Abstract
Electronic Health Records (EHRs) capture all information regarding the treatment of a patient. In the case of patients with chronic diseases that needs periodic interdepartmental evaluations, the volume of EHR becomes very large and clinicians find it difficult to find all the required information within the constrained time. In such scenarios, automated EHR summarization can be very useful. Summary of EHR can help healthcare provider to see the most important clinical decisions and history details in brief. This will help in saving time, reducing medical errors like prescribing an existing medication and can help in better clinical decision making. This paper reviews the existing approaches for extractive summarization and proposes a technique to provide extractive summary for a single EHR note.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
George M, Chacko A, Kurien SK (2019) Proactive diabetes management: research directions. In: Proceedings of the 20th international conference on distributed computing and networking, ICDCN ’19, pp 486–491. ACM, New York, NY, USA
Pivovarov R, Elhadad N (2015) Automated methods for the summarization of electronic health records. J Am Med Inf Assoc 22(5):938–947
Dabek F, Jimenez E, Caban J. J (2017) A timeline-based framework for aggregating and summarizing electronic health records. In: 2017 IEEE workshop on visual analytics in healthcare (VAHC), pp 55–61. IEEE
Alsentzer E, Kim A (2018) Extractive summarization of ehr discharge notes. In: arXiv:1810.12085v1 [cs.IR]
Liang J, Tsou CH, Poddar A (2019) A novel system for extractive clinical note summarization using ehr data. In: Proceedings of the 2nd clinical natural language processing workshop, pp 46–54
Si Y, Roberts K (2018) A frame-based nlp system for cancer-related information extraction. In: AMIA annual symposium proceedings, pp 1524–1533
Gong JJ, Guttag JV(2018) Learning to summarize electronic health records using cross-modality correspondences. In: Machine learning for healthcare conference, pp 551–570
Plaza L, Stevenson M, D´ıaz A (2012) Resolving ambiguity in biomedical text to improve summarization. Inf Process Manag 48(4):755–766
Rossiello G, Basile P, Semeraro G (2017) Centroid-based text summarization through compositionality of word embeddings. In: Proceedings of the multiling 2017 workshop on summarization and summary evaluation across source types and genres, pp 12–21
Chengzhang X, Dan L (2018) Chinese text summarization algorithm based on word2vec. J Phys 976(1):012006
Arora S, Liang Y, Ma T (2016) A simple but tough-to-beat baseline for sentence embeddings. In: ICLR 2017 conference
Hirao T, Isozaki H, Maeda E, Matsumoto Y (2002) Extracting important sentences with support vector machines. In: Proceedings of the 19th international conference on computational linguistics, vol 1, pp 1–7
Beam AL, Kompa B, Fried I, Palmer NP, Shi X, Cai T, Kohane IS (2018) Clinical concept embeddings learned from massive sources of multimodal medical data. arXiv preprint arXiv:1804.01486
Chen Q, Peng Y, Lu Z (2019) Biosentvec: creating sentence embeddings for biomedical texts. In: IEEE international conference on health informatics (ICHI). IEEE
Bodenreider O (2004) The unified medical language system (umls): integrating biomedical terminology. Nucleic Acids Res 32(1):D267–D270
Aronson AR, Lang FM (2010) An overview of metamap: historical perspective and recent advances. J Am Med Inf Assoc 17(3):229–236
Johnson AE, Pollard TJ, Shen L, Li-wei HL, Feng M, Ghassemi M, Moody B, Szolovits P, Celi LA, Mark RG (2016) Mimic-iii, a freely accessible critical care database. Sci Data 3:160035
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Chaudhary, A., George, M., Chacko, A.M. (2021). Extractive Summarization of EHR Notes. In: Dave, M., Garg, R., Dua, M., Hussien, J. (eds) Proceedings of the International Conference on Paradigms of Computing, Communication and Data Sciences. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-7533-4_73
Download citation
DOI: https://doi.org/10.1007/978-981-15-7533-4_73
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-7532-7
Online ISBN: 978-981-15-7533-4
eBook Packages: EngineeringEngineering (R0)