Knowledge Driven Phenotyping

Honghan Wu, Minhong Wang, Qianyi Zeng, Wenjun Chen, Thomas Nind, Emily Jefferson, Marion Bennie, Corri Black, Jeff Z Pan, Cathie Sudlow, Dave Robertson

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)
24 Downloads (Pure)


Extracting patient phenotypes from routinely collected health data (such as Electronic Health Records) requires translating clinically-sound phenotype definitions into queries/computations executable on the underlying data sources by clinical researchers. This requires significant knowledge and skills to deal with heterogeneous and often imperfect data. Translations are time-consuming, error-prone and, most importantly, hard to share and reproduce across different settings. This paper proposes a knowledge driven framework that (1) decouples the specification of phenotype semantics from underlying data sources; (2) can automatically populate and conduct phenotype computations on heterogeneous data spaces. We report preliminary results of deploying this framework on five Scottish health datasets.

Original languageEnglish
Pages (from-to)1327-1328
Number of pages2
JournalStudies in Health Technology and Informatics
Publication statusPublished - 16 Jun 2020


  • Electronic Health Records
  • Information Storage and Retrieval
  • Semantics


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