospitalized patients. Clin Appl Thromb Hemost 2021; 27: 10 760296211040868
    CrossrefPubMedGoogle Scholar
  • 34 Liu Y, Song C, Tian Z, Shen W. Ten-year multicenter retrospective study utilizing machine learning algorithms to identify patients at high risk of venous thromboembolism after radical gastrectomy. Int J Gen Med 2023; 16: 1909-1925
    CrossrefPubMedGoogle Scholar
  • 35 Ma H, Sheng W, Li J. et al. A novel hierarchical machine learning model for hospital-acquired venous thromboembolism risk assessment among multiple-departments. J Biomed Inform 2021; 122: 103892
    CrossrefPubMedGoogle Scholar
  • 36 Shen J, Casie Chetty S, Shokouhi S. et al. Massive external validation of a machine learning algorithm to predict pulmonary embolism in hospitalized patients. Thromb Res 2022; 216: 14-21
    CrossrefPubMedGoogle Scholar
  • 37 Ryan L, Mataraso S, Siefkas A. et al. A machine learning approach to predict deep venous thrombosis among hospitalized patients. Clin Appl Thromb Hemost 2021; 27: 10 76029621991185
    CrossrefPubMedGoogle Scholar
  • 38 Anupoomchaiya P, Sukperm A, Rojnuckarin P, Sa-Ing V. Automatic diagnosis model for risk factors of symptomatic venous thromboembolism based on machine learning. In: 2022 19th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON). Piscataway, NJ: IEEE; 2022: 1-4
    PubMedGoogle Scholar
  • 39 He L, Luo L, Hou X. et al. Predicting venous thromboembolism in hospitalized trauma patients: a combination of the Caprini score and data-driven machine learning model. BMC Emerg Med 2021; 21 (01) 60
    CrossrefPubMedGoogle Scholar
  • 40 Wang KY, Ikwuezunma I, Puvanesarajah V. et al. Using predictive modeling and supervised machine learning to identify patients at risk for venous thromboembolism following posterior lumbar fusion. Global Spine J 2023; 13 (04) 1097-1103
    CrossrefPubMedGoogle Scholar
  • 41 da Silveira WC, Ramos LEF, Silva RT. et al. Predictors of venous thromboembolism in COVID-19 patients: results of the COVID-19 Brazilian Registry. Intern Emerg Med 2022; 17 (07) 1863-1878
    CrossrefPubMedGoogle Scholar
  • 42 Bonkhoff AK, Rübsamen N, Grefkes C, Rost NS, Berger K, Karch A. Development and validation of prediction models for severe complications after acute ischemic stroke: a study based on the Stroke Registry of Northwestern Germany. J Am Heart Assoc 2022; 11 (06) e023175
    CrossrefPubMedGoogle Scholar
  • 43 Liu K, Chen J, Zhang K, Wang S, Li X. A diagnostic prediction model of acute symptomatic portal vein thrombosis. Ann Vasc Surg 2019; 61: 394-399
    CrossrefPubMedGoogle Scholar
  • 44 Hou T, Qiao W, Song S. et al. The use of machine learning techniques to predict deep vein thrombosis in rehabilitation inpatients. Clin Appl Thromb Hemost 2023; 29: 10 760296231179438
    CrossrefPubMedGoogle Scholar
  • 45 Lu C, Song J, Li H. et al. Predicting venous thrombosis in osteoarthritis using a machine learning algorithm: a population-based cohort study. J Pers Med 2022; 12 (01) 114
    CrossrefPubMedGoogle Scholar
  • 46 Meiyappan A. Advanced Risk Stratification and Prediction of Venous Thromboembolism in Critically Ill Patients. Published online 2021; available at: http://jhir.library.jhu.edu/handle/1774.2/64240.
    PubMedGoogle Scholar
  • 47 Bollepalli SC, Sahani AK, Aslam N. et al. An optimized machine learning model accurately predicts in-hospital outcomes at admission to a cardiac unit. Diagnostics (Basel) 2022; 12 (02) 241
    CrossrefPubMedGoogle Scholar
  • 48 Wang X, Yang YQ, Hong XY, Liu SH, Li JC, Chen T. A new risk assessment model of venous thromboembolism by considering fuzzy population. 2023 (e-pub ahead of print). doi: 10.21203/RS.3.RS-2987619/V1
    PubMedGoogle Scholar
  • 49 Liu H, Yuan H, Wang Y, Huang W, Xue H, Zhang X. Prediction of venous thromboembolism with machine learning techniques in young-middle-aged inpatients. Sci Rep 2021; 11 (01) 12868
    CrossrefPubMedGoogle Scholar
  • 50 Agharezaei L, Agharezaei Z, Nemati A. et al. The prediction of the risk level of pulmonary embolism and deep vein thrombosis through artificial neural network. Acta Inform Med 2016; 24 (05) 354-359
    CrossrefPubMedGoogle Scholar
  • 51 Wang X, Yang YQ, Liu SH, Hong XY, Sun XF, Shi JH. Comparing different venous thromboembolism risk assessment machine learning models in Chinese patients. J Eval Clin Pract 2020; 26 (01) 26-34
    CrossrefPubMedGoogle Scholar
  • 52 Arvind V, Kim JS, Oermann EK, Kaji D, Cho SK. Predicting surgical complications in adult patients undergoing anterior cervical discectomy and fusion using machine learning. Neurospine 2018; 15 (04) 329-337
    CrossrefPubMedGoogle Scholar
  • 53 Thangirala A, Aphinyanaphongs Y, Chen J. et al. Automated machine learning (AutoML) algorithms to predict post-operative venous thromboembolism (VTE) using the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) Database. Circulation 2022 ;146(Suppl_1): A15199
    PubMedGoogle Scholar
  • 54 Xue B, Li D, Lu C. et al. Use of machine learning to develop and evaluate models using preoperative and intraoperative data to identify risks of postoperative complications. JAMA Netw Open 2021; 4 (03) e212240
    CrossrefPubMedGoogle Scholar
  • 55 Kim JS, Merrill RK, Arvind V. et al. Examining the ability of artificial neural networks machine learning models to accurately predict complications following posterior lumbar spine fusion. Spine 2018; 43 (12) 853-860
    CrossrefPubMedGoogle Scholar
  • 56 Nudel J, Bishara AM, de Geus SWL. et al. Development and validation of machine learning models to predict gastrointestinal leak and venous thromboembolism after weight loss surgery: an analysis of the MBSAQIP database. Surg Endosc 2021; 35 (01) 182-191
    CrossrefPubMedGoogle Scholar
  • 57 Bihorac A, Ozrazgat-Baslanti T, Ebadi A. et al. MySurgeryRisk: development and validation of a machine-learning risk algorithm for major complications and death after surgery. Ann Surg 2019; 269 (04) 652-662
    CrossrefPubMedGoogle Scholar
  • 58 Rasouli Dezfouli E, Delen D, Zhao H, Davazdahemami B. A machine learning framework for assessing the risk of venous thromboembolism in patients undergoing hip or knee replacement. J Healthc Inform Res 2022; 6 (04) 423-441
    CrossrefPubMedGoogle Scholar
  • 59 Li J, Wu QQ, Zhu RH. et al. Machine learning predicts portal vein thrombosis after splenectomy in patients with portal hypertension: comparative analysis of three practical models. World J Gastroenterol 2022; 28 (32) 4681-4697
    CrossrefPubMedGoogle Scholar
  • 60 Shohat N, Ludwick L, Sherman MB, Fillingham Y, Parvizi J. Using machine learning to predict venous thromboembolism and major bleeding events following total joint arthroplasty. Sci Rep 2023; 13 (01) 2197
    CrossrefPubMedGoogle Scholar
  • 61 Wang P, Wang Y, Yuan Z. et al. Venous thromboembolism risk assessment of surgical patients in Southwest China using real-world data: establishment and evaluation of an improved venous thromboembolism risk model. BMC Med Inform Decis Mak 2022; 22 (01) 59
    CrossrefPubMedGoogle Scholar
  • 62 Ren Y, Loftus TJ, Datta S. et al. Performance of a machine learning algorithm using electronic health record data to predict postoperative complications and report on a mobile platform. JAMA Netw Open 2022; 5 (05) e2211973
    CrossrefPubMedGoogle Scholar
  • 63 Chen Y, Jiang Y. Construction of prediction model of deep vein thrombosis risk after total knee arthroplasty based on XGBoost algorithm. Comput Math Methods Med 2022; 2022: 3452348
    PubMedGoogle Scholar
  • 64 Harris AHS, Kuo AC, Weng Y, Trickey AW, Bowe T, Giori NJ. Can machine learning methods produce accurate and easy-to-use prediction models of 30-day complications and mortality after knee or hip arthroplasty?. Clin Orthop Relat Res 2019; 477 (02) 452-460
    CrossrefPubMedGoogle Scholar
  • 65 Yan YD, Yu Z, Ding LP. et al. Machine learning to dynamically predict in-hospital venous thromboembolism after inguinal hernia surgery: results from the CHAT-1 study. Clin Appl Thromb Hemost 2023 ;29:10760296231171082
    PubMedGoogle Scholar
  • 66 Gowd AK, Agarwalla A, Amin NH. et al. Construct validation of machine learning in the prediction of short-term postoperative complications following total shoulder arthroplasty. J Shoulder Elbow Surg 2019; 28 (12) e410-e421
    CrossrefPubMedGoogle Scholar
  • 67 Heus P, Damen JAAG, Pajouheshnia R. et al. Uniformity in measuring adherence to reporting guidelines: the example of TRIPOD for assessing completeness of reporting of prediction model studies. BMJ Open 2019; 9 (04) e025611
    CrossrefPubMedGoogle Scholar
  • 68 Cuschieri S. The STROBE guidelines. Saudi J Anaesth 2019; 13 (5, Suppl 1): S31-S34
    CrossrefPubMedGoogle Scholar
  • 69 Yu T, Shen R, You G. et al. Machine learning-based prediction of the post-thrombotic syndrome: model development and validation study. Front Cardiovasc Med 2022; 9: 990788
    CrossrefPubMedGoogle Scholar
  • 70 Wu Z, Li Y, Lei J. et al. Developing and optimizing a machine learning predictive model for post-thrombotic syndrome in a longitudinal cohort of patients with proximal deep venous thrombosis. J Vasc Surg Venous Lymphat Disord 2023; 11 (03) 555-564.e5
    CrossrefPubMedGoogle Scholar
  • 71 Martins TD, Annichino-Bizzacchi JM, Romano AVC, Maciel Filho R. Artificial neural networks for prediction of recurrent venous thromboembolism. Int J Med Inform 2020; 141: 104221
    CrossrefPubMedGoogle Scholar
  • 72 Danilatou V, Nikolakakis S, Antonakaki D. et al. Outcome prediction in critically-ill patients with venous thromboembolism and/or cancer using machine learning algorithms: external validation and comparison with scoring systems. Int J Mol Sci 2022; 23 (13) 7132
    CrossrefPubMedGoogle Scholar
  • 73 Mora D, Nieto JA, Mateo J. et al; RIETE Investigators. Machine Learning to Predict Outcomes in Patients with Acute Pulmonary Embolism Who Prematurely Discontinued Anticoagulant Therapy. Thromb Haemost 2022; 122 (04) 570-577
    Article in Thieme ConnectPubMedGoogle Scholar
  • 74 Mora D, Mateo J, Nieto JA. et al; Registro Informatizado de Enfermedad TromboEmbólica (RIETE) Investigators. Machine learning to predict major bleeding during anticoagulation for venous thromboembolism: possibilities and limitations. Br J Haematol 2023; 201 (05) 971-981
    CrossrefPubMedGoogle Scholar
  • 75 Herrin J, Abraham NS, Yao X. et al. Comparative effectiveness of machine learning approaches for predicting gastrointestinal bleeds in patients receiving antithrombotic treatment. JAMA Netw Open 2021; 4 (05) e2110703
    CrossrefPubMedGoogle Scholar
  • 76 El-Bouri W, Sanders A, Lip GYH. Machine learning prediction of mortality in venous thromboembolism patients: the Birmingham Black Country Venous Thromboembolism (BBC-VTE) cohort. Eur Heart J 2021; 42 (Suppl. 01) ehab724.3059
    CrossrefPubMedGoogle Scholar
  • 77 Manshad A, Akbilgic O, Brailovsky Y. et al. Machine learning-based prediction of 30-day all-cause mortality in patients hospitalized with acute pulmonary embolism. Chest 2020; 158 (04) A2213-A2214
    CrossrefPubMedGoogle Scholar
  • 78 Chen D, Wang R, Jiang Y. et al. Application of artificial neural network in daily prediction of bleeding in ICU patients treated with anti-thrombotic therapy. BMC Med Inform Decis Mak 2023; 23 (01) 171
    CrossrefPubMedGoogle Scholar
  • 79 Yu R, Kong X, Li Y. Optimizing the diagnostic algorithm for pulmonary embolism in acute COPD exacerbation using fuzzy rough sets and support vector machine. COPD 2023; 20 (01) 1-8
    CrossrefPubMedGoogle Scholar
  • 80 Contreras-Luján EE, García-Guerrero EE, López-Bonilla OR, Tlelo-Cuautle E, López-Mancilla D, Inzunza-González E. Evaluation of machine learning algorithms for early diagnosis of deep venous thrombosis. Math Comput Appl 2022; 27 (02) 24
    PubMedGoogle Scholar
  • 81 Kline JA, Novobilski AJ, Kabrhel C, Richman PB, Courtney DM. Derivation and validation of a Bayesian network to predict pretest probability of venous thromboembolism. Ann Emerg Med 2005; 45 (03) 282-290
    CrossrefPubMedGoogle Scholar
  • 82 Luo L, Kou R, Feng Y, Xiang J, Zhu W. Cost-effective machine learning based clinical pre-test probability strategy for DVT diagnosis in neurological intensive care unit. Clin Appl Thromb Hemost 2021 ;27:10760296211008650
    PubMedGoogle Scholar
  • 83 Patil S, Henry JW, Rubenfire M, Stein PD. Neural network in the clinical diagnosis of acute pulmonary embolism. Chest 1993; 104 (06) 1685-1689
    CrossrefPubMedGoogle Scholar
  • 84 Falsetti L, Merelli E, Rucco M. et al. A data-driven clinical prediction rule for pulmonary embolism. Eur Heart J 2013 ;34(1):P243
    PubMedGoogle Scholar
  • 85 Rucco M, Sousa-Rodrigues D, Merelli E. et al. Neural hypernetwork approach for pulmonary embolism diagnosis. BMC Res Notes 2015; 8 (01) 617
    CrossrefPubMedGoogle Scholar
  • 86 Navarro A. Constanza Lourdes. Quality of Machine Learning Prediction Models in Healthcare. Utrecht University; 2023. DOI: 10.33540/1394
    CrossrefGoogle Scholar
  • 87 Bastidas AR, Faizal Gómez I, Ortiz Ramírez S, Aguirre G, Mantilla Cardozo BM. Validity wells, Geneva and PISA score with use of intelligence artificial for pulmonary embolism diagnosis. In: D36. Innovations in Research Methods and Evidence Synthesis. New York, NY: American Thoracic Society; 2019: A6221-A6221
    Google Scholar
  • 88 Willan J, Katz H, Keeling D. The use of artificial neural network analysis can improve the risk-stratification of patients presenting with suspected deep vein thrombosis. Br J Haematol 2019; 185 (02) 289-296
    CrossrefPubMedGoogle Scholar
  • 89 Somani SS, Honarvar H, Narula S. et al. Development of a machine learning model using electrocardiogram signals to improve acute pulmonary embolism screening. Eur Heart J Digit Health 2021; 3 (01) 56-66
    CrossrefPubMedGoogle Scholar
  • 90 Villacorta H, Pickering JW, Horiuchi Y. et al. Machine learning with D-dimer in the risk stratification for pulmonary embolism: a derivation and internal validation study. Eur Heart J Acute Cardiovasc Care 2022; 11 (01) 13-19
    CrossrefPubMedGoogle Scholar
  • 91 Gawlitza J, Ziegelmayer S, Wilkens H. et al. Beyond the d-dimer - Machine-learning assisted pre-test probability evaluation in patients with suspected pulmonary embolism and elevated d-dimers. Thromb Res 2021; 205: 11-16
    CrossrefPubMedGoogle Scholar
  • 92 Banerjee I, Sofela M, Yang J. et al. Development and Performance of the Pulmonary Embolism Result Forecast Model (PERFORM) for computed tomography clinical decision support. JAMA Netw Open 2019; 2 (08) e198719
    CrossrefPubMedGoogle Scholar
  • 93 Su H, Han Z, Fu Y. et al. Detection of pulmonary embolism severity using clinical characteristics, hematological indices, and machine learning techniques. Front Neuroinform 2022; 16: 1029690
    CrossrefPubMedGoogle Scholar
  • 94 von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP. STROBE Initiative. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. Int J Surg 2014; 12 (12) 1495-1499
    CrossrefPubMedGoogle Scholar
  • 95 Kline JA, Mitchell AM, Kabrhel C, Richman PB, Courtney DM. Clinical criteria to prevent unnecessary diagnostic testing in emergency department patients with suspected pulmonary embolism. J Thromb Haemost 2004; 2 (08) 1247-1255
    CrossrefPubMedGoogle Scholar
  • 96 Sharabiani A, Bress A, Douzali E, Darabi H. Revisiting warfarin dosing using machine learning techniques. Comput Math Methods Med 2015; 2015: 560108
    CrossrefPubMedGoogle Scholar
  • 97 Lee H, Kim HJ, Chang HW, Kim DJ, Mo J, Kim JE. Development of a system to support warfarin dose decisions using deep neural networks. Sci Rep 2021; 11 (01) 14745
    CrossrefPubMedGoogle Scholar
  • 98 Asiimwe IG, Blockman M, Cohen K. et al. Stable warfarin dose prediction in sub-Saharan African patients: a machine-learning approach and external validation of a clinical dose-initiation algorithm. CPT Pharmacometrics Syst Pharmacol 2022; 11 (01) 20-29
    CrossrefPubMedGoogle Scholar
  • 99 Truda G, Marais P. Evaluating warfarin dosing models on multiple datasets with a novel software framework and evolutionary optimisation. J Biomed Inform 2021; 113: 103634
    CrossrefPubMedGoogle Scholar
  • 100 Jahmunah V, Chen S, Oh SL, Acharya UR, Chowbay B. Automated warfarin dose prediction for Asian, American, and Caucasian populations using a deep neural network. Comput Biol Med 2023; 153: 106548
    CrossrefPubMedGoogle Scholar
  • 101 Ma W, Li H, Dong L. et al. Warfarin maintenance dose prediction for Chinese after heart valve replacement by a feedforward neural network with equal stratified sampling. Sci Rep 2021; 11 (01) 13778
    CrossrefPubMedGoogle Scholar
  • 102 Grossi E, Podda GM, Pugliano M. et al. Prediction of optimal warfarin maintenance dose using advanced artificial neural networks. Pharmacogenomics 2014; 15 (01) 29-37
    CrossrefPubMedGoogle Scholar
  • 103 Gordon J, Norman M, Hurst M. et al. Using machine learning to predict anticoagulation control in atrial fibrillation: a UK Clinical Practice Research Datalink study. Inform Med Unlocked 2021; 25: 100688
    CrossrefPubMedGoogle Scholar
  • 104 Goto S, Goto S, Pieper KS. et al. New artificial intelligence prediction model using serial prothrombin time international normalized ratio measurements in atrial fibrillation patients on vitamin K antagonists: GARFIELD-AF. Eur Heart J Cardiovasc Pharmacother 2020; 6 (05) 301-309
    CrossrefPubMedGoogle Scholar
  • 105 Whirl-Carrillo M, McDonagh EM, Hebert JM. et al. Pharmacogenomics knowledge for personalized medicine. Clin Pharmacol Ther 2012; 92 (04) 414-417
    CrossrefPubMedGoogle Scholar
  • 106 Ma Z, Wang P, Gao Z, Wang R, Khalighi K. Ensemble of machine learning algorithms using the stacked generalization approach to estimate the warfarin dose. PLoS One 2018; 13 (10) e0205872
    CrossrefPubMedGoogle Scholar
  • 107 Anzabi Zadeh S, Street WN, Thomas BW. Optimizing warfarin dosing using deep reinforcement learning. J Biomed Inform 2023; 137: 104267
    CrossrefPubMedGoogle Scholar
  • 108 Nemati S, Ghassemi MM, Clifford GD. Optimal medication dosing from suboptimal clinical examples: a deep reinforcement learning approach. In: 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Piscataway, NJ: IEEE; 2016: 2978-2981
    PubMedGoogle Scholar
  • 109 Lian J, Zhao QF. Prediction of heparin dose during continuous renal replacement therapy surgery by using the gradient boosting regression model. In: 2019 6th International Conference on Control, Decision and Information Technologies (CoDIT). Piscataway, NJ: IEEE; 2019: 182-186
    PubMedGoogle Scholar
  • 110 Abdel-Hafez A, Scott IA, Falconer N. et al. Predicting therapeutic response to unfractionated heparin therapy: machine learning approach. Interact J Med Res 2022; 11 (02) e34533
    CrossrefPubMedGoogle Scholar
  • 111 Su L, Liu C, Li D. et al. Toward optimal heparin dosing by comparing multiple machine learning methods: retrospective study. JMIR Med Inform 2020; 8 (06) e17648
    CrossrefPubMedGoogle Scholar
  • 112 Lee J, Scott DJ, Villarroel M, Clifford GD, Saeed M, Mark RG. Open-access MIMIC-II database for intensive care research. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Piscataway, NJ: IEEE; 2011: 8315-8318
    PubMedGoogle Scholar
  • 113 Johnson AEW, Pollard TJ, Shen L. et al. MIMIC-III, a freely accessible critical care database. Sci Data 2016; 3 (01) 160035
    CrossrefPubMedGoogle Scholar
  • 114 Pollard TJ, Johnson AEW, Raffa JD, Celi LA, Mark RG, Badawi O. The eICU Collaborative Research Database, a freely available multi-center database for critical care research. Sci Data 2018; 5: 180178
    CrossrefPubMedGoogle Scholar
  • 115 Labovitz DL, Shafner L, Reyes Gil M, Virmani D, Hanina A. Using artificial intelligence to reduce the risk of nonadherence in patients on anticoagulation therapy. Stroke 2017; 48 (05) 1416-1419
    CrossrefPubMedGoogle Scholar
  • 116 Nwanosike EM, Sunter W, Ansari MA, Merchant HA, Conway B, Hasan SS. A real-world exploration into clinical outcomes of direct oral anticoagulant dosing regimens in morbidly obese patients using data-driven approaches. Am J Cardiovasc Drugs 2023; 23 (03) 287-299
    CrossrefPubMedGoogle Scholar
  • 117 Meid AD, Wirbka L, Groll A, Haefeli WE. ARMIN Study Group. Can machine learning from real-world data support drug treatment decisions? A prediction modeling case for direct oral anticoagulants. Med Decis Making 2022; 42 (05) 587-598
    CrossrefPubMedGoogle Scholar
  • 118 Symeonidis P, Kostoulas T, Danilatou V, Andras C, Chairistanidis S. Mortality prediction and safe drug recommendation for critically-ill patients. In: 2022 IEEE 22nd International Conference on Bioinformatics and Bioengineering (BIBE). Piscataway, NJ: IEEE; 2022: 79-84
    PubMedGoogle Scholar
  • 119 Chen RJ, Lu MY, Chen TY, Williamson DFK, Mahmood F. Synthetic data in machine learning for medicine and healthcare. Nat Biomed Eng 2021; 5 (06) 493-497
    CrossrefPubMedGoogle Scholar
  • 120 Accessed April 22, 2024 at: www.physionet.org
    PubMed
  • 121 European Commission. European Health Union: A European Health data space for people and science. 2022 . Accessed April 22, 2024 at: https://ec.europa.eu/commission/presscorner/detail/e%20n/ip_22_2711
    PubMedGoogle Scholar
  • 122 Kucher N, Koo S, Quiroz R. et al. Electronic alerts to prevent venous thromboembolism among hospitalized patients. N Engl J Med 2005; 352 (10) 969-977
    CrossrefPubMedGoogle Scholar
  • 123 Huang X, Zhou S, Ma X. et al. Effectiveness of an artificial intelligence clinical assistant decision support system to improve the incidence of hospital-associated venous thromboembolism: a prospective, randomised controlled study. BMJ Open Qual 2023; 12 (04) e002267
    CrossrefPubMedGoogle Scholar
  • 124 Zhou S, Ma X, Jiang S. et al. A retrospective study on the effectiveness of Artificial Intelligence-based Clinical Decision Support System (AI-CDSS) to improve the incidence of hospital-related venous thromboembolism (VTE). Ann Transl Med 2021; 9 (06) 491-491
    CrossrefPubMedGoogle Scholar
  • 125 Korjus K, Hebart MN, Vicente R. An efficient data partitioning to improve classification performance while keeping parameters interpretable. PLoS One 2016; 11 (08) e0161788
    CrossrefPubMedGoogle Scholar
  • 126 Mincu D, Roy S. Developing robust benchmarks for driving forward AI innovation in healthcare. Nat Mach Intell 2022; 4 (11) 916-921
    CrossrefPubMedGoogle Scholar
  • 127 Mateen BA, Liley J, Denniston AK, Holmes CC, Vollmer SJ. Improving the quality of machine learning in health applications and clinical research. Nat Mach Intell 2020; 2 (10) 554-556
    CrossrefPubMedGoogle Scholar
  • 128 Chen PC, Liu Y, Peng L. How to develop machine learning models for healthcare. Nat Mater 2019; 18 (05) 410-414
    CrossrefPubMedGoogle Scholar
  • 129 Volovici V, Syn NL, Ercole A, Zhao JJ, Liu N. Steps to avoid overuse and misuse of machine learning in clinical research. Nat Med 2022; 28 (10) 1996-1999
    CrossrefPubMedGoogle Scholar
  • 130 Stevens LM, Mortazavi BJ, Deo RC, Curtis L, Kao DP. Recommendations for reporting machine learning analyses in clinical research. Circ Cardiovasc Qual Outcomes 2020; 13 (10) e006556
    CrossrefPubMedGoogle Scholar
  • 131 Hernandez-Boussard T, Bozkurt S, Ioannidis JPA, Shah NH. MINIMAR (MINimum Information for Medical AI Reporting): Developing reporting standards for artificial intelligence in health care. J Am Med Inform Assoc 2020; 27 (12) 2011-2015
    CrossrefPubMedGoogle Scholar
  • 132 Panel for the Future of Science and Technology (STOA) European Parliamentary Research Service (EPRS). Panel for the future of science and tech auditing the quality of datasets used in algorithmic decision making systems. 2022 . Accessed April 22, 2024 at: https://www.europarl.europa.eu/regdata/etudes/stud/2022/729541/eprs_stu(2022)729541_en.pdf
    PubMedGoogle Scholar
  • 133 Moons KGM, Altman DG, Reitsma JB. et al. Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): explanation and elaboration. Ann Intern Med 2015; 162 (01) W1-73
    CrossrefPubMedGoogle Scholar
  • 134 Collins GS, Moons KGM. Reporting of artificial intelligence prediction models. Lancet 2019; 393 (10181): 1577-1579
    CrossrefPubMedGoogle Scholar
  • 135 Sounderajah V, Ashrafian H, Golub RM. et al; STARD-AI Steering Committee. Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: the STARD-AI protocol. BMJ Open 2021; 11 (06) e047709
    CrossrefPubMedGoogle Scholar
  • 136 Vasey B, Nagendran M, Campbell B. et al; DECIDE-AI expert group. Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI. Nat Med 2022; 28 (05) 924-933
    CrossrefPubMedGoogle Scholar
  • 137 Cruz Rivera S, Liu X, Chan AW, Denniston AK, Calvert MJ. SPIRIT-AI and CONSORT-AI Working Group, SPIRIT-AI and CONSORT-AI Steering Group, SPIRIT-AI and CONSORT-AI Consensus Group. Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension. Nat Med 2020; 26 (09) 1351-1363
    CrossrefPubMedGoogle Scholar
  • 138 Liu X, Cruz Rivera S, Moher D, Calvert MJ, Denniston AK. SPIRIT-AI and CONSORT-AI Working Group. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension. Nat Med 2020; 26 (09) 1364-1374
    CrossrefPubMedGoogle Scholar
  • 139 Karimian G, Petelos E, Evers SMAA. The ethical issues of the application of artificial intelligence in healthcare: a systematic scoping review. AI Ethics 2022; 2 (04) 539-551
    CrossrefPubMedGoogle Scholar
  • 140 Castelnovo A, Crupi R, Greco G, Regoli D, Penco IG, Cosentini AC. A clarification of the nuances in the fairness metrics landscape. Sci Rep 2022; 12 (01) 4209
    CrossrefPubMedGoogle Scholar
  • 141 Yatish H, Swamy S. Recent trends in time series forecasting-a survey. Int Res J Eng Technol 2020; 7 (04) 5623-5628
    PubMedGoogle Scholar
  • 142 Falconer N, Abdel-Hafez A, Scott IA, Marxen S, Canaris S, Barras M. Systematic review of machine learning models for personalised dosing of heparin. Br J Clin Pharmacol 2021; 87 (11) 4124-4139
    CrossrefPubMedGoogle Scholar
  • 143 Deshpande CG, Kogut S, Laforge R, Willey C. Impact of medication adherence on risk of ischemic stroke, major bleeding and deep vein thrombosis in atrial fibrillation patients using novel oral anticoagulants. Curr Med Res Opin 2018; 34 (07) 1285-1292
    CrossrefPubMedGoogle Scholar
  • 144 Gu Y, Zalkikar A, Liu M. et al. Predicting medication adherence using ensemble learning and deep learning models with large scale healthcare data. Sci Rep 2021; 11 (01) 18961
    CrossrefPubMedGoogle Scholar
  • 145 Symeonidis P, Chairistanidis S, Zanker M. Safe, effective and explainable drug recommendation based on medical data integration. User Model User-adapt Interact 2022; 32 (05) 999-1018
    CrossrefPubMedGoogle Scholar
  • 146 Cahan N, Klang E, Marom EM. et al. Multimodal fusion models for pulmonary embolism mortality prediction. Sci Rep 2023; 13 (01) 7544
    CrossrefPubMedGoogle Scholar