|Title:||Modified Bidirectional Encoder Representations From Transformers Extractive Summarization Model for Hospital Information Systems Based on Character-Level Tokens (AlphaBERT): Development and Performance Evaluation||Authors:||YEN-PIN CHEN
|Keywords:||BERT; automatic summarization; deep learning; emergency medicine; transformer||Issue Date:||29-Apr-2020||Publisher:||JMIR PUBLICATIONS, INC||Journal Volume:||8||Journal Issue:||4||Source:||JMIR medical informatics||Abstract:||
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.
|Appears in Collections:||醫學院附設醫院 (臺大醫院)|
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.