Chih-Lin Chi, PhD, MBA

Dr. Chi is a biomedical informatician specializing in the creation and implementation of approaches, methods, and software tools designed to discover information and evidence from diverse medical data including electronic medical records, hospital discharge notes, longitudinal cohort studies, laboratory tests, and genetic data. He is particularly interested in understanding how a person's individual characteristics influence the outcome of multiple medical treatments; and how an individual, care team, and hospital network can select and implement the treatment decision that will maximize the overall healthcare outcome. Dr. Chi's background in machine learning, operations research, artificial intelligence, and statistics has prepared him to create sophisticated computational approaches to discover evidence and optimize the results of this type of personalized health management from high-dimensional medical data including personalized prevention, diagnosis, treatment, and prognosis.

To realize the personalized health management in clinical settings, the highly complex mathematical functions used to accurately model complicated medical events will be converted to decision support rules. These rules indicate which treatment option most improves outcomes for a particular type of individuals. The transparent property of rules further allows clinical validation by domain experts, in-depth clinical studies, and clinical trials. Dr. Chi's recent efforts also include using clinical trial simulations and genetic study to gain insight into such computational evidence and understand why a particular type of patients has the optimal outcome when receiving a certain treatment option.

Dr. Chi's agenda of the personalized health management studies includes four elements that complement each other: (1) developing translational research platform for the personalized health management starting from evidence discovery from medical data to clinical validation and implementation, (2) including omics data study aiming to strengthen such personalized health management evidence, (3) improving computational methods to incorporate realistic factors (such as factors that have been discussed in the past and ongoing projects: costs, compliance, disease progression, distance to clinics, and other clinical limitations) to support practical settings, and (4) applying abovementioned frameworks and approaches to multiple-center studies to improve robustness of the evidence. Instead of inventing new treatment that typically takes millions (if not, billions) of dollars and years of efforts, the personalized health management seeks to identify improved-outcome evidence from medical data and apply the evidence to support personalized care.

PhD, University of Iowa
Major: Machine Learning in Healthcare: Health Informatics

MBA, Feng Chia University
Major: Marketing and Management Information Systems

BS, National Chung-Hsing University
Major: Zoology

• Postdoctoral Fellowship, Harvard Medical School

• American Heart Association
• American Medical Informatics Association
• Institute for Operations Research and the Management Sciences

Xiong, J., Bhimani, R., McMahon, S., Chi, C-L., & Anderson, L. (2024). How do nurses assess cognition in adults with neurological conditions? A scoping review. Rehabilitation Nursing, 49(5), 169–182. https://doi.org/10.1097/RNJ.000000000000047 

Chi, N. C., Nguyen, K., Shanahan, A., Demir, I., Fu, Y. K., Chi, C-L., Perkhounkova, Y., Hein, M., Buckwalter, K., Wolf, M., Williams, K., & Herr, K. (2024). Usability testing of the PACE-app to support family caregivers in managing pain for people with dementia. The Gerontologist, 65(2). https://doi.org/10.1093/geront/gnae163 

Yew, P. Y., Loth, M., Adam, T. J., Wolfson, J., Liang, Y., Tonellato, P. J., & Chi, C-L. (2024). Potential impact of blood cholesterol guidelines on statin treatment in the U.S. population using interrupted time series analysis. BMC Cardiovascular Disorders, 24(1), 245. https://doi.org/10.1186/s12872-024-03921-z 

Yew, P. Y., Devera, R., Liang, Y., Khalifa. R. A. E., Sun, J., Chi, N. C., Chou, Y. C., Tonellato, P. J., & Chi, C-L. Unraveling the multiple chronic conditions patterns among people with Alzheimer's disease and related dementia: A machine learning approach to incorporate synergistic interactions. Alzheimer's & Dementia, 20(7), 4818–4827. https://doi.org/10.1002/alz.13923 

Sun, B., Yew, P. Y., Chi, C-L., Song, M., Loth, M., Liang, Y., Zhang, R., & Straka, R. (2024). Development and validation of the pharmacological statin-associated muscle symptoms risk stratification score using electronic health record data. Clinical Pharmacology & Therapeutics, 115(4), 839–846. https://doi.org/10.1002/cpt.3208 

Will, K., Liang, Y., Chi, C-L., Lamb, G., Todd, M. & Delaney, C. (2024). Measuring the impact of primary care team composition on patient activation utilizing Electronic Health Record big data analytics. Journal of Patient-Centered Research and Reviews, 11(1), 17-28. https://doi.org/10.17294/2330-0698.2019 

Yew, P. Y., Liang, Y., Adam, T. J., Wolfson J., Tonellato, P. J., & Chi, C-L. (2023). Decision rules for personalized statin treatment prescriptions over multi-objectives. Experimental Biology and Medicine, 248(24), 2526–2537. https://doi.org/10.1177/15353702231220660 

Sun, B., Yew, P. Y., Chi, C-L., Song, M., Loth, M., Zhang, R., & Straka, R. J. (2023). Development and application of pharmacological statin-associated muscle symptoms phenotyping algorithms using structured and unstructured Electronic Health Records data. JAMIA Open, 6(4). https://doi.org/10.1093/jamiaopen/ooad087 

Zhang, M., Zhu, L., Lin, S-Y., Herr, K., Chi, C-L., Demir, I., Lopez D. K., & Chi, N-C. (2023). Using artificial intelligence to improve pain assessment and pain management: A systematic review.  Journal of the American Medical Informatics Association, 30(3), 570–587. https://doi.org/10.1093/jamia/ocac231 

Liang, Y., Yew, P. Y., & Chi, C-L. (2023). Personalized statin treatment plan using counterfactual approach with multi-objective optimization over benefits and risks. Informatics in Medicine Unlocked 42, 101362. https://doi.org/10.1016/j.imu.2023.101362 

Khalifa R. A. E., Liang Y., Yew P. Y., Zhang W., & Chi C-L. "Comparative Analysis of Multimorbidity in Alzheimer's Disease and Related Dementia: A Focus on Statistical Differences and Association," American Medical Informatics Association 2025 Informatics Summit, Pittsburgh, PA. (March 2025).

Yew P. Y., & Chi C-L. "Identifying Individualized Multiple Chronic Condition (MCC) Patterns Associated with Dementia," Pacific Symposium on Biocomputing, Big Island, HI. (January 2025).

Liang, Y., Chi, C-L., & Yew P. Y. "Personalized Statin Treatment Plan Using Counterfactual Prediction and Optimization," INFORMS Annual Meeting, Phoenix, AZ. (October 2023).

Yew, P. Y., & Chi, C-L. "Decision Rules for Personalized Statin Treatment Prescriptions Over Multi-Objectives," INFORMS Healthcare Conference, Toronto, ON, Canada. (July 2023). 

Sun, B., Yew, P. Y., Loth M., Chi, C-L., Song, M., Straka, R. J., & Zhang R. "Optimizing the Identification and Prediction of Statin Intolerance to Improve Statin Adherence Using Natural Language Processing and Machine Learning," American Medical Informatics Association 2023 Informatics Summit, Seattle, WA. (March 2023).

Pradhan, P. M., Liang, Y.,  Yew, P. Y., Loth, M., Adam, T., Robinson, G. J., Tonellato, P. J., & Chi, C-L. "Does Comorbidity Matrix Provide Similar Amount of Predictive Information: Comparisons from Charlson and Elixhauser Using Deep Learning. IEEE 10th International Conference on Healthcare Informatics, Rochester, MN. (June 2022). 

Award: Personalized Statin Treatment Plan to Optimize Clinical Outcomes Using Big Data
Principal Investigator: Chi, Chih-Lin
Sponsoring Organization: NIH National Heart, Lunch, and Blood Institu
Award Dates: 2019 - 2025

Award: University of Minnesota Clinical and Translational Science Institute
Principal Investigator: Blazar, Bruce R
Sponsoring Organization: NIH National Center for Advancing Tran Science
Award Dates: 2018 - 2023

Award: Predictive Optimal Anticlotting Treatment for Segmented Patient Populations
Principal Investigator: Chi, Chih-Lin
Sponsoring Organization: University of Missouri
Award Dates: 2018 - 2018

Award: University of Pennsylvania+ PLUS Clinical Center (PENN+P
Principal Investigator: Wyman, Jean F
Sponsoring Organization: University of Pennsylvania
Award Dates: 2015 - 2020

Award: Predictive optimal anticlotting treatment for segmented
Principal Investigator: Chi, Chih-Lin
Sponsoring Organization: Harvard University
Award Dates: 2013 - 2017