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Data-Driven Disease Progression Modeling

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Healthcare Information Management Systems

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

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Abstract

This chapter provides a comprehensive overview to data driven disease progression modeling techniques. It adopts a broad approach to disease progression, focusing on all computational methods able to model any temporal aspects of disease progression. Consequently, we have focused on three classes of analysis: staging and trajectory estimation analysis to better understand the course of a disease, predictive classification analysis for important disease related event prediction, and time to event analysis with survival models to estimate when clinically significant events are expected to occur during the progression of a disease. We describe the state of the art in each of these classes, together with discussions on challenges and opportunities for additional research.

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Authors and Affiliations

  1. IBM Research, International Business Machines Corporation, Cambridge, MA, USA

    Kenney Ng

  2. IBM Research, International Business Machines Corporation, Yorktown Heights, NY, USA

    Mohamed Ghalwash, Prithwish Chakraborty & Daby M. Sow

  3. IBM Research, International Business Machines Corporation, Tokyo, Japan

    Akira Koseki, Hiroki Yanagisawa & Michiharu Kudo

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  1. Kenney Ng
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  2. Mohamed Ghalwash
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  3. Prithwish Chakraborty
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Correspondence to Kenney Ng .

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Editors and Affiliations

  1. Duquesne University, Pittsburgh, PA, USA

    Joan M. Kiel

  2. The Johns Hopkins University, Baltimore, MD, USA

    George R. Kim

  3. The University of Texas at Arlington, Arlington, TX, USA

    Marion J. Ball

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Ng, K. et al. (2022). Data-Driven Disease Progression Modeling. In: Kiel, J.M., Kim, G.R., Ball, M.J. (eds) Healthcare Information Management Systems. Health Informatics. Springer, Cham. https://doi.org/10.1007/978-3-031-07912-2_17

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