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

Details

• Alternative Title:
  Kidney Int
• Personal Author:
  Chan, Lili ; Beers, Kelly ; Yau, Amy ; Chauhan, Kinsuk ; Duffy, Aine ; Chaudhary, Kumardeep ; Debnath, Neha ; Saha, Aparna ; Pattharanitima, Pattharawin ; Cho, Judy ; Kotanko, Peter ; Federman, Alex ; Coca, Steven ; Van Vleck, Tielman ; Nadkarni, Girish N.
• Description:
  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, and nausea/vomiting. In BioMe, sensitivity for NLP (0.85 - 0.99) was higher than for ICD codes (0.09 - 0.59) for all symptoms with similar results in the BioMe validation cohort and MIMIC-III. ICD codes were significantly more specific for nausea/vomiting in BioMe and more specific for fatigue, depression, and pain in the MIMIC-III database. A majority of patients in both cohorts had four or more symptoms. Patients with more symptoms identified by NLP, ICD, and chart review had more clinical encounters. NLP had higher specificity in inpatient notes but higher sensitivity in outpatient notes and performed similarly across pain severity subgroups. Thus, NLP had higher sensitivity compared to ICD codes for identification of seven common hemodialysis-related symptoms, with comparable specificity between the two methods. Hence, NLP may be useful for the high-throughput identification of patient-centered outcomes when using electronic health records.
• Subjects:
  Algorithms Databases, Factual Electronic Health Records Humans Natural Language Processing Renal Dialysis
• Keywords:
  Geriatric Nephrology Hemodialysis
• Source:
  Kidney Int. 97(2):383-392
• Pubmed ID:
  31883805
• Pubmed Central ID:
  PMC7001114
• Document Type:
  Journal Article
• Funding:
  L30 DK118724/DK/NIDDK NIH HHSUnited States/ ; R01 HL085757/HL/NHLBI NIH HHSUnited States/ ; U01 HG009610/HG/NHGRI NIH HHSUnited States/ ; U01 DK106962/DK/NIDDK NIH HHSUnited States/ ; U01 HG007278/HG/NHGRI NIH HHSUnited States/ ; R01 DK108803/DK/NIDDK NIH HHSUnited States/ ; T32 DK007757/DK/NIDDK NIH HHSUnited States/ ; U01OH011326/ACL/ACL HHSUnited States/ ; U01 OH011326/OH/NIOSH CDC HHSUnited States/ ; U01 DK116100/DK/NIDDK NIH HHSUnited States/ ; R01 DK112258/DK/NIDDK NIH HHSUnited States/ ; K23 DK107908/DK/NIDDK NIH HHSUnited States/
• Volume:
  97
• Issue:
  2
• Collection(s):
  CDC Public Access
• Main Document Checksum:
  urn:sha256:ba426174e25045f5ea9b0adc9e06c992eb310260004c917bafe2b0779fb8cd5c
• Download URL:
  https://stacks.cdc.gov/view/cdc/84937/cdc_84937_DS1.pdf
• File Type:
  [PDF - 887.47 KB ]