ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2019
Deep Learning on Electronic Health Records to Improve Disease Coding Accuracy.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
15 citing papers in PubMed.
- Hemodialysis equipment malfunction incidence and risk profiling in clinical settings: a retrospective single-center cohort analysis.Biomedical engineering online · 2025Article
- Identifying and Characterising a Chronic Kidney Disease Electronic-Phenotype Using Electronic Health Record-Derived Data: A Narrative Review of Strategies and Applications.Nephrology (Carlton, Vic.) · 2025Review
- Application of Machine Learning in Chronic Kidney Disease: Current Status and Future Prospects.Biomedicines · 2024Review
- SCOPE: predicting future diagnoses in office visits using electronic health records.Scientific reports · 2023Article
- Applying Deep Learning Model to Predict Diagnosis Code of Medical Records.Diagnostics (Basel, Switzerland) · 2023Article
- Identifying risk of opioid use disorder for patients taking opioid medications with deep learning.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Deep learning in systems medicine.Briefings in bioinformatics · 2021Review
- Prevalence of Missing Data in the National Cancer Database and Association With Overall Survival.JAMA network open · 2021Article
- Detecting Miscoded Diabetes Diagnosis Codes in Electronic Health Records for Quality Improvement: Temporal Deep Learning Approach.JMIR medical informatics · 2020Article
- Applying Convolutional Neural Networks to Predict the ICD-9 Codes of Medical Records.Sensors (Basel, Switzerland) · 2020Article
- Hospitalizations and mortality among patients with fetal alcohol spectrum disorders: a prospective study.Scientific reports · 2020Article
- Optimized Identification of Advanced Chronic Kidney Disease and Absence of Kidney Disease by Combining Different Electronic Health Data Resources and by Applying Machine Learning Strategies.Journal of clinical medicine · 2020Article
- Role of Artificial Intelligence in Kidney Disease.International journal of medical sciences · 2020Review
- Machine Learning Based Opioid Overdose Prediction Using Electronic Health Records.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2019Article
- Evaluating the effect of an in-service training workshop on ICD-10 coding instructions of pregnancy, childbirth and the puerperium for clinical coders.Journal of medicine and lifeArticle
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Characterization of a patient's clinical phenotype is central to biomedical informatics. ICD codes, assigned to inpatient encounters by coders, is important for population health and cohort discovery when clinical information is limited. While ICD codes are assigned to patients by professionals trained and certified in coding there is substantial variability in coding. We present a methodology that uses deep learning methods to model coder decision making and that predicts ICD codes. Our approach predicts codes based on demographics, lab results, and medications, as well as codes from previous encounters. We are able to predict existing codes with high accuracy for all three of the test cases we investigated: diabetes, acute renal failure, and chronic kidney disease. We employed a panel of clinicians, in a blinded manner, to assess ground truth and compared the predictions of coders, model and clinicians. When disparities between the model prediction and coder assigned codes were reviewed, our model outperformed coder assigned ICD codes.
Identifiers
31259017PMC6568065What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.