Ramesh Sharda
[introductory/intermediate] Network-Based Health Analytics
Summary
This course will introduce network level properties and illustrate how such network measures can be used to help inform medical decision-making. It will illustrate how such measures can be computed, and then used in machine learning models to improve modeling performance.
Syllabus
- Introduction to network measures
- Applications of network metrics in health analytics – comorbidities
- Descriptive analytics in health demographics based upon comorbidities
- Network measures in health analytics modeling – incorporating comorbidities to predict hospital lengths of stay
- Clique modeling to determine identify diseases combinations that impact mortality
- Conclusions and future work
References
Pankush Kalgotra, Ramesh Sharda, and Sravanthi Parasa. “Quantifying Disease-Interactions through Co-occurrence Matrices to Predict Early Onset Colorectal Cancer”. Decision Support Systems (forthcoming).
Pankush Kalgotra and Ramesh Sharda. (2021). “When will I get out of the hospital? Modeling Length of Stay using Comorbidity Networks”. Journal of Management Information Systems. (38), 4, pp. 1150-1184. https://doi.org/10.1080/07421222.2021.1990618.
Pankush Kalgotra, Ramesh Sharda, and Julie M. Croff. (2020). “Examining multimorbidity differences across racial groups: a network analysis of electronic medical records”. Scientific Reports. (10), 13538.
Pankush Kalgotra, Ramesh Sharda, and Julie M Croff. (2017). “Examining Health Disparities by Gender: A Multimorbidity Network Analysis of Electronic Medical Record”. International Journal of Medical Informatics. (108), 22–28.
J. Loscalzo, A.-L. Barabási, E.K. Silverman (Eds.). (2017). Network Medicine: Complex Systems in Human Disease and Therapeutics. Harvard University Press.
Pre-requisites
None.
Short bio
Ramesh Sharda is the Vice Dean for Research and the Watson Graduate School of Management, Watson/ConocoPhillips Chair and a Regents Professor of Management Science and Information Systems in the Spears School of Business at Oklahoma State University. He has coauthored two textbooks (Analytics, Data Science, and Artificial Intelligence: Systems for Decision Support, 11th edition, Pearson; and Business Intelligence, Analytics, and Data Science: A Managerial Perspective, 4th edition, Pearson). His research has been published in major journals in management science and information systems including Management Science, Operations Research, Information Systems Research, Decision Support Systems, Interfaces, INFORMS Journal on Computing, and many others. He is a member of the editorial boards of journals such as Decision Support Systems, Decision Sciences, ACM Database, and Information Systems Frontiers. He served as the Executive Director of Teradata University Network through 2020 and was inducted into the Oklahoma Higher Education Hall of Fame in 2016. Ramesh is a Fellow of INFORMS and AIS. He was the winner of 2020 OSU Eminent Faculty Award. Ramesh also won the Fulbright Distinguished Chair Award at Aalto University in Finland for 2022-2023.
