Learning causal and predictive clinical practice guidelines from data.
Clinical practice guidelines (CPG) propose preventive, diagnostic and treatment strategies based on the best available evidence. CPG enable practice of evidencebased medicine and bring about standardization of healthcare delivery in a given hospital, region, country or the whole world. This study explores generation of guidelines from data using machine learning, causal discovery methods and the domain of high blood pressure as an example.
Mani
Subramani
S
Department of Biomedical Informatics, Vanderbilt University, Nashville TN 37232, USA. subramani.mani@vanderbilt.edu
Aliferis
Constantin
C
Krishnaswami
Shanthi
S
Kotchen
Theodore
T
D000465:Algorithms; D001185:Artificial Intelligence; D006801:Humans; D006973:Hypertension; D017410:Practice Guidelines as Topic