TY - JOUR
T1 - Individualized patient-centered lifestyle recommendations
T2 - An expert system for communicating patient specific cardiovascular risk information and prioritizing lifestyle options
AU - Chi, Chih Lin
AU - Nick Street, W.
AU - Robinson, Jennifer G.
AU - Crawford, Matthew A.
PY - 2012/12
Y1 - 2012/12
N2 - 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.
AB - 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.
KW - Decision support systems
KW - Individualized lifestyle recommendation
KW - K
KW - Machine learning
KW - Optimization
KW - Patient centered medicine
UR - http://www.scopus.com/inward/record.url?scp=84869868749&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84869868749&partnerID=8YFLogxK
U2 - 10.1016/j.jbi.2012.07.011
DO - 10.1016/j.jbi.2012.07.011
M3 - Article
C2 - 22903051
AN - SCOPUS:84869868749
SN - 1532-0464
VL - 45
SP - 1164
EP - 1174
JO - Journal of Biomedical Informatics
JF - Journal of Biomedical Informatics
IS - 6
ER -