GDPR and FAIR compliant decision support system design for triage and disease detection

Date

2023-05-14

Author

KARAMANLIOĞLU, ALPER
Alpaslan, Ferda Nur
Sunar, Elif Tansu
Cetin, Cihan
Akca, Gülsüm
Merdanoglu, Hakan
Dogan, Osman Tufan

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In this study, a novel decision support system design is proposed that addresses triage and disease detection, and automatically makes predictions on structural and semi- structural clinical data. The proposed system consists of a hybrid design that uses ontology-driven and machine learning based methods together while performing the dis- ease prediction and triage processes. PUBMED citation records and well-known biomedical ontologies were used as source of information to effectively determine disease- symptom relationships. Since patient data are sensitive and require responsibility, the solution to be developed to comply with certain criteria and principles. In order to achieve this, data is obtained and stored in accordance with the General Data Protection Regulation and FAIR Data Principles.

Subject Keywords

Clinical decision support, Data anonymization, Disease prediction, Expert system, FAIR data principles, GDPR-compliance, Ontology integration

URI

https://doi.org/10.1007/978-3-031-28332-1
https://hdl.handle.net/11511/103422

DOI

https://doi.org/10.1007/978-3-031-28332-1

Conference Name

20th Int’l Conf. on Information Technology- New Generations (ITNG 2023)

Collections

Department of Computer Engineering, Conference / Seminar

Citation Formats

A. KARAMANLIOĞLU et al., “GDPR and FAIR compliant decision support system design for triage and disease detection,” presented at the 20th Int’l Conf. on Information Technology- New Generations (ITNG 2023), Amerika Birleşik Devletleri, 2023, Accessed: 00, 2023. [Online]. Available: https://doi.org/10.1007/978-3-031-28332-1.