An ontology-based personalization of health-care knowledge to support clinical decisions for chronically ill patients.

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.

[Artificial intelligence in medicine: project of a mobile platform in an intelligent environment for the care of disabled and elderly people]. / Intelligenza artificiale in medicina: progetto di una piattaforma mobile inserita in un ambiente intelligente per l'assistenza ai disabili e agli anziani.

A project based on the integration of new technologies and artificial intelligence to develop a device--e-tool--for disabled patients and elderly people is presented. A mobile platform in intelligent environments (skilled-care facilities and home-care), controlled and managed by a multi-level architecture, is proposed to support patients and caregivers to increase self-dependency in activities of daily living.

Qualitative profiles of disability.

This study identified profiles of functional disability (FD) paralleled by increasing levels of disability. We assessed 96 subjects using the World Health Organization Disability Assessment Schedule II (WHODAS II). Clustering Based on Rules (ClBR) (a hybrid technique of Statistics and Artificial Intelligence) was used in the analysis. Four groups of subjects with different profiles of FD were ordered according to an increasing degree of disability: "Low," self-dependent subjects with no physical or emotional problems; "Intermediate I," subjects with low or moderate physical and emotional disability, with high perception of disability; "Intermediate II," subjects with moderate or severe disability concerning only physical problems related to self-dependency, without emotional problems; and "High," subjects with the highest degree of disability, both physical and emotional. The order of the four classes is paralleled by a significant difference (<0.001) in the WHODAS II standardized global score. In this paper, a new ontology for the knowledge of FD, based on the use of ClBR, is proposed. The definition of four classes, qualitatively different and with an increasing degree of FD, helps to appropriately place each patient in a group of individuals with a similar profile of disability and to propose standardized treatments for these groups.