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

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 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

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