Developing Artificial Intelligence Models for Extracting Oncologic Outcomes from Japanese Electronic Health Records.

ABSTRACT:

Introduction

A framework that extracts oncological outcomes from large-scale databases using artificial intelligence (AI) is not well established. Thus, we aimed to develop AI models to extract outcomes in patients with lung cancer using unstructured text data from electronic health records of multiple hospitals.

Methods

We constructed AI models (Bidirectional Encoder Representations from Transformers [BERT], Naïve Bayes, and Longformer) for tumor evaluation using the University of Miyazaki Hospital (UMH) database. This data included both structured and unstructured data from progress notes, radiology reports, and discharge summaries. The BERT model was applied to the Life Data Initiative (LDI) data set of six hospitals. Study outcomes included the performance of AI models a

SUBMITTER: Araki K 

PROVIDER: S-EPMC9988800 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature