Comparing ICD9-encoded diagnoses and NLP-processed discharge summaries for clinical trials pre-screening: a case study.
The prevalence of electronic medical record (EMR) systems has made mass-screening for clinical trials viable through secondary uses of clinical data, which often exist in both structured and free text formats. The tradeoffs of using information in either data format for clinical trials screening are understudied. This paper compares the results of clinical trial eligibility queries over ICD9-encoded diagnoses and NLP-processed textual discharge summaries. The strengths and weaknesses of both data sources are summarized along the following dimensions: information completeness, expressiveness, code granularity, and accuracy of temporal information. We conclude that NLP-processed patient reports supplement important information for eligibility screening and should be used in combination with structured data.
Li
Li
L
Department of Biomedical Informatics, Columbia University, New York, NY, USA.
Chase
Herbert S
HS
Patel
Chintan O
CO
Friedman
Carol
C
Weng
Chunhua
C
D000465:Algorithms; D001185:Artificial Intelligence; D002986:Clinical Trials as Topic / Q000379:methods*; D003933:Diagnosis; D016347:Medical Records Systems, Computerized; D009323:Natural Language Processing; D009518:New York; D010351:Patient Discharge; D018579:Patient Selection; D010363:Pattern Recognition, Automated / Q000379:methods*