MELLO: Medical Lifelog Ontology

1. Introduction

  • Integrating heterogeneous medical lifelogs from the ever-growing health self-tracking devices is difficult as each device has different data models and uses their own proprietary terminologies. Medical terminologies such as UMLS and SNOMED-CT are insufficient to cover the emerging terms and concepts of a variety of medical lifelog data.
  • MELLO is developed as the first medical lifelog ontology with rich contents supporting definitions, synonyms, and semantic relationships through the process of systematic collection and medical expert review.
  • The unified representation of diverse medical lifelog terms enabled by MELLO may improve clinical research and data sharing by integrating personal big data including lifestyle, environmental factors, and user-generated data.

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

    Hye Hyeon Kim, Soo Youn Lee, Su Youn Baik, and Ju Han Kim, Int. J. of Med. Inform. 84 (2015) 1099-1110

    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.  
    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 1,998 lifelog concepts, with 4,996 synonyms for 1,211 (61%) concepts and 1,395 definitions for 926 (46%) concepts. The Web-based MELLO Browser and MELLO Mapper provide convenient access and annotating non-standard proprietary terms with MELLO (  
    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 lifestyle of an individual and environmental factors, which can be included with user-generated data for clinical research and thereby enhance data integration and sharing.


    2. MELLO Statistics

    MELLO Concept Type First-level Term NamesNo. of MELLO Perferred TermsNo. of MELLO TermsMaximum Depth
    MELLO Primary Terms Behavior 1153357
    Body measurement 135 3649
    Exercise 1902678
    Nutrition 14 943
    Symptom 133 910 6
    Therapy 63 2225
    Total No. of MELLO Primary Terms 659 2,2709
    MELLO Secondary Terms Body region 73296 4
    Lifelog apps & devices 50 472
    Time patterns 435 1106
    Units 7812273 2
    Total No. of MELLO Secondary Terms 1,3392,7266
    Total 1,9984,996 9


    3. Information Source Map

    3.1 Composition of each source data in MELLO


    3.2 Venn diagrams indicating how MELLO concepts were derived from various sources

    (A) MELLO primary terms of MELLO (n=659) are primarily derived from SNOMED-CT (n=517), HVV (n=216), and proprietary lifelog terms (n=325). Only 0.16% of SNOMED-CT terms are related to lifelog terms.

    (B) Lifelog qualifier terms of MELLO (n=1,339) are primarily extracted from UMLS (n=1,243), including SNOMED-CT terms, HVV (n=217), and proprietary lifelog terms (n=1,266). Only 0.06% of UMLS terms are related to lifelog terms.