A Novel System for Extractive Clinical Note Summarization using

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Corpus-Based Problem Selection for EHR Note Summarization.

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Extractive Summarization of EHR Discharge NotesPreprint

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The CLEF Chronicle: Patient Histories Derived from Electronic Health Records

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Cut-and-paste clinical notes confuse care, say US internists

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IBM Research System at MADE 2018: Detecting Adverse Drug Events from Electronic Health Records.

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Challenges of developing a digital scribe to reduce clinical documentation burden

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Leveraging Medical Knowledge Graphs Into Large Language Models for Diagnosis Prediction: Design and Application Study

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What’s in a Summary? Laying the Groundwork for Advances in Hospital-Course Summarization

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Applications of natural language processing in ophthalmology: present and future

Chen, J., & Baxter, S. L. (2022) · Frontiers in Medicine · doi:10.3389/fmed.2022.906554 · Cited by 40 · see citing works

Is artificial intelligence capable of generating hospital discharge summaries from inpatient records?

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Enhancing evidence-based medicine with natural language argumentative analysis of clinical trials.

Mayer, T., Marro, S., Cabrio, E., & Villata, S. (2021) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2021.102098 · Cited by 36 · see citing works

Identification of pediatric respiratory diseases using a fine-grained diagnosis system

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Patient Information Summarization in Clinical Settings: Scoping Review

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Sentiment Classification Method Based on Blending of Emoticons and Short Texts

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A Comprehensive Review of AI in Healthcare: Exploring Neural Networks in Medical Imaging, LLM-Based Interactive Response Systems, NLP-Based EHR Systems, Ethics, and Beyond

Sathe, N., Deodhe, V., Sharma, Y., & Shinde, A. (2023) · International Conference on Advanced Computing & Communication Technologies (ICACCTech) · doi:10.1109/icacctech61146.2023.00108 · Cited by 18 · see citing works

Towards Clinical Encounter Summarization: Learning to Compose Discharge Summaries from Prior NotesPreprint

Shing, H.-C., Shivade, C., Pourdamghani, N., Nan, F., Resnik, P., Oard, D. W., & Bhatia, P. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.13498 · Cited by 24 · see citing works

A two-stage workflow to extract and harmonize drug mentions from clinical notes into observational databases

Almeida, J. R., Silva, J., Matos, S., & Oliveira, J. L. (2021) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2021.103849 · Cited by 15 · see citing works

Characterizing the Value of Information in Medical Notes

Hsu, C.-C., Karnwal, S., Mullainathan, S., Obermeyer, Z., & Tan, C. (2020) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2020.findings-emnlp.187 · Cited by 14 · see citing works

Survey on the Biomedical Text Summarization Techniques with an Emphasis on Databases, Techniques, Semantic Approaches, Classification Techniques, and Similarity Measures

Pawar, D., Phansalkar, S., Sharma, A., Sahu, G. K., Ang, C. K., & Lim, W. H. (2023) · Sustainability · doi:10.3390/su15054216 · Cited by 13 · see citing works

CLIP: A Dataset for Extracting Action Items for Physicians from Hospital Discharge Notes

Mullenbach, J., Pruksachatkun, Y., Adler, S., Seale, J., Swartz, J. L., McKelvey, T. G., Dai, H., Yang, Y., & Sontag, D. A. (2021) · doi:10.18653/v1/2021.acl-long.109 · Cited by 13 · see citing works

Updated Headline Generation: Creating Updated Summaries for Evolving News Stories

Panthaplackel, S., Benton, A., & Dredze, M. H. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.446 · Cited by 12 · see citing works

Towards an Automated SOAP Note: Classifying Utterances from Medical ConversationsPreprint

Schloss, B., & Konam, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.08749 · Cited by 13 · see citing works

Hybrid Summarization of Medical Records for Predicting Length of Stay in the Intensive Care Unit

Rhazzafe, S., Caraffini, F., Colreavy‐Donnelly, S., Dhassi, Y., Kühn, S., & Nikolov, N. S. (2024) · Applied Sciences · doi:10.3390/app14135809 · Cited by 11 · see citing works

Transformer-based active learning for multi-class text annotation and classification

Afzal, M., Hussain, J., Abbas, A., Hussain, M., Attique, M., & Lee, S. (2024) · Digital Health · doi:10.1177/20552076241287357 · Cited by 9 · see citing works

Zero-Shot Large Language Models for Long Clinical Text Summarization with Temporal ReasoningPreprint

Kruse, M., Hu, S., Derby, N., Wu, Y., Stonbraker, S., Yao, B., Wang, D., Goldberg, E., & Gao, Y. (2025) · medRxiv · doi:10.1101/2025.07.21.25331947 · Cited by 9 · see citing works

Generating medically-accurate summaries of patient-provider dialogue: A multi-stage approach using large language models

Nair, V., Schumacher, E., & Kannan, A. (2023) · Clinical Natural Language Processing Workshop (ClinicalNLP) · doi:10.18653/v1/2023.clinicalnlp-1.26 · Cited by 11 · see citing works

Multi-Task Training with In-Domain Language Models for Diagnostic Reasoning

Sharma, B., Gao, Y., Miller, T. A., Churpek, M. M., Afshar, M., & Dligach, D. (2023) · Clinical Natural Language Processing Workshop (ClinicalNLP) · doi:10.18653/v1/2023.clinicalnlp-1.10 · Cited by 8 · see citing works

RNSum: A Large-Scale Dataset for Automatic Release Note Generation via Commit Logs Summarization

Kamezawa, H., Nishida, N., Shimizu, N., Miyazaki, T., & Nakayama, H. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.597 · Cited by 7 · see citing works

Exploring optimal granularity for extractive summarization of unstructured health records: Analysis of the largest multi-institutional archive of health records in Japan

Ando, K., Okumura, T., Komachi, M., Horiguchi, H., & Matsumoto, Y. (2022) · PLOS Digital Health · doi:10.1371/journal.pdig.0000099 · Cited by 7 · see citing works

Clinical Artificial Intelligence

McDermott, M. B. A., Nestor, B., & Szolovits, P. (2022) · Clinics in Laboratory Medicine · doi:10.1016/j.cll.2022.09.004 · Cited by 6 · see citing works

Is In-hospital Meta-information Useful for Abstractive Discharge Summary Generation?

Ando, K., Komachi, M., Okumura, T., Horiguchi, H., & Matsumoto, Y. (2022) · Conference on Technologies and Applications of Artificial Intelligence (TAAI) · doi:10.1109/taai57707.2022.00034 · Cited by 4 · see citing works

SpanPredict: Extraction of Predictive Document Spans with Neural Attention

Subramanian, V., Engelhard, M., Berchuck, S. I., Chen, L., Henao, R., & Carin, L. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-main.413 · Cited by 4 · see citing works

Enhancing Radiology Clinical Histories Through Transformer-Based Automated Clinical Note Summarization

Eghbali, N., Klochko, C. L., Mahdi, Z. K., Alhiari, L., Lee, J. K., Knisely, B., Craig, J. G., & Ghassemi, M. M. (2025) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-025-01477-8 · Cited by 3 · see citing works

Semantic modeling and visualization of semantic groups of clinical text documents

Kenei, J., & Opiyo, E. T. O. (2022) · International Journal of Information Technology · doi:10.1007/s41870-022-00970-5 · Cited by 3 · see citing works

Modeling and Visualization of Clinical Texts to Enhance Meaningful and User-Friendly Information Retrieval

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Prediction of Sudden Health Crises Owing to Congestive Heart Failure with Deep Learning Models

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DG_summ: A schema-driven approach for personalized summarizing heterogeneous data graphs

Beldi, A., Sassi, S., Chbeir, R., & Jemai, A. (2023) · Computer Science and Information Systems · doi:10.2298/csis230331062b · Cited by 1 · see citing works

Extractive Summarization of EHR Notes

Chaudhary, A., George, M., & Chacko, A. M. (2021) · Algorithms for intelligent systems · doi:10.1007/978-981-15-7533-4_73 · Cited by 1 · see citing works

Argument Mining on Clinical Trials

Mayer, T. (2020) · theses.fr (ABES) · doi:10.70675/8c2cf7b5z08cbz40e4za421z0115aa4a8eaa · Cited by 1 · see citing works

Can Electronic care planning using AI Summarization Yield equal Documentation Quality? (EASY eDocQ)Preprint

Dorr, D. A., Weiskopf, N. G., Sabile, J. M. G., Young, E., Mathis, D., Weller, S. L., Alex, J., Doerr, K., & Bedrick, S. D. (2025) · medRxiv · doi:10.1101/2025.01.22.25320982

MedSyn: AI-driven Medical History Summarization Using EHR Data

Muralidharan, A., ahnaf, S. M. A., Anirudh, A., Raj, A. B., & Manmadhan, S. (2025) · Advanced Computing and Communication Technologies for High Performance Applications (ACCTHPA) · doi:10.1109/accthpa65749.2025.11168697

Integrating Prescription Summarization and Handwritten Prescription Digitization for Efficient Clinical Data Management in EHR and PHR Systems

Shankar, R. S., Ganiga, R., N., M. S., Arjunan, R. V., & Chadaga, K. (2025) · IEEE Global Conference for Advancement in Technology (GCAT) · doi:10.1109/gcat66372.2025.11368390

How Summary Length Affects ROUGE Scores: A Case Study on BERT Summarization of Clinical Case Reports

Rhazzafe, S., Colreavy‐Donnelly, S., & Nikolov, N. S. (2025) · IEEE International Conference on Advances in Data-Driven Analytics and Intelligent Systems (ADACIS) · doi:10.1109/adacis65663.2025.11436781

Synthèse Graphique Multidimensionnelle : Application aux documents hétérogènes

Beldi, A. (2024) · doi:10.70675/87fdd0ebzb635z419ezb1a6z22607b8201a7

Automated Medical Snippets Generation From Patient Discharge Summaries Using NLP and LLM Models - A Review

Meena, E., & Sivashankari, R. (2026) · Springer eBooks · doi:10.1007/978-3-032-29942-0_22

Using Natural Language Processing to Identify Adverse Drug Events Characterized by Medication Replacement in Primary Care Electronic Medical Records: Algorithm and Validation Study

Katz, A., Dhankar, A., Fransoo, G., Pappas, D. E., Hamad, A. F., Leong, C., & Singer, A. G. (2026) · Journal of Medical Internet Research · doi:10.2196/86467

Optimizing generative artificial intelligence for clinical summarization: a blinded comparison study of automated versus human care planning synopses

Dorr, D. A., Weiskopf, N. G., Sabile, J. M. G., Young, E., Mathis, D., Weller, S. L., Alex, J., Doerr, K., & Bedrick, S. (2026) · JAMIA Open · doi:10.1093/jamiaopen/ooag175

Longitudinal Summarization of Mental Health Clinical Notes Using Fine-Tuned LLM and Hybrid RAG

Dilip, R., & Venugopalan, M. (2026) · International Conference on Emerging Technologies in Computing and Communication (ETCC) · doi:10.1109/etcc69750.2026.11681980

NLP-driven extraction of clinical insights from unstructured EHR data

Jasthi, V. N. (2021) · International Journal on Science and Technology · doi:10.71097/ijsat.v12.i3.7551

Extractive Summarization of EHR Notes

Chaudhary, A., George, M., & Chacko, A. M. (2021) · Algorithms for intelligent systems · doi:10.1007/978-981-15-7533-4_73 · Cited by 1 · see citing works

Unlocking the Power of Clinical Notes: Natural Language Processing in Healthcare

Kondra, S., Xu, W., & Raghavan, V. V. (2024) · Acta Scientific Medical Sciences · doi:10.31080/asms.2024.08.1821 · Cited by 4 · see citing works

Towards Accurate and Clinically Meaningful Summarization of Electronic Health Record Notes: A Guided Approach

Luo, Z., Ji, Y., Gupta, A., Li, Z., Frisch, A., & He, D. (2023) · IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI ...) · doi:10.1109/bhi58575.2023.10313411 · Cited by 8 · see citing works

EHRKit: A Python Natural Language Processing Toolkit for Electronic Health Record TextsPreprint

Li, I., You, K., Qiao, Y., Huang, L., Hsieh, C.-C., Rosand, B., Goldwasser, J., & Radev, D. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2204.06604 · Cited by 3 · see citing works