Design and development of medical recommendation system for home care service for geriatrics


Tezin Türü: Yüksek Lisans

Tezin Yürütüldüğü Kurum: Orta Doğu Teknik Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü, Türkiye

Tezin Onay Tarihi: 2015

Öğrenci: SALİHA İREM BEŞİK

Danışman: FERDA NUR ALPASLAN

Özet:

Demands and expectations for health care have gradually increased with the longer life expectancy and decline in birth rate, however the resources reserved for health services are relatively limited. The countries with aging population problems are trying to develop new systems to obtain more effective usage of current resources. The aging population and resultant chronic illnesses has become a real problem for Turkey as well. The increase in elderly population results in more demand for health care because of aging-associated physical or mental limitations and chronic illnesses. Research illustrates that home care services for seniors speed up the healing process. The aim of the thesis is developing a medical recommendation system (RHCS) which generates treatment and care plan recommendations to assist health professionals to make decisions on treatment process of geriatrics. This developed recommendation system will be a part of an integrated patient based e-health platform which provides a home health care for those elderly people who need care, including all of the actors (particularly relatives of elderly people) involved in the nursing period. One of the distinctive points of this study lies in the methodology used which is empowering collaborative filtering recommendation approach with historical data of geriatric patients. Its ontological-based approach, electronic health record structure, compatibility with ICD-10 and ATC clinical classification systems also makes this study prominent. RHCS has evaluated by both offline experiments with historical patient data taken by Ankara Numune Hospital and user studies conducted with 13 doctors. The results are measured by three different types of evaluation metrics, and it is showed that in each case RHCS is a successful system to generate reliable and relevant recommendations. As a future work, RHCS will be adapted to integrate with a rule-based clinical decision support system.