ArticleBioinformatics (Oxford, England)2020
Polar labeling: silver standard algorithm for training disease classifiers.
Article in Bioinformatics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Automatic identification of diagnosis from hospital discharge letters via weakly supervised Natural Language Processing.Scientific reports · 2026Article
- A deep learning derived prostate zonal volume-based biomarker from T2-weighted MRI to distinguish between prostate cancer and benign prostatic hyperplasia.Medical physics · 2025Article
- Design and validation of an automated healthcare-integrated biobanking algorithm for identification of advanced chronic kidney disease.Computational and structural biotechnology journal · 2025Article
- Weakly Semi-supervised phenotyping using Electronic Health records.Journal of biomedical informatics · 2022Article
- Phe2vec: Automated disease phenotyping based on unsupervised embeddings from electronic health records.Patterns (New York, N.Y.) · 2021Article
- Generative transfer learning for measuring plausibility of EHR diagnosis records.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Transitive Sequencing Medical Records for Mining Predictive and Interpretable Temporal Representations.Patterns (New York, N.Y.) · 2020Article
- Cloud Services for Patient Cohort Identification Using the Informatics for Integrating Biology and the Bedside Platform.BioMed research international · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
motivationExpert-labeled data are essential to train phenotyping algorithms for cohort identification. However expert labeling is time and labor intensive, and the costs remain prohibitive for scaling phenotyping to wider use-cases.
resultsWe present an approach referred to as polar labeling (PL), to create silver standard for training machine learning (ML) for disease classification. We test the hypothesis that ML models trained on the silver standard created by applying PL on unlabeled patient records, are comparable in performance to the ML models trained on gold standard, created by clinical experts through manual review of patient records. We perform experimental validation using health records of 38 023 patients spanning six diseases. Our results demonstrate the superior performance of the proposed approach. AVAILABILITY AND IMPLEMENTATION: We provide a Python implementation of the algorithm and the Python code developed for this study on Github. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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