Intelligent Data Analysis for Knowl edge Discovery, Patient Monitoring and Quality Assessment

Summary

Objective: To introduce the focus theme of Methods of Information in Medicine on Intelligent Data Analysis for Knowledge Discovery, Patient Monitoring and Quality Assessment.

Methods: Based on two workshops on Intelligent Data Analysis in bioMedicine (IDAMAP) held in Washington, DC, USA (2010) and Bled, Slovenia (2011), six authors were invited to write full papers for the focus theme. Each paper was throughly reviewed by anonymous referees and revised one or more times by the authors.

Results: The selected papers cover four ongoing and emerging topics in Intelligent Data Analysis (IDA), being i) systems biology and metabolic pathway modelling; ii) gene expression data modelling; iii) signal processing from in-home monitoring systems; and iv) quality of care assessment. Each of these topics is discussed in detail to introduce the papers to the reader.

Conclusion: The development and application of IDA methods in biomedicine is an active area of research which continues to blend with other subfields of medical informatics. As data become increasingly ubiquitous in the biomedical domain, the demand for fast, smart and flexible data analysis methods is undiminished.

• References

• 1 Agrawal R, Srikant R. Fast algorithms for mining association rules. In: Proc. of the 1994 Int. Conf. Very Large Data Bases (VLDB’94) 1994: 487-499.
• 2 Bucklin RE, Sismeiro C. A model of web site browsing behavior estimated on clickstream data. J Market Res 2003; 40 (03) 249-267.
• 3 Senathirajah Y, Bakken S. Visual clustering analysis of CIS logs to inform creation of a user-configurable Web CIS interface. Methods Inf Med 2011; 50 (04) 337-348.
• 4 Hand DJ, Mannila H, Smyth P. Principles of Data Mining. Cambridge, Massachusetts: MIT Press; 2001.
• 5 Giudici P. Applied Data Mining Statistical Methods for Business and Industry. London: John Wiley & Sons; 2003.
• 6 Han J, Kamber M. Data Mining. Concepts and Techniques. San Francisco, California: Morgan Kaufmann Publishers; 2006.
• 7 Fayyad UM, Piatetsky-Shapiro G, Smyth P. The KDD process for extracting useful knowledge from volumes of data. Commun ACM 1996; 39 (11) 27-34.
• 8 Fayyad UM, Piatetsky-Shapiro G, Smyth P, Uthurusamy R. Advances in Knowledge Discovery and Data Mining. Menlo Park, California: AAAI Press; 1996.
• 9 Berthold M, Hand DJ. Intelligent Data Analysis: An Introduction. New York: Springer; 2003.
• 10 Bellazzi R, Diomidous M, Sarkar IN, Takabayashi K, Ziegler A, McCray AT. Data analysis and data mining: current issues in biomedical informatics. Methods Inf Med 2011; 50 (06) 536-544.
• 11 Lavrac N, Kononenko I, Keravnou E, Kukar M, Zupan B. Intelligent data analysis for medical diagnosis: using machine learning and temporal abstraction. AI Commun 1998; 11: 191-218.
• 12 Kononenko I. Machine learning for medical diagnosis: history, state of the art and perspective. Artif Intell Med 2001; 23: 89-109.
• 13 Mancini F, Sousa FS, Hummel AD, Falcão AE, Yi LC, Ortolani CF, Sigulem D, Pisa IT. Classification of postural profiles among mouth-breathing children by learning vector quantization. Methods Inf Med 2011; 50 (04) 349-357.
• 14 Abu-Hanna A, Lucas PJ. Prognostic models in medicine: AI and statistical approaches. Methods Inf Med 2001; 40 (01) 1-5.
• 15 Ma S, Huang J, Xie Y, Yi N. Identification of breast cancer prognosis markers using integrative sparse boosting. Methods Inf Med 2012; 51 (02) 152-161.
• 16 Eyal E, Degani H. Model-based and model-free parametric analysis of breast dynamic-contrast-enhanced MRI. NMR Biomed 2009; 22 (01) 40-53.
• 17 Gong L, Kulikowski CA. High-performance medical imaging informatics. Methods Inf Med 2012; 51 (03) 258-259.
• 18 Brossette SE, Sprague AP, Jones WT, Moser SA. A data mining system for infection control surveillance. Methods Inf Med 2000; 39 (4-5) 303-310.
• 19 Concaro S, Sacchi L, Cerra C, Fratino P, Bellazzi R. Mining health care administrative data with temporal association rules on hybrid events. Methods Inf Med 2011; 50 (02) 166-179.
• 20 Bellazzi R, Zupan B. Towards knowledge-based gene expression data mining. J Biomed Inform 2007; 40 (06) 787-802.
• 21 Saeed M, Villarroel M, Reisner AT, Clifford G, Lehman LW, Moody G, Heldt T, Kyaw TH, Moody B, Mark RG. Multiparameter Intelligent Monitoring in Intensive Care II: a public-access intensive care unit database. Crit Care Med 2011; 39 (05) 952-960.
• 22 Patel S, Park H, Bonato P, Chan L, Rodgers M. A review of wearable sensors and systems with application in rehabilitation. J Neuroeng Rehabil 2012; 9: 21
• 23 Marschollek M, Rehwald A, Wolf KH, Gietzelt M, Nemitz G, Meyer Zu Schwabedissen H, Haux R. Sensor-based fall risk assessment - an expert ‘to go’. Methods Inf Med 2011; 50 (05) 420-426.
• 24 www.biolab.si/idamap Last visited on June 18 2012
• 25 http://www.imia-medinfo.org/new2/node/144 Last visited on June 18 2012
• 26 Zupan B, Lavrac N, Keravnou E. Data mining techniques and applications in medicine. Artif Intell Med 1999; 16 (01) 1-2.
• 27 Bellazzi R, Zupan B. Intelligent data analysis--special issue. Methods Inf Med 2001; 40 (05) 362-364.
• 28 Zupan B, Holmes JH, Bellazzi R. Knowledge-based data analysis and interpretation. Artif Intell Med 2006; 37 (03) 163-165.
• 29 Holmes JH, Peek N. Intelligent data analysis in biomedicine. J Biomed Inform 2007; 40 (06) 605-608.
• 30 Peek N, Combi C, Tucker A. Biomedical data mining. Methods Inf Med 2009; 48 (03) 225-228.
• 31 Fischer H. Mathematical Modeling of Complex Biological Systems. Alcohol Research and Health 2008; 31 (01) 49-59.
• 32 Smolke C, Silver P. Informing biological design by integration of systems and synthetic biology. Cell 2011; 144 (06) 855-859.
• 33 Saket N, Bar-Joseph Z. Algorithms in nature: the convergence of systems biology and computational thinking. Molecular Systems Biology 2011; 7: 546
• 34 Kitano H. Systems biology: a brief overview. Science 2002; 295 5560 1662-1664.
• 35 Pavlidis S, Swift S, Payne A. Pathway based microarray analysis, utilising enzyme compounds and cascade events. Methods Inf Med 2012; 51 (04) 323-331.
• 36 Kanehisa M, Goto S. KEGG: Kyoto encyclopaedia of genes and genomes. Nucleic Acid Research 2000; 28 (01) 27-30.
• 37 Vastrik I, D’Eustachio P, Schmidt E, Joshi-Tope G, Gopinath G, Croft D, de Bono B, Gillespie M, Jassal B, Lewis S, Matthews L, Wu G, Birney E, Stein L. Reactome: a knowledge base of biologic pathways and processes. Genome Biology 2007; 8: R39
• 38 Entrez, The Life Sciences Search Engine (Internet). 2012 (cited June 18, 2012) Available from: http://www.ncbi.nlm.nih.gov/sites/gquery.
• 39 Sorger P. A reductionist’s systems biology: opinion. Current Opinion in Cell Biology 2005; 17 (01) 9-11.
• 40 Patterson A, Ashtari M, Ribe D, Stenbeck G, Tucker A. Intelligent Data Analysis to Model and Understand Live Cell Time-Lapse Sequences. Methods Inf Med 2012; 51 (04) 332-340.
• 41 Schena M, Shalon D, Davis R, Brown P. Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 1995; 20 (270) 467-470.
• 42 Parkinson H, Sarkans U, Shojatalab M, Abeygunawardena N, Contrino S, Coulson R, Farne A, Garcia Lara G, Holloway E, Kapushesky M, Lilja P, Mukherjee G, Oezcimen A, Rayner T, Rocca-Serra P, Sharma A, Sansone S, Brazma A. ArrayExpress - a public repository for microarray gene expression data at the EBI. Nucleic Acids Research 2003; 33 (01) 68-71.
• 43 Jafari P, Azuaje F. An assessment of recently published gene expression data analyses: reporting experimental design and statistical factors. BMC Medical Informatics and Decision Making 2006; 6: 27
• 44 Jenner R, Maillard K, Cattini N, Weiss R, Boshoff C, Wooster R, Kellam P. Kaposi’s sarcoma-associated herpesvirus-infected primary effusion lymphoma has a plasma cell gene expression profile. Proceedings of the National Academy of Science 2003; 100 (18) 10399-10404.
• 45 Mulas F, Zagar L, Zupan B, Bellazzi R. Supporting regenerative medicine by integrative dimensionality reduction. Methods Inf Med 2012; 51 (04) 341-347.
• 46 Wang Z, Gerstein M, Snyder M. RNA-Seq: a revolutionary tool for transcriptomics. Nature Reviews Genetics 2009; 10 (01) 57-63.
• 47 Institute of Medicine To Err is Human. Washington, DC, USA: National Academy Press; 2000.
• 48 Institute of Medicine Crossing the Quality Chasm: A New Health System for the 21st Century. Washington, DC, USA: National Academy Press; 2001.
• 49 Bero LA, Grilli R, Grimshaw JM, Harvey E, Oxman AD, Thomson MA. Closing the gap between research and practice: an overview of systematic reviews of interventions to promote the implementation of research findings. British Medical Journal 1998; 317 7156 465-468.
• 50 Thorsen T, Mäkelä M. Changing professional practice. Theory and practice of Clinical Guidelines Implementation. Copenhagen, Denmark: Danish Institute for Health Services; 1999.
• 51 Koetsier A, van der Veer SN, Jager KJ, Peek N, de Keizer NF. Control charts in healthcare quality improvement. A systematic review on adherence to methodological criteria. Methods Inf Med 2012; 51 (03) 189-198.
• 52 Donabedian A. Evaluating the quality of medical care. The Milbank Memorial Fund Quarterly 1966; 44 (3 Pt. 2) 166-203.
• 53 Stiglic G, Kokol P. Discovering subgroups using descriptive models of adverse outcomes in medical care. Methods Inf Med 2012; 51 (04) 348-352.
• 54 Harrison DA, Brady AR, Rowan K. Case mix, outcome and length of stay for admissions to adult, general critical care units in England, Wales and Northern Ireland: the Intensive Care National Audit & Research Centre Case Mix Programme Database. Crit Care 2004; 8 (02) R99-111.
• 55 Arts D, de Keizer N, Scheffer GJ, de Jonge E. Quality of data collected for severity of illness scores in the Dutch National Intensive Care Evaluation (NICE) registry. Intensive Care Med 2002; 28 (05) 656-659.
• 56 Le Gall JR, Lemeshow S, Saulnier F. A new Simplified Acute Physiology Score (SAPS II) based on a European/North American multicenter study. JAMA 1993; 270: 2957-2963.
• 57 Zimmerman JE, Kramer AA, McNair DS, Malila FM. Acute Physiology and Chronic Health Evaluation (APACHE) IV: hospital mortality assessment for today’s critically ill patients. Crit Care Med 2006; 34 (05) 1297-1310.
• 58 de Rooij SE, Abu-Hanna A, Levi M, de Jonge E. Identification of high-risk subgroups in very elderly intensive care unit patients. Crit Care 2007; 11: R33
• 59 Nannings B, Abu-Hanna A, de Jonge E. Applying PRIM (patient rule induction method) and logistic regression for selecting high-risk subgroups in very elderly ICU patients. Int J Med Inform 2008; 77: 272-279.
• 60 Ambalavanan N, Carlo WA. Comparison of the prediction of extremely low birth weight neonatal mortality by regression analysis and by neural networks. Early Hum Develop 2001; 65: 123-137.
• 61 Neumann A, Holstein J, Le Gall JR. et al. Measuring performance in health care: case-mix adjustment by boosted decision trees. Artificial Intelligence in Medicine 2004; 32 (02) 97-113.
• 62 Minne L, Eslami S, de Keizer NF, de Jonge E, de Rooij S, Abu-Hanna A. Statistical process control for monitoring standardized mortality ratios of a classification tree model. Methods Inf Med 2012; 51 (04) 353-358.
• 63 Forkner-Dunn J. Internet-based patient self-care. The next generation of health care delivery. J Med Internet Res 2003; 5 (02) e8
• 64 Johnston B, Wheeler L, Deuser J, Sousa K.H. Outcomes of the Kaiser Permanente Tele-Home Health Research Project. Arch Fam Med 2000; 9: 40-45.
• 65 Alwan M, Sifferlin EB, Turner B, Kell S, Brower P, Mack DC, Dalal S, Felder RA. Impact of passive health status monitoring to care providers and payers in assisted living. Telemed J E Health 2007; 13 (03) 279-285.
• 66 Barnes N, Webster S, Mizutani T, Ng J, Buckland M, Reeves A. Liverpool Telecare Pilot: case studies. Inform Prim Care 2006; 14 (03) 197-202.
• 67 Faes L, Nollo G. Assessing frequency domain causality in cardiovascular time series with instantaneous interactions. Methods Inf Med 2010; 49 (05) 453-457.
• 68 Matic A, Mehta P, Rehg JM, Osmani V, Mayora O. Monitoring dressing activity failures through RFID and video. Methods Inf Med 2012; 51 (01) 45-54.
• 69 Popescu M, Mahnot A. Early illness recognition in older adults using in-home monitoring sensors and multiple instance learning. Methods Inf Med 2012; 51 (04) 359-367.