title: Prognostic data-driven clinical decision support - formulation and implications.
abstract: Existing Clinical Decision Support Systems (CDSSs) typically rely on rule-based algorithms and focus on tasks like guidelines adherence and drug prescribing and monitoring. However, the increasing dominance of Electronic Health Record technologies and personalized medicine suggest great potential for prognostic data-driven CDSS. A major goal for such systems would be to accurately predict the outcome of patients' candidate treatments by statistical analysis of the clinical data stored at a Health Care Organization. We formally define the concepts involved in the development of such a system, highlight an inherent difficulty arising from bias in treatment allocation, and propose a general strategy to address this difficulty. Experiments over hypertension clinical data demonstrate the validity of our approach.
authors: IBM Haifa Research Labs, 165 Aba Hushi st., Haifa 31905, Israel.
authors: Ruty
authors: R
authors: Rinott
authors: Boaz
authors: B
authors: Carmeli
authors: Carmel
authors: C
authors: Kent
authors: Daphna
authors: D
authors: Landau
authors: Yonatan
authors: Y
authors: Maman
authors: Yoav
authors: Y
authors: Rubin
authors: Noam
authors: N
authors: Slonim
mesh_terms: D000465:Algorithms; D003625:Data Collection; D003627:Data Interpretation, Statistical; D020000:Decision Support Systems, Clinical; D019983:Guideline Adherence; D006801:Humans; D006973:Hypertension / Q000175:diagnosis* / Q000628:therapy*; D008490:Medical Informatics / Q000639:trends; D016347:Medical Records Systems, Computerized; D017063:Outcome Assessment, Health Care; D057285:Precision Medicine / Q000295:instrumentation; D011379:Prognosis; D015203:Reproducibility of Results; D016896:Treatment Outcome