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

YEN-PIN CHENChen, Yi-YingYi-YingChenCHIEN-HUA HUANGJR-JIUN LINFEI-PEI LAI2022-02-222022-02-222020-04-292291-9694https://scholars.lib.ntu.edu.tw/handle/123456789/595336Doctors 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.enBERT; automatic summarization; deep learning; emergency medicine; transformerModified Bidirectional Encoder Representations From Transformers Extractive Summarization Model for Hospital Information Systems Based on Character-Level Tokens (AlphaBERT): Development and Performance Evaluationjournal article10.2196/17787323478062-s2.0-85096304469WOS:000531089000020https://scholars.lib.ntu.edu.tw/handle/123456789/557856