Insight extraction from e-Health bookings by means of Hypergraph and Machine Learning

• Published in: IEEE journal of biomedical and health informatics Vol. 27; no. 10; pp. 1 - 12
• Main Authors: Cola, Vincenzo Schiano di, Chiaro, Diletta, Prezioso, Edoardo, Izzo, Stefano, Giampaolo, Fabio
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.10.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Big Data; Codes; Data mining; Diseases; e-Healthcare services; Electronic Medical Records; Embedding; Enterprise Knowledge Graph; Health care; Health services; Information retrieval; Knowledge representation; Learning algorithms; Machine learning; Medical diagnosis; Medical diagnostic imaging; Medical services; New technology; Object oriented modeling; Patients; Prediction algorithms; Subnational government; Vector spaces
• ISSN: 2168-2194; 2168-2208; 2168-2208
• DOI: 10.1109/JBHI.2022.3233498

New technologies are transforming medicine, and this revolution starts with data. Usually, health services within public healthcare systems are accessed through a booking centre managed by local health authorities and controlled by the regional government. In this perspective, structuring e-health data through a Knowledge Graph (KG) approach can provide a feasible method to quickly and simply organize data and/or retrieve new information. Starting from raw health bookings data from the public healthcare system in Italy, a KG method is presented to support e-health services through the extraction of medical knowledge and novel insights. By exploiting graph embedding which arranges the various attributes of the entities into the same vector space, we are able to apply Machine Learning (ML) techniques to the embedded vectors. The findings suggest that KGs could be used to assess patients' medical booking patterns, either from unsupervised or supervised ML. In particular, the former can determine possible presence of hidden groups of entities that is not immediately available through the original legacy dataset structure. The latter, although the performance of the used algorithms is not very high, shows encouraging results in predicting a patient's likelihood to undergo a particular medical visit within a year. However, many technological advances remain to be made, especially in graph database technologies and graph embedding algorithms.