title: An ontology-based personalization of health-care knowledge to support clinical decisions for chronically ill patients.
abstract: Chronically ill patients are complex health care cases that require the coordinated interaction of multiple professionals. A correct intervention of these sort of patients entails the accurate analysis of the conditions of each concrete patient and the adaptation of evidence-based standard intervention plans to these conditions. There are some other clinical circumstances such as wrong diagnoses, unobserved comorbidities, missing information, unobserved related diseases or prevention, whose detection depends on the capacities of deduction of the professionals involved. In this paper, we introduce an ontology for the care of chronically ill patients and implement two personalization processes and a decision support tool. The first personalization process adapts the contents of the ontology to the particularities observed in the health-care record of a given concrete patient, automatically providing a personalized ontology containing only the clinical information that is relevant for health-care professionals to manage that patient. The second personalization process uses the personalized ontology of a patient to automatically transform intervention plans describing health-care general treatments into individual intervention plans. For comorbid patients, this process concludes with the semi-automatic integration of several individual plans into a single personalized plan. Finally, the ontology is also used as the knowledge base of a decision support tool that helps health-care professionals to detect anomalous circumstances such as wrong diagnoses, unobserved comorbidities, missing information, unobserved related diseases, or preventive actions. Seven health-care centers participating in the K4CARE project, together with the group SAGESA and the Local Health System in the town of Pollenza have served as the validation platform for these two processes and tool. Health-care professionals participating in the evaluation agree about the average quality 84% (5.9/7.0) and utility 90% (6.3/7.0) of the tools and also about the correct reasoning of the decision support tool, according to clinical standards.
authors.affiliation: Research Group on Artificial Intelligence, Universitat Rovira i Virgili, Tarragona, Spain.
authors.forename: David
authors.initials: D
authors.lastname: Riaño
authors.forename: Francis
authors.initials: F
authors.lastname: Real
authors.forename: Joan Albert
authors.initials: JA
authors.lastname: López-Vallverdú
authors.forename: Fabio
authors.initials: F
authors.lastname: Campana
authors.forename: Sara
authors.initials: S
authors.lastname: Ercolani
authors.forename: Patrizia
authors.initials: P
authors.lastname: Mecocci
authors.forename: Roberta
authors.initials: R
authors.lastname: Annicchiarico
authors.forename: Carlo
authors.initials: C
authors.lastname: Caltagirone
mesh_terms: D002908:Chronic Disease / Q000453:epidemiology*; D020000:Decision Support Systems, Clinical / Q000592:standards*; D003695:Delivery of Health Care / Q000706:statistics & numerical data; D006282:Health Personnel; D006801:Humans; D057285:Precision Medicine