This paper is in the following e-collection/theme issue:

Clinical Informatics (1045) Big Data (241) Machine Learning (1491) Natural Language Processing (704)

Published on in Vol 22, No 10 (2020): October

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/19810, first published .
Clinical Context–Aware Biomedical Text Summarization Using Deep Neural Network: Model Development and Validation

Clinical Context–Aware Biomedical Text Summarization Using Deep Neural Network: Model Development and Validation

Clinical Context–Aware Biomedical Text Summarization Using Deep Neural Network: Model Development and Validation

Authors of this article:

Muhammad Afzal1, 2 Author Orcid Image ;   Fakhare Alam2 Author Orcid Image ;   Khalid Mahmood Malik2 Author Orcid Image ;   Ghaus M Malik3 Author Orcid Image
Article Authors Cited by (31) Tweetations (5) Metrics

Journals

  1. Wang M, Wang M, Yu F, Yang Y, Walker J, Mostafa J. A systematic review of automatic text summarization for biomedical literature and EHRs. Journal of the American Medical Informatics Association 2021;28(10):2287 View
  2. Chatterjee N, Agarwal R. Studying the Effect of Syntactic Simplification on Text Summarization. IETE Technical Review 2023;40(2):155 View
  3. M N, P S. Abstractive text summarization employing ontology-based knowledge-aware multi-focus conditional generative adversarial network (OKAM-CGAN) with hybrid pre-processing methodology. Multimedia Tools and Applications 2023;82(15):23305 View
  4. Yang K, Nambudiri V. Anticipating Ambulatory Automation: Potential Applications of Administrative and Clinical Automation in Outpatient Healthcare Delivery. Applied Clinical Informatics 2021;12(05):1157 View
  5. Bitar H, Babour A, Nafa F, Alzamzami O, Alismail S. Increasing Women’s Knowledge about HPV Using BERT Text Summarization: An Online Randomized Study. International Journal of Environmental Research and Public Health 2022;19(13):8100 View
  6. Phatak A, Savage D, Ohle R, Smith J, Mago V. Medical Text Simplification Using Reinforcement Learning (TESLEA): Deep Learning–Based Text Simplification Approach. JMIR Medical Informatics 2022;10(11):e38095 View
  7. Bedi P, Bala M, Sharma K. Extractive summarization using concept‐space and keyword phrase. Expert Systems 2022;39(10) View
  8. Pawar D, Phansalkar S, Sharma A, Sahu G, Ang C, Lim W. Survey on the Biomedical Text Summarization Techniques with an Emphasis on Databases, Techniques, Semantic Approaches, Classification Techniques, and Similarity Measures. Sustainability 2023;15(5):4216 View
  9. Abimannan S, El-Alfy E, Chang Y, Hussain S, Shukla S, Satheesh D. Ensemble Multifeatured Deep Learning Models and Applications: A Survey. IEEE Access 2023;11:107194 View
  10. Alam F, Giglou H, Malik K. Automated clinical knowledge graph generation framework for evidence based medicine. Expert Systems with Applications 2023;233:120964 View
  11. Schmidt L, Finnerty Mutlu A, Elmore R, Olorisade B, Thomas J, Higgins J. Data extraction methods for systematic review (semi)automation: Update of a living systematic review. F1000Research 2023;10:401 View
  12. Swetha G, Kumar S. A hierarchical framework based on transformer technology to achieve factual consistent and non-redundant abstractive text summarization. Multimedia Tools and Applications 2023;83(16):47587 View
  13. Zhou B, Yang G, Shi Z, Ma S. Natural Language Processing for Smart Healthcare. IEEE Reviews in Biomedical Engineering 2024;17:4 View
  14. Mulla S, Shaikh N. Weighted Graph Embedding Feature with Bi-Directional Long Short-Term Memory Classifier for Multi-Document Text Summarization. International Journal of Image and Graphics 2024;24(02) View
  15. Witte C, Schmidt D, Cimiano P. Comparing generative and extractive approaches to information extraction from abstracts describing randomized clinical trials. Journal of Biomedical Semantics 2024;15(1) View
  16. Abazari Kia M, Garifullina A, Kern M, Chamberlain J, Jameel S. Question‐driven text summarization using an extractive‐abstractive framework. Computational Intelligence 2024;40(3) View
  17. Huang E, Yang J, Liao K, Tseng W, Lee C, Gill M, Compas C, See S, Tsai F. Predicting blood–brain barrier permeability of molecules with a large language model and machine learning. Scientific Reports 2024;14(1) View
  18. Lokker C, McKibbon K, Afzal M, Navarro T, Linkins L, Haynes R, Iorio A. The McMaster Health Information Research Unit: Over a Quarter-Century of Health Informatics Supporting Evidence-Based Medicine. Journal of Medical Internet Research 2024;26:e58764 View
  19. Adhikary P, Srivastava A, Kumar S, Singh S, Manuja P, Gopinath J, Krishnan V, Gupta S, Deb K, Chakraborty T. Exploring the Efficacy of Large Language Models in Summarizing Mental Health Counseling Sessions: Benchmark Study. JMIR Mental Health 2024;11:e57306 View
  20. Luo M, Xue B, Niu B. A comprehensive survey for automatic text summarization: Techniques, approaches and perspectives. Neurocomputing 2024;603:128280 View
  21. Prasad Mudigonda K, Lingineni N, Manisai Y, Pennada M, Gadde M, Sai Aluri R. Extractive text summarization on medical insights using fine-tuned transformers. International Journal of Computers and Applications 2024;46(11):957 View

Books/Policy Documents

  1. Krishnaveni Reddy E, Mohammed T. Intelligent Systems and Sustainable Computing. View
  2. Ramesh D, Kothandaraman D, Chegoni R, Mohmmad S, Pasha S. Proceedings of the 2nd International Conference on Cognitive and Intelligent Computing. View
  3. Kukreja J, Kumar R. Mining Biomedical Text, Images and Visual Features for Information Retrieval. View