Training a Deep Contextualized Language Model for International Classification of Diseases, 10th Revision Classification via Federated Learning: Model Development and Validation Study.

ABSTRACT:

Background

The automatic coding of clinical text documents by using the International Classification of Diseases, 10th Revision (ICD-10) can be performed for statistical analyses and reimbursements. With the development of natural language processing models, new transformer architectures with attention mechanisms have outperformed previous models. Although multicenter training may increase a model's performance and external validity, the privacy of clinical documents should be protected. We used federated learning to train a model with multicenter data, without sharing data per se.

Objective

This study aims to train a classification model via federated learning for ICD-10 multilabel classification.

Methods

Text data from discharge notes in electronic medical records w

SUBMITTER: Chen PF 

PROVIDER: S-EPMC9693720 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature