Colin G. Walsh, MD, MA, FAMIA

Assistant Professor
Department of Biomedical Informatics
Assistant Professor
Department of Medicine
Assistant Professor
Department of Psychiatry
2525 West End Avenue

Dr. Colin G. Walsh is an internist and clinical informatician who joined Vanderbilt University as Assistant Professor of Biomedical Informatics, Medicine, and Psychiatry in early 2015. His research is focused in predictive analytics applied to vulnerable populations, clinical workflow, and decision support at the point-of-care. 

His foci of research and operational work are: 1) machine learning/data science applied to use-cases in mental health; 2) utilization optimization and quality improvement; 3) scalable phenotyping to inform genetic analyses.

As Founder and Principal Investigator of the Health Analytics for Risk, Behavioral, and Operations Research (HARBOR) Lab, he is mentoring multiple trainees ranging from high school students to informatics PhD candidates to practicing clinical sub-specialists. 

After undergraduate training in mechanical engineering at Princeton University, Dr. Walsh attended medical school at the University of Chicago. He completed residency and chief residency in internal medicine at Columbia University Medical Center. He studied machine learning and data science in the domain of hospital readmission risk prediction at Columbia University under research mentor, Dr. George Hripcsak, in fellowship in Biomedical Informatics funded by the National Library of Medicine.

At Vanderbilt, he continues to develop clinically-grounded predictive models using data science approaches on structured and unstructured clinical data. Examples of active projects range from:

1) Machine learning + natural language processing approaches to predict and phenotype risks of suicidality 

2) Implementation science to design, deploy, and evaluate decision support driven by predictive modeling

3) Natural language processing to improve ascertainment and prediction of clinical problems from healthcare data


Research Information

ORCID: 0000-0002-9379-2056

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