Individualized patient-centered lifestyle recommendations: An expert system for communicating patient specific cardiovascular risk information and prioritizing lifestyle options

Authors:
Chih-Lin Chi;W. Nick Street;Jennifer G. Robinson;Matthew A. Crawford
Affiliations:
Center for Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA 02115, USA;Management Sciences Department and Interdisciplinary Graduate Program in Informatics, S210 Pappajohn Business Building, The University of Iowa, Iowa City, IA 52242, USA;Department of Epidemiology, University of Iowa, Lipid Research Clinic, University of Iowa, USA;Center for Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA 02115, USA
Venue:
Journal of Biomedical Informatics
Year:
2012

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Abstract

We propose a proof-of-concept machine-learning expert system that learned knowledge of lifestyle and the associated 10-year cardiovascular disease (CVD) risks from individual-level data (i.e., Atherosclerosis Risk in Communities Study, ARIC). The expert system prioritizes lifestyle options and identifies the one that maximally reduce an individual's 10-year CVD risk by (1) using the knowledge learned from the ARIC data and (2) communicating for patient-specific cardiovascular risk information and personal limitations and preferences (as defined by variables used in this study). As a result, the optimal lifestyle is not only prioritized based on an individual's characteristics but is also relevant to personal circumstances. We also explored probable uses and tested the system in several examples using real-world scenarios and patient preferences. For example, the system identifies the most effective lifestyle activities as the starting point for an individual's behavior change, shows different levels of BMI changes and the associated CVD risk reductions to encourage weight loss, identifies whether weight loss or smoking cessation is the most urgent change for a diabetes patient, etc. Answers to the questions noted above vary based on an individual's characteristics. Our validation results from clinical trial simulations, which compared original with the optimal lifestyle using an independent dataset, show that the optimal individualized patient-centered lifestyle consistently reduced 10-year CVD risks.