title: Learning causal and predictive clinical practice guidelines from data.
abstract: 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.
authors.affiliation: Department of Biomedical Informatics, Vanderbilt University, Nashville TN 37232, USA. subramani.mani@vanderbilt.edu
authors.forename: Subramani
authors.initials: S
authors.lastname: Mani
authors.forename: Constantin
authors.initials: C
authors.lastname: Aliferis
authors.forename: Shanthi
authors.initials: S
authors.lastname: Krishnaswami
authors.forename: Theodore
authors.initials: T
authors.lastname: Kotchen
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D006801:Humans; D006973:Hypertension; D017410:Practice Guidelines as Topic