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Data Driven Analytics for Personalized Healthcare

  • Chapter
Healthcare Information Management Systems

Part of the book series: Health Informatics ((HI))

Abstract

The concept of Learning Health Systems (LHS) is gaining momentum as more and more electronic healthcare data becomes increasingly accessible. The core idea is to enable learning from the collective experience of a care delivery network as recorded in the observational data, to iteratively improve care quality as care is being provided in a real world setting. In line with this vision, much recent research effort has been devoted to exploring machine learning, data mining and data visualization methodologies that can be used to derive real world evidence from diverse sources of healthcare data to provide personalized decision support for care delivery and care management. In this chapter, we will give an overview of a wide range of analytics and visualization components we have developed, examples of clinical insights reached from these components, and some new directions we are taking.

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References

  1. Alexander GC, Stafford RS. Does comparative effectiveness have a comparative edge? JAMA. 2009;301:2488–90.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  2. Berwick DM. Disseminating innovations in healthcare. JAMA. 2003;289:1969–75.

    Article  PubMed  Google Scholar 

  3. Ebadollahi S, Sun J, Gotz D, Hu J, Sow D, Neti C. Predicting patient’s trajectory of physiological data using temporal trends in similar patients: a system for near-term prognostics. AMIA Annu Symp Proc. 2010;2010:192–6.

    PubMed Central  PubMed  Google Scholar 

  4. Gawande A. The hot spotters. New Yorker, Jan 2011.

    Google Scholar 

  5. Global Initiative for Chronic Obstructive Lung Disease. Global strategy for the diagnosis, management, and prevention of COPD. 2014. http://www.goldcopd.org/guidelines-global-strategy-for-diagnosis-management.html. Accessed 21 Apr 2015.

  6. Gotz D, Starvropoulos H, Sun J, Wang F. ICDA: a platform for intelligent care delivery analytics. Am Med Inform Assoc Annu Symp AMIA. 2012;2012:264–73.

    Google Scholar 

  7. Halpern Y, Sontag D. Unsupervised learning of noisy-or bayesian networks. In: Proceedings of the twenty-ninth conference on uncertainty in artificial intelligence (UAI2013). Bellevue, WA, USA. 2013. p. 272–81. arXiv:1309.6834 [cs.LG].

  8. Hu J, Wang F, Sun J, Sorrentino R, Ebadollahi S. A healthcare utilization analysis framework for hot spotting and contextual anomaly detection. Am Med Inform Assoc Annu Symp (AMIA 2012). 2012;2012:360–9.

    Google Scholar 

  9. Krause J, Perer A, Bertini E. INFUSE: interactive feature selection for predictive modelling of high dimensional data. Paris: IEEE Visual Analytics Science and Technology (VAST 2014); 2014.

    Google Scholar 

  10. Lenfant C. Clinical research to clinical practice – lost in translation. N Engl J Med. 2003;349:868–74.

    Article  PubMed  Google Scholar 

  11. LHS. Institute of Medicine Report: best care at lower cost: the path to continuously learning health care in America, released on 6 Sept 2012. 2013. http://www.iom.edu/Reports/2012/Best-Care-at-Lower-Cost-The-Path-to-Continuously-Learning-Health-Care-in-America.aspx.

  12. Luo D, Wang F, Sun J, Markatou M, Hu J, Ebadollahi S. SOR: scalable orthogonal regression for non redundant feature selection and its healthcare applications. SIAM Data Mining. 2012. http://www.research.ibm.com/healthcare/papers/sorSDM2012.pdf.

  13. Markatou M, Kuruppumullage Don P, Hu J, Wang F, Sun J, Sorrentino R, Ebadollahi S. Case-based reasoning in comparative effectiveness research. IBM J Dev Res. 2012;56(5):468–79.

    Article  Google Scholar 

  14. Mitsa T, editor. Temporal data mining. 1st ed. Boca Raton: Chapman & Hall/CRC; 2010.

    Google Scholar 

  15. Ng K, Ghoting A, Steinhubl SR, Stewart WF, Malin B, Sun J. PARAMO: a PARAllel predictive MOdeling platform for healthcare analytic research using electronic health records. J Biomed Inform. 2014;48:160–70.

    Article  PubMed Central  PubMed  Google Scholar 

  16. Partners Healthcare. i2b2. 2014. https://www.i2b2.org/.

  17. Perer A, Gotz D. Data driven exploration of care plans for patients. Paris: ACM CHI; 2013.

    Book  Google Scholar 

  18. Perer A, Sun J. MatrixFlow: temporal network visual analytics to track symptom evolution during disease progression. Am Med Inform Assoc Annu Symp (AMIA 2012). 2012;2012:716–25.

    Google Scholar 

  19. Perer A, Wang F. Frequence: interactive mining and visualization of temporal frequent event sequences. In: IUI ’14 proceedings of the 19th international conference on intelligent user interfaces. New York: ACM; 2014. doi:10.1145/2557500.2557508.

  20. Plaisant C, Mushlin R, Snyder A, Li J, Heller D, Shneiderman B. Lifelines: using visualization to enhance navigation and analysis of patient records. In American Medical Informatics Association Annual Symposium (AMIA), AMIA 1998 (1998), 7680.

    Google Scholar 

  21. Shwe MA, Middleton B, Heckerman D, Henrion M, Horvitz E, Lehmann H, Cooper G. Probabilistic diagnosis using a reformulation of the internist-1/qmr knowledge base. Methods Inf Med. 1991;30:241–55.

    CAS  PubMed  Google Scholar 

  22. Sun J, Hu J, Luo D, Markatou M, Wang F, Edabollahi S, Steinhubl SE, Daar Z, Stewart WF. Combining knowledge and data driven insights for identifying risk factors using electronic health records. AMIA. 2012;2012:901–10.

    PubMed Central  Google Scholar 

  23. Sun J, Sow DM, Hu J, Ebadollahi S. A system for mining temporal physiological data streams for advanced prognostic decision support. In: IEEE international conference on data mining. 2010. p. 1061–66. http://www.research.ibm.com/healthcare/papers/05694085.pdf.

  24. Sun J, Sow DM, Hu J, Ebadollahi S. Localized supervised metric learning on temporal physiological data. In: International conference on pattern recognition. 2010. p. 4149–52. http://www.research.ibm.com/healthcare/papers/05597728.pdf.

  25. Tracy CS, Dantas G, Upshur R. Evidence- based medicine in primary care: qualitative study of family physicians. BMC Fam Pract. 2003;4(1):6.

    Article  PubMed Central  PubMed  Google Scholar 

  26. Wang X, Sontag D, Wang F. Unsupervised learning of disease progression models. In: Proceedings of the 20th ACM SIGKDD international conference on knowledge discovery and data mining. New York: ACM; 2014. p. 85–94.

  27. Wang F, Sun J, Hu J, Ebadollahi S. iMet: interactive metric learning in healthcare applications. In: SIAM Data Mining Conference. 2011. pp. 944–55. http://www.research.ibm.com/healthcare/papers/304.pdf.

  28. Wang F, Sun J, Ebadollahi S. Integrating distance metrics learned from multiple experts and its application in inter-patient similarity assessment. In: SIAM Data Mining Conference. 2011. p. 59–70. http://www.research.ibm.com/healthcare/papers/113.pdf.

  29. Wang X, Wang F, Wang J, Qian B, Hu J. Exploring patient risk groups with incomplete knowledge. 2013 IEEE 13th international conference on data mining (ICDM). New York: IEEE; 2013. p. 1223–28.

  30. Wang F, Zhang C. Feature extraction by maximizing the average neighborhood margin. In: Computer Vision and Pattern Recognition, New York: IEEE; 2007. p. 1–8.

  31. Wang F, Zhang C. Label propagation through linear neighborhoods. In: Proceedings of the 23rd international conference on machine learning, Pittsburgh, 2006, p. 985–92. http://www.autonlab.org/icml_documents/camera-ready/124_Label_Propagation_th.pdf.

  32. WDA. What is Watson? 2014. http://www.ibm.com/smarterplanet/us/en/ibmwatson/discovery-advisor.html.

  33. Wei WQ, Cronin RM, Xu H, Lasko TA, Bastarache L, Denny JC. Development and evaluation of an ensemble resource linking medications to their indications. J Am Med Inform Assoc. 2013;20(5):954–61.

    Article  PubMed Central  PubMed  Google Scholar 

  34. Wishart DS, Knox C, Guo AC, Shrivastava S, Hassanali M, Stothard P, Chang Z, Woolsey J. DrugBank: a comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Res. 2006;34(Database issue):D668–72.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  35. Zhang Z, Gotz D, Perer A. Iterative cohort analysis and exploration. Journal of Information Visualization, March 19, 2014. doi: 10.1177/1473871614526077. http://ivi.sagepub.com/content/early/2014/03/19/1473871614526077.abstract

    Google Scholar 

  36. Zhang P, Wang F, Hu J, Sorrentino R. Towards personalized medicine: leveraging patient similarity and drug similarity analytics. Am Med Inform Assoc (AMIA) Jt Summit Transl Sci Transl Bioinforma (TBI). 2014;2014:132–6.

    Google Scholar 

  37. Zhou J, Wang F, Hu J, Ye J. From micro to macro: data driven phenotyping by densification of longitudinal electronic medical records Proceedings of the 20th ACM SIGKDD international conference on knowledge discovery and data mining. New York: ACM; 2014. p. 135–44.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Healthcare Analytics Research Group, IBM T.J. Watson Research Center, Yorktown Heights, NY, USA

    Jianying Hu PhD & Adam Perer PhD

  2. Computer Science and Engineering, University of Connecticut, Storrs, CT, USA

    Fei Wang PhD

Authors
  1. Jianying Hu PhD
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  2. Adam Perer PhD
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  3. Fei Wang PhD
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Corresponding author

Correspondence to Jianying Hu PhD .

Editor information

Editors and Affiliations

  1. Gentiva Health Services, Atlanta, Georgia, USA

    Charlotte A. Weaver

  2. Healthcare Informatics, IBM Research, Baltimore, Maryland, USA

    Marion J. Ball

  3. Division of Health Sciences Informatics, The Johns Hopkins University, Baltimore, Maryland, USA

    George R. Kim

  4. Duquesne University, Pittsburgh, Pennsylvania, USA

    Joan M. Kiel

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© 2016 Springer International Publishing Switzerland

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Hu, J., Perer, A., Wang, F. (2016). Data Driven Analytics for Personalized Healthcare. In: Weaver, C., Ball, M., Kim, G., Kiel, J. (eds) Healthcare Information Management Systems. Health Informatics. Springer, Cham. https://doi.org/10.1007/978-3-319-20765-0_31

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  • DOI: https://doi.org/10.1007/978-3-319-20765-0_31

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-20764-3

  • Online ISBN: 978-3-319-20765-0

  • eBook Packages: MedicineMedicine (R0)

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