Castillo C, Steffens T, Sim L, Caffery L. The effect of clinical information on radiology reporting: a systematic review. J Med Radiat Sci 2021;68:60-74. https://doi.org/10.1002/jmrs.424.
Hawkins CM, Anton CG, Bankes WM, et al. Improving the availability of clinical history accompanying radiographic examinations in a large pediatric radiology department. AJR Am J Roentgenol 2014;202:790-796. https://doi.org/10.2214/AJR.13.11273.
Shoolin J, Ozeran L, Hamann C, Bria W 2nd. Association of Medical Directors of Information Systems consensus on inpatient electronic health record documentation. Appl Clin Inform 2013;4:293-303. https://doi.org/10.4338/ACI-2013-02-R-0012.
Article CAS PubMed PubMed Central Google Scholar
Demner-Fushman D, Chapman WW, McDonald CJ. What can natural language processing do for clinical decision support? J Biomed Inform 2009;42:760-772. https://doi.org/10.1016/j.jbi.2009.08.007.
Article PubMed PubMed Central Google Scholar
Liang J, Tsou CH, Poddar A. A novel system for extractive clinical note summarization using EHR data. In: Rumshisky A, Roberts K, Bethard S, Naumann T, eds. Proceedings of the 2nd Clinical Natural Language Processing Workshop. Minneapolis, MN: Association for Computational Linguistics, 2019;46–54. https://doi.org/10.18653/v1/W19-1906.
Vogel L. Cut-and-paste clinical notes confuse care, say US internists. CMAJ 2013;185:E826. https://doi.org/10.1503/cmaj.109-4656.
Article PubMed PubMed Central Google Scholar
Miller D. Leveraging BERT for extractive text summarization on lectures. arXiv. Preprint posted online June 7, 2019.https://doi.org/10.48550/arXiv.1906.04165.
Raffel C, Shazeer N, Roberts A, et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv. Preprint posted online October 23, 2019.https://doi.org/10.48550/arXiv.1910.10683
Moradi M, Dorffner G, and Samwald M. Deep contextualized embeddings for quantifying the informative content in biomedical text summarization. Computer methods and programs in biomedicine, 2020;184, p.105117.
Kanwal N, Rizzo G. Attention-based clinical note summarization. In: Hong J, Bures M, Won Park J, Cerny T, eds. Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing. New York, NY: Association for Computing Machinery, 2022;813–820. https://doi.org/10.1145/3477314.3507256.
Lalitha E, Ramani K, Shahida D, Deepak EVS, Bindu MH, and Shaikshavali D. “Text Summarization of Medical Documents using Abstractive Techniques.” 2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC), Salem, India, 2023, pp. 939–943, https://doi.org/10.1109/ICAAIC56838.2023.10140885.
Kalyan KS, Rajasekharan A, Sangeetha S. AMMU: a survey of transformer-based biomedical pretrained language models. J Biomed Inform 2021;126:103982. https://doi.org/10.1016/j.jbi.2021.103982.
Smith LN. Cyclical learning rates for training neural networks. IEEE Winter Conf Appl Comput Vis 2017;2017:464-472. https://doi.org/10.1109/WACV.2017.58.
Kingma DP, Ba J. Adam: a method for stochastic optimization. arXiv. Preprint posted online December 22, 2014. https://doi.org/10.48550/arXiv.1412.6980.
Liu Y, Lapata M. Text summarization with pretrained encoders. arXiv. Preprint posted online August 22, 2019. https://doi.org/10.48550/arXiv.1908.08345.
Liu Y. Fine-tune BERT for extractive summarization. arXiv. Preprint posted online March 25, 2019. https://doi.org/10.48550/arXiv.1903.10318.
Wong J, Murray Horwitz M, Zhou L, Toh S. Using machine learning to identify health outcomes from electronic health record data. Current epidemiology reports. 2018 Dec;5:331-42.
Article PubMed PubMed Central Google Scholar
Padmakumar V and He H. Unsupervised extractive summarization using pointwise mutual information. arXiv. Preprint posted online February 11, 2021. https://doi.org/10.48550/arXiv.2102.06272.
Comments (0)