• Lili Chan, Kelly Beers, Amy A Yau, Kinsuk Chauhan, Áine Duffy, Kumardeep Chaudhary, Neha Debnath, Aparna Saha, Pattharawin Pattharanitima, Judy Cho, Peter Kotanko, Alex Federman, Steven G Coca, Tielman Van Vleck, and Girish N Nadkarni.
• Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The Charles Bronfman Institute for Personalized Medicine, Department of Genetics and Genomics Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA. Electronic address: lili.chan@mountsinai.org.
• Kidney Int. 2020 Feb 1; 97 (2): 383-392.

AbstractSymptoms 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.Copyright © 2019 International Society of Nephrology. Published by Elsevier Inc. All rights reserved.

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