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

• Published in: Journal of biomedical informatics 2012-12, Vol.45 (6), p.1164-1174
• Main Authors: Chi, Chih-Lin, Nick Street, W., Robinson, Jennifer G., Crawford, Matthew A.
• Format: Article
• Language: English
• Subjects: Artificial Intelligence; Cardiovascular Diseases - etiology; Cardiovascular Diseases - prevention & control; Cardiovascular Diseases - psychology; Decision support systems; Decision Support Systems, Clinical; Humans; Individualized lifestyle recommendation; Life Style; Machine learning; Optimization; Patient Acceptance of Health Care; Patient centered medicine; Patient Education as Topic; Patient-Centered Care; Risk Factors; Risk Reduction Behavior
• ISSN: 1532-0464; 1532-0480
• DOI: 10.1016/j.jbi.2012.07.011

[Display omitted] ► The expert system is constructed by supervised learning approach and optimization. ► The system predicts 10-year CVD risks based on cardiovascular risk information. ► The lifestyle option with the lowest CVD risk varies based on individuals. ► The system prioritizes lifestyle options based risks, limitations, and preferences. 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.