Attention-based Clinical Note Summarization
This work addresses the need for efficient summarization of clinical notes to aid doctors in diagnosis and save time, particularly during high-workload situations like the COVID-19 pandemic, but it appears incremental as it applies an existing attention-based method to a specific domain.
The paper tackles the problem of dense information in electronic health records by applying a multi-head attention mechanism for extractive summarization of clinical notes, resulting in attention scores transformed to extract critical phrases for visualization and human use.
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.