ArticleBMC medical informatics and decision making2024
Characterizing the limitations of using diagnosis codes in the context of machine learning for healthcare.
Article in BMC medical informatics and decision making, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Representational Veracity in Data Science Health Research: Targets, Proxies, Labels, and Descriptors.Journal of medical Internet research · 2026Article
- Electroencephalogram Monitoring in Critical Care: Multicenter Analysis of Timing, Duration, and Readmissions.Critical care explorations · 2026Article
- Identifying Hemophagocytic Lymphohistiocytosis and Describing Outcomes Using Computable Phenotypes: Retrospective Cohort Study.JMIR cancer · 2026Article
- Challenges and solutions in determining urolithiasis caseloads using the digital infrastructure of a clinical data warehouse.PloS one · 2026Article
- Development and prospective evaluation of a machine learning model to predict vomiting among pediatric cancer and hematopoietic cell transplant patients.BMC cancer · 2025Article
- Feasibility of machine learning analysis for the identification of patients with possible primary ciliary dyskinesia.Orphanet journal of rare diseases · 2025Article
- A self-supervised framework for laboratory data imputation in electronic health records.Communications medicine · 2025Article
- Detecting and Remediating Harmful Data Shifts for the Responsible Deployment of Clinical AI Models.JAMA network open · 2025Article
- Feasibility of Machine Learning Analysis for the Identification of Patients with Possible Primary Ciliary Dyskinesia.medRxiv : the preprint server for health sciences · 2025Article
- Distilling the knowledge from large-language model for health event prediction.Scientific reports · 2024Article
- A multi-center study on the adaptability of a shared foundation model for electronic health records.NPJ digital medicine · 2024Article
- Self-supervised machine learning using adult inpatient data produces effective models for pediatric clinical prediction tasks.Journal of the American Medical Informatics Association : JAMIA · 2023Article
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11 authors.
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Abstract
backgroundDiagnostic codes are commonly used as inputs for clinical prediction models, to create labels for prediction tasks, and to identify cohorts for multicenter network studies. However, the coverage rates of diagnostic codes and their variability across institutions are underexplored. The primary objective was to describe lab- and diagnosis-based labels for 7 selected outcomes at three institutions. Secondary objectives were to describe agreement, sensitivity, and specificity of diagnosis-based labels against lab-based labels.
methodsThis study included three cohorts: SickKids from The Hospital for Sick Children, and StanfordPeds and StanfordAdults from Stanford Medicine. We included seven clinical outcomes with lab-based definitions: acute kidney injury, hyperkalemia, hypoglycemia, hyponatremia, anemia, neutropenia and thrombocytopenia. For each outcome, we created four lab-based labels (abnormal, mild, moderate and severe) based on test result and one diagnosis-based label. Proportion of admissions with a positive label were presented for each outcome stratified by cohort. Using lab-based labels as the gold standard, agreement using Cohen's Kappa, sensitivity and specificity were calculated for each lab-based severity level.
resultsThe number of admissions included were: SickKids (n = 59,298), StanfordPeds (n = 24,639) and StanfordAdults (n = 159,985). The proportion of admissions with a positive diagnosis-based label was significantly higher for StanfordPeds compared to SickKids across all outcomes, with odds ratio (99.9% confidence interval) for abnormal diagnosis-based label ranging from 2.2 (1.7-2.7) for neutropenia to 18.4 (10.1-33.4) for hyperkalemia. Lab-based labels were more similar by institution. When using lab-based labels as the gold standard, Cohen's Kappa and sensitivity were lower at SickKids for all severity levels compared to StanfordPeds.
conclusionsAcross multiple outcomes, diagnosis codes were consistently different between the two pediatric institutions. This difference was not explained by differences in test results. These results may have implications for machine learning model development and deployment.
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