Natural language processing of electronic health records is superior to billing codes to identify symptom burden in hemodialysis patients.

ABSTRACT: Symptoms are common in patients on maintenance hemodialysis but identification is challenging. New informatics approaches including natural language processing (NLP) can be utilized to identify symptoms from narrative clinical documentation. Here we utilized NLP to identify seven patient symptoms from notes of maintenance hemodialysis patients of the BioMe Biobank and validated our findings using a separate cohort and the MIMIC-III database. NLP performance was compared for symptom detection with International Classification of Diseases (ICD)-9/10 codes and the performance of both methods were validated against manual chart review. From 1034 and 519 hemodialysis patients within BioMe and MIMIC-III databases, respectively, the most frequently identified symptoms by NLP were fatigue, pain, a

SUBMITTER: Chan L 

PROVIDER: S-EPMC7001114 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature