MELLO: MEdical Life-Log Ontology
Date Submitted: Nov 24, 2014
Open Peer Review Period: Nov 25, 2014 - Jan 20, 2015
Background: 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. Objective: MEdical Life-Log 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. Methods: MELLO was developed using the General Formal Ontology method with a manual iterative process comprising five steps: (1) defining the scope of lifelog data, (2) identifying lifelog concepts, (3) assigning relationships among MELLO concepts, (4) developing MELLO properties (e.g., synonyms, preferred terms, and definitions) for each MELLO concept, and (5) evaluating representative layers of the ontology content. An evaluation was performed by classifying 11 devices into 3 classes by subjects, and performing pairwise comparisons of lifelog terms among 5 devices in each class as measured using the Jaccard similarity index. Results: MELLO represents a comprehensive knowledge base of 1,980 lifelog concepts, with 4,596 synonyms for 1,193 (61%) concepts and 1,395 definitions for 923 (48%) concepts. The Web-based MELLO Browser and MELLO Mapper provide convenient access and annotating non-standard proprietary terms with MELLO (http://www.snubi.org/software/mello). MELLO covers 88.1% of lifelog terms from 11 health self-tracking devices and uses simple string matching to match semantically similar terms provided by various devices that are not yet integrated. The results from the comparisons of Jaccard similarities between simple string matching and MELLO matching revealed increases of 2.5-fold for the physical activity class, 2.2-fold for the body measure class, and 5.7-fold for the sleep class. Conclusions: MELLO is the first ontology for representing health-related lifelog data with rich contents including definitions, synonyms, and semantic relationships. MELLO fills the semantic gaps among heterogeneous lifelog terms that are generated by diverse health self-tracking devices. The unified representation of lifelog terms facilitated by MELLO can help describe an individual’s lifestyle and environmental factors, which can be included with user-generated data for clinical research and thereby enhance data integration and sharing.