Knowledge guided integration of structured and unstructured data in health decision process
M Radaoui, S Ben Abdallah Ben Lamine… - 2019 - aisel.aisnet.org
M Radaoui, S Ben Abdallah Ben Lamine, H Baazaoui Zghal, C Ghedira Guegan, N Kabachi
2019•aisel.aisnet.orgData in the health domain is continuously increasing. It is collected from several sources,
has several formats and is characterized by its sensibility (protection of personal health
data). These characteristics make the management and the expert interaction with the
collected data, in order to facilitate decision-making in Health Information Systems (HIS) a
challenging field. In this paper, we propose a Knowledge guided integration of structured
and unstructured data for health decision process. The knowledge is represented by domain …
has several formats and is characterized by its sensibility (protection of personal health
data). These characteristics make the management and the expert interaction with the
collected data, in order to facilitate decision-making in Health Information Systems (HIS) a
challenging field. In this paper, we propose a Knowledge guided integration of structured
and unstructured data for health decision process. The knowledge is represented by domain …
Abstract
Data in the health domain is continuously increasing. It is collected from several sources, has several formats and is characterized by its sensibility (protection of personal health data). These characteristics make the management and the expert interaction with the collected data, in order to facilitate decision-making in Health Information Systems (HIS) a challenging field. In this paper, we propose a Knowledge guided integration of structured and unstructured data for health decision process. The knowledge is represented by domain ontology, which allows the integration of structured and unstructured data, stored in NoSQL format. Our motivation is to combine the confirmed advantages of ontologies and NoSQL databases both in data integration and decision aided processes. The proposed ontology has been implemented and evaluated using quality metrics. The approach was evaluated and results show response time optimization, compared with traditional approaches, and improvement of data relevance.
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