MELLO: Medical lifelog ontology for data terms from self-tracking and lifelog devices.

Journal: International journal of medical informatics
Published Date: Dec 1, 2015

Abstract

OBJECTIVE: The increasing use of health self-tracking devices is making the integration of heterogeneous data and shared decision-making more challenging. Computational analysis of lifelog data has been hampered by the lack of semantic and syntactic consistency among lifelog terms and related ontologies. Medical lifelog ontology (MELLO) was developed by identifying lifelog concepts and relationships between concepts, and it provides clear definitions by following ontology development methods. MELLO aims to support the classification and semantic mapping of lifelog data from diverse health self-tracking devices.

Authors

  • Hye Hyeon Kim
    Seoul National University Biomedical Informatics (SNUBI), Division of Biomedical Informatics, Seoul National University College of Medicine, Seoul 110799, South Korea.
  • Soo Youn Lee
    Seoul National University Biomedical Informatics (SNUBI), Division of Biomedical Informatics, Seoul National University College of Medicine, Seoul 110799, South Korea.
  • Su Youn Baik
    Seoul National University Biomedical Informatics (SNUBI), Division of Biomedical Informatics, Seoul National University College of Medicine, Seoul 110799, South Korea.
  • Ju Han Kim
    Department of Cardiovascular Medicine, Chonnam National University Hospital, Gwangju, Korea.

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