Polar labeling: silver standard algorithm for training disease classifiers.
Bioinformatics
; 36(10): 3200-3206, 2020 05 01.
Article
em En
| MEDLINE
| ID: mdl-32049335
ABSTRACT
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 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.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Algoritmos
/
Aprendizado de Máquina
Tipo de estudo:
Guideline
/
Prognostic_studies
Limite:
Humans
Idioma:
En
Revista:
Bioinformatics
Assunto da revista:
INFORMATICA MEDICA
Ano de publicação:
2020
Tipo de documento:
Article
País de afiliação:
Estados Unidos