2021/04/30 by Neel Kanwal, Giuseppe Rizzo
Computer Science · Medicine · #Advanced Text Analysis Techniques #Artificial intelligence #Automatic summarization #Computer science #Data science #Health care #Health records #Information retrieval #Medical record #Medicine #Natural Language Processing Techniques #Natural language processing #Topic Modeling #Visualization #Workload #cs.AI #cs.CL #cs.IR #cs.LG
paper · pdf · doi:10.1145/3477314.3507256
published as ACM SAC 2022 · Accepted at ACM SAC 2022, in Special Track "KNLP"
arxiv created 2022/02/28 · arxiv updated 2022/03/01 · openalex publication_date 2022/04/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.