A method for probing disease relatedness using common clinical eligibility criteria.
Clinical trial eligibility criteria define fine-grained characteristics of research volunteers for various disease trials and hence are a promising data source for disease profiling. This paper explores the feasibility of using disease-specific common eligibility features (CEFs) for representing diseases and understanding their relatedness. We extracted disease-specific CEFs from eligibility criteria on ClinicalTrials.gov for three illustrative categories - cancers, mental disorders and chronic diseases - each including seven diseases. We then constructed disease-specific CEF networks to assess the degree of overlap among the diseases. Using these automatically derived networks, we observed several findings that were confirmed in medicine. For example, we highlighted connections among schizophrenia, epilepsy and depression. We also identified a link between Crohn's disease and arthritis. These observations confirm the value of using clinical trial eligibility criteria for identifying disease relatedness. We further discuss the implications of CEFs for standardizing clinical trial eligibility criteria through reuse.
Department of Biomedical Informatics, Columbia University, New York, NY, USA.
Mary Regina
MR
Boland
Riccardo
R
Miotto
Chunhua
C
Weng
D057225:Data Mining / Q000379:methods*; D004194:Disease / Q000145:classification*; D057286:Electronic Health Records / Q000145:classification / Q000706:statistics & numerical data*; D004608:Eligibility Determination / Q000379:methods*; D006801:Humans; D018579:Patient Selection; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data*; D012042:Registries / Q000706:statistics & numerical data*; D014481:United States