Natural Language Processing of Electronic Health Records Is Superior to Billing Codes to Identify Symptom Burden in Hemodialysis Patients

Details

• Personal Author:
  Beers K ; Chan L ; Chaudhary K ; Chauhan K ; Cho J ; Coca SG ; Debnath N ; Duffy Á ; Federman A ; Kotanko P ; Nadkarni GN ; Pattharanitima P ; Saha A ; Van Vleck T ; Yau AA
• 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. [Description provided by NIOSH]
• Subjects:
  Delivery Of Health Care Occupational Health Safety
• Keywords:
  Author Keywords: Geriatric Nephrology Electronic Health Records Hemodialysis Kidney Disorders Medical Care Medical Records Natural Language Processing Patient-centered Outcomes Symptoms
• ISSN:
  0085-2538
• Document Type:
  Text
• Funding:
  Cooperative Agreement
• Genre:
  Journal Article
• Place as Subject:
  New York ; OSHA Region 2
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Pages in Document:
  383-392
• Volume:
  97
• Issue:
  2
• NIOSHTIC Number:
  nn:20059139
• Citation:
  Kidney Int 2020 Feb; 97(2):383-392
• Contact Point Address:
  Lili Chan or Girish N. Nadkarni, Icahn School of Medicine at Mount Sinai, One Gustave L Levy Place, Box 1243, New York, New York 10029, USA
• Email:
  lili.chan@mountsinai.org
• Federal Fiscal Year:
  2020
• Performing Organization:
  Icahn School of Medicine at Mount Sinai, New York
• Peer Reviewed:
  True
• Start Date:
  20170701
• Source Full Name:
  Kidney International
• End Date:
  20200630
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:1edca5368f69ff5e75e55be3e9f0bbdf7c20291e66b377bdad84fd3ff21a85517cb140b95a5389715be6f8caf66fd124c17c72d387949a608851b8f0e646d0af
• Download URL:
  https://stacks.cdc.gov/view/cdc/223449/cdc_223449_DS1.pdf
• File Type:
  [PDF - 1.43 MB ]