Attention-based Clinical Note Summarization
37th Annual Acm Symposium on Applied Computing
; : 813-820, 2022.
Article
in English
| Web of Science | ID: covidwho-2309179
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.
Full text:
Available
Collection:
Databases of international organizations
Database:
Web of Science
Type of study:
Prognostic study
Language:
English
Journal:
37th Annual Acm Symposium on Applied Computing
Year:
2022
Document Type:
Article
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