ArticleKidney international2020
Natural language processing of electronic health records is superior to billing codes to identify symptom burden in hemodialysis patients.
Article in Kidney international, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 3 of them syntheses that pooled it.
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Who cites it
28 citing papers in PubMed, 3 syntheses or guidelines pooled it, 55 citations in OpenAlex.
- eHealth Solutions for Symptom Assessment and Monitoring in Adults With Chronic Kidney Disease: A Systematic Review.Journal of clinical nursing · 2025Pooled it
- Natural language processing with machine learning methods to analyze unstructured patient-reported outcomes derived from electronic health records: A systematic review.Artificial intelligence in medicine · 2023Pooled it
- Genetics in chronic kidney disease: conclusions from a Kidney Disease: Improving Global Outcomes (KDIGO) Controversies Conference.Kidney international · 2022Guideline
- Leveraging Natural Language Processing to Identify Variation in Kidney Transplant Access.Kidney international reports · 2026Article
- Augmenting structured diagnoses through effective use of pre-trained large language models on clinical notes.JAMIA open · 2026Article
- Natural Language Processing (NLP) tools are superior to ICD-10 codes in capturing clinically relevant information.Pediatric research · 2026Article
- Examining Transparency in Kidney Transplant Recipient Selection Criteria: Nationwide Cross-Sectional Study.JMIR AI · 2025Article
- A patient-centered approach to developing and validating a natural language processing model for extracting patient-reported symptoms.Scientific reports · 2025Article
- Estimating depression severity in narrative clinical notes using large language models.Journal of affective disorders · 2025Article
- Natural Language Processing Identifies Underdocumentation of Symptoms in Patients on Hemodialysis.Kidney360 · 2025Article
- Article
- Suicide Phenotyping from Clinical Notes in Safety-Net Psychiatric Hospital Using Multi-Label Classification with Pre-Trained Language Models.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2025Article
- Artificial Intelligence in Nephrology: Clinical Applications and Challenges.Kidney medicine · 2025Review
- Information heterogeneity between progress notes by physicians and nurses for inpatients with digestive system diseases.Scientific reports · 2024Article
- Performance and clinical utility of a new supervised machine-learning pipeline in detecting rare ciliopathy patients based on deep phenotyping from electronic health records and semantic similarity.Orphanet journal of rare diseases · 2024Article
- Hospitalization, case fatality, comorbidities, and isolated pathogens of adult inpatients with pneumonia from 2013 to 2022: a real-world study in Guangzhou, China.BMC infectious diseases · 2024Article
- Diagnostic rate estimation from Medicare records: Dependence on claim numbers and latent clinical features.Journal of biomedical informatics · 2023Article
- Positive Predictive Value ofJournal of the American Heart Association · 2023Article
- Clinical Characteristics and Management of Patients with Secondary Hyperparathyroidism Undergoing Hemodialysis: A Feasibility Analysis of Electronic Health Records Using Natural Language Processing.Kidney diseases (Basel, Switzerland) · 2023Article
- Natural Language Processing in Nephrology.Advances in chronic kidney disease · 2022Review
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15 authors at 2 institutions in 1 country.
Funding
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, 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.
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