MOTIVATION: Expert-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. RESULTS: We 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 38023 patients spanning 6 diseases. Our results demonstrate the superior performance of the proposed approach. AVAILABILITY: 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.
MOTIVATION: Expert-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. RESULTS: We 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 38023 patients spanning 6 diseases. Our results demonstrate the superior performance of the proposed approach. AVAILABILITY: 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.
Authors: Kavishwar B Wagholikar; Shreekanth V Joshi; Vishal V Pai Vernekar; Yuri Ostrovsky; Somnath D Desai; Pooja B Magdum; Sachin B Wakle; Sheetal Jain; Akshay Zagade; Rahul Patel; Shawn N Murphy Journal: Biomed Res Int Date: 2020-07-07 Impact factor: 3.411