CERC: an interactive content extraction, recognition, and construction tool for clinical and biomedical text

• Type of Material: Article
• Authors: Eva K. Lee, Georgia Institute of Technology; Karan Uppal, Emory University
• Language: English
• Date: 2020-12-15
• Publisher: BMC
• Publication Version: Final Published Version
• Copyright Statement: © The Author(s) 2020.
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1186/s12911-020-01330-8
• Title of Journal or Parent Work: BMC MEDICAL INFORMATICS AND DECISION MAKING
• Volume: 20
• Issue: Suppl 14
• Start Page: 306
• End Page: 306
• Grant/Funding Information: This work is partially supported by grants from the National Science Foundation, IIP-0832390 and IIP-1361532. Findings and conclusions in this paper (the study and collection, analysis, and interpretation of data and in writing the manuscript) are those of the authors and do not necessarily reflect the views of the National Science Foundation. We acknowledge funding from Emory University in support of Karan Uppal while he was a full-time Ph.D. student at Georgia Institute of Technology, supervised under Lee and worked on this project. No sponsorship is provided for publication. The lead author is responsible for the publication fee.
• Keywords: Content extraction and recognition; Automatic translation; IMPACT; Extractive summarization; Indicative summarization; SERVICES; RECORDS; Clinical decision support; HEALTH-CARE; Multiple indicators; LANGUAGE; INFORMATION; MEDICAL INTERPRETATION; Sentence extraction and ranking; Extracting salient information; PROFESSIONAL INTERPRETERS; Multi indicator text summarization algorithm; Life Sciences & Biomedicine; Science & Technology; Automatic summarization; Medical Informatics; Machine learning; LIMITED ENGLISH PROFICIENCY
• Research Categories: Engineering, Biomedical

Abstract

Background: Automated summarization of scientific literature and patient records is essential for enhancing clinical decision-making and facilitating precision medicine. Most existing summarization methods are based on single indicators of relevance, offer limited capabilities for information visualization, and do not account for user specific interests. In this work, we develop an interactive content extraction, recognition, and construction system (CERC) that combines machine learning and visualization techniques with domain knowledge for highlighting and extracting salient information from clinical and biomedical text. Methods: A novel sentence-ranking framework multi indicator text summarization, MINTS, is developed for extractive summarization. MINTS uses random forests and multiple indicators of importance for relevance evaluation and ranking of sentences. Indicative summarization is performed using weighted term frequency-inverse document frequency scores of over-represented domain-specific terms. A controlled vocabulary dictionary generated using MeSH, SNOMED-CT, and PubTator is used for determining relevant terms. 35 full-text CRAFT articles were used as the training set. The performance of the MINTS algorithm is evaluated on a test set consisting of the remaining 32 full-text CRAFT articles and 30 clinical case reports using the ROUGE toolkit. Results: The random forests model classified sentences as “good” or “bad” with 87.5% accuracy on the test set. Summarization results from the MINTS algorithm achieved higher ROUGE-1, ROUGE-2, and ROUGE-SU4 scores when compared to methods based on single indicators such as term frequency distribution, position, eigenvector centrality (LexRank), and random selection, p < 0.01. The automatic language translator and the customizable information extraction and pre-processing pipeline for EHR demonstrate that CERC can readily be incorporated within clinical decision support systems to improve quality of care and assist in data-driven and evidence-based informed decision making for direct patient care. Conclusions: We have developed a web-based summarization and visualization tool, CERC (https://newton.isye.gatech.edu/CERC1/), for extracting salient information from clinical and biomedical text. The system ranks sentences by relevance and includes features that can facilitate early detection of medical risks in a clinical setting. The interactive interface allows users to filter content and edit/save summaries. The evaluation results on two test corpuses show that the newly developed MINTS algorithm outperforms methods based on single characteristics of importance.