Attention-based clinical note summarization
37th ACM/SIGAPP Symposium on Applied Computing, SAC 2022
; : 813-820, 2022.
Article
in English
| Scopus | ID: covidwho-1874703
ABSTRACT
In recent years, the trend of deploying digital systems in numerous industries has hiked. The health sector has observed an extensive adoption of digital systems and services that generate significant medical records. Electronic health records contain valuable information for prospective and retrospective analysis that is often not entirely exploited because of the complicated dense information storage. The crude purpose of condensing health records is to select the information that holds most characteristics of the original documents based on a reported disease. These summaries may boost diagnosis and save a doctor's time during a saturated workload situation like the COVID-19 pandemic. In this paper, we are applying a multi-head attention-based mechanism to perform extractive summarization of meaningful phrases on clinical notes. Our method finds major sentences for a summary by correlating tokens, segments, and positional embeddings of sentences in a clinical note. The model outputs attention scores that are statistically transformed to extract critical phrases for visualization on the heat-mapping tool and for human use. © 2022 ACM.
clinical notes; deep learning; electronic health records; extractive summarization; ICD-9; information extraction; medical records; MIMIC-III; multi-head attention; natural language processing; transformer models; Diagnosis; E-learning; Information retrieval; Medical computing; Medical informatics; Records management; Digital services; Digital system; Extractive summarizations; Medical record; Transformer modeling; Natural language processing systems
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Prognostic study
Language:
English
Journal:
SIGAPP Symposium on Applied Computing, SAC 2022
Year:
2022
Document Type:
Article
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