Chih-Lin Chi, PhD, MBA

I am 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. I am 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. My background in machine learning, operations research, artificial intelligence, and statistics has prepared me 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. My 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.

My 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.

Award: Personalized Statin Treatment Plan to Optimize Clinical
Principal Investigator: Chi, Chih-Lin
Sponsoring Organization: NIH National Heart, Lunch, and Blood Institu
Award Dates: 2019 - 2023

Award: University of Minnesota Clinical and Translational Scien
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
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

• Big data research, machine learning, and knowledge discovery
• Translational research and clinical trial simulation for personalized health management
• Develop innovative nursing informatics methods to discover a proactive treatment strategy and minimize statin intolerance