Modified Bidirectional Encoder Representations From Transformers Extractive Summarization Model for Hospital Information Systems Based on Character-Level Tokens (AlphaBERT): Development and Performance Evaluation.

ABSTRACT: BACKGROUND:Doctors must care for many patients simultaneously, and it is time-consuming to find and examine all patients' medical histories. Discharge diagnoses provide hospital staff with sufficient information to enable handling multiple patients; however, the excessive amount of words in the diagnostic sentences poses problems. Deep learning may be an effective solution to overcome this problem, but the use of such a heavy model may also add another obstacle to systems with limited computing resources. OBJECTIVE:We aimed to build a diagnoses-extractive summarization model for hospital information systems and provide a service that can be operated even with limited computing resources. METHODS:We used a Bidirectional Encoder Representations from Transformers (BERT)-based structure with a

SUBMITTER: Chen YP 

PROVIDER: S-EPMC7221648 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature