{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T23:09:50Z","timestamp":1784243390147,"version":"3.55.0"},"reference-count":44,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,4,14]],"date-time":"2025-04-14T00:00:00Z","timestamp":1744588800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>This systematic study seeks to evaluate the use and impact of transformer models in the healthcare domain, with a particular emphasis on their usefulness in tackling key medical difficulties and performing critical natural language processing (NLP) functions. The research questions focus on how these models can improve clinical decision-making through information extraction and predictive analytics. Our findings show that transformer models, especially in applications like named entity recognition (NER) and clinical data analysis, greatly increase the accuracy and efficiency of processing unstructured data. Notably, case studies demonstrated a 30% boost in entity recognition accuracy in clinical notes and a 90% detection rate for malignancies in medical imaging. These contributions emphasize the revolutionary potential of transformer models in healthcare, and therefore their importance in enhancing resource management and patient outcomes. Furthermore, this paper emphasizes significant obstacles, such as the reliance on restricted datasets and the need for data format standardization, and provides a road map for future research to improve the applicability and performance of these models in real-world clinical settings.<\/jats:p>","DOI":"10.3390\/computers14040148","type":"journal-article","created":{"date-parts":[[2025,4,14]],"date-time":"2025-04-14T04:42:07Z","timestamp":1744605727000},"page":"148","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Advancing Predictive Healthcare: A Systematic Review of Transformer Models in Electronic Health Records"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1244-4448","authenticated-orcid":false,"given":"Azza","family":"Mohamed","sequence":"first","affiliation":[{"name":"Faculty of Engineering and Computing, Liwa College, Al Ain P.O. Box 41009, United Arab Emirates"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-5446-9269","authenticated-orcid":false,"given":"Reem","family":"AlAleeli","sequence":"additional","affiliation":[{"name":"Faculty of Engineering & IT, The British University in Dubai, Dubai 345015, United Arab Emirates"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0823-8390","authenticated-orcid":false,"given":"Khaled","family":"Shaalan","sequence":"additional","affiliation":[{"name":"Faculty of Engineering & IT, The British University in Dubai, Dubai 345015, United Arab Emirates"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"8869","DOI":"10.1109\/ACCESS.2017.2694446","article-title":"Disease prediction by machine learning over big data from healthcare communities","volume":"5","author":"Chen","year":"2021","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Cho, K., Van Merri\u00ebnboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv.","DOI":"10.3115\/v1\/D14-1179"},{"key":"ref_3","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., and Polosukhin, I. (2017). Attention is all you need. arXiv."},{"key":"ref_4","unstructured":"Li, P., Zhang, T., Bai, Y., and Tian, Y. (2019). Transformer-based predictive modeling for electronic health records. J. Biomed. Inform., 93."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"7857","DOI":"10.1038\/s41467-023-43715-z","article-title":"TransformEHR: Transformer-based encoder-decoder generative model to enhance prediction of disease outcomes using electronic health records","volume":"14","author":"Yang","year":"2023","journal-title":"Nat. Commun."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"52","DOI":"10.32996\/jcsts.2023.5.1.7","article-title":"A Study of Ethical Issues in Natural Language Processing with Artificial Intelligence","volume":"5","author":"Ma","year":"2023","journal-title":"J. Comput. Sci. Technol. Stud."},{"key":"ref_7","unstructured":"Topol, E. (2019). Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again, Hachette UK. Basic Books."},{"key":"ref_8","unstructured":"Devlin, J., Chang, M.W., Lee, K., and Toutanova, K. (2019, January 2\u20137). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Minneapolis, MA, USA."},{"key":"ref_9","unstructured":"Liu, Y., and Lapata, M. (August, January 28). Hierarchical Transformers for Document Classification. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Florence, Italy."},{"key":"ref_10","first-page":"1","article-title":"Transformers for Text Classification: A Survey","volume":"975","author":"Zhang","year":"2019","journal-title":"Int. J. Comput. Appl."},{"key":"ref_11","first-page":"9","article-title":"Language Models are Unsupervised Multitask Learners","volume":"1","author":"Radford","year":"2019","journal-title":"OpenAI"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1234","DOI":"10.1093\/bioinformatics\/btz682","article-title":"BioBERT: A pre-trained biomedical language representation model for biomedical text mining","volume":"36","author":"Chen","year":"2020","journal-title":"Bioinformatics"},{"key":"ref_13","unstructured":"Kitchenham, B., and Charters, S. (2025, January 01). Guidelines for Performing Systematic Literature Reviews in Software Engineering. EBSE Technical Report. Available online: https:\/\/www.researchgate.net\/profile\/Barbara-Kitchenham\/publication\/302924724_Guidelines_for_performing_Systematic_Literature_Reviews_in_Software_Engineering\/links\/61712932766c4a211c03a6f7\/Guidelines-for-performing-Systematic-Literature-Reviews-in-Software-Engineering.pdf."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Alsentzer, E., Murphy, J.R., Boag, W., Weng, W.-H., Jin, D., Naumann, T., and McDermott, M. (2019, January 7). Publicly Available Clinical BERT Embeddings. Proceedings of the 2nd Clinical Natural Language Processing Workshop (ClinicalNLP), Minneapolis, MA, USA.","DOI":"10.18653\/v1\/W19-1909"},{"key":"ref_15","first-page":"2961","article-title":"Reproducibility in machine learning: A survey","volume":"31","author":"Fang","year":"2020","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_16","first-page":"55","article-title":"Precision and recall: A comprehensive study of the evaluation metrics for deep learning models","volume":"19","author":"He","year":"2017","journal-title":"J. Mach. Learn."},{"key":"ref_17","first-page":"1","article-title":"Advancements in transformer models for NLP and their applications","volume":"71","author":"Khan","year":"2022","journal-title":"J. Artif. Intell. Res."},{"key":"ref_18","unstructured":"Kumar, S., Patil, S., and Wadhwa, A. (2021). Data-Driven Healthcare: Applications of Machine Learning and NLP Techniques, Springer."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Antikainen, E., Linnosmaa, J., Umer, A., Oksala, N., Eskola, M., van Gils, M., Hernesniemi, J., and Gabbouj, M. (2023). Transformers for cardiac patient mortality risk prediction from heterogeneous electronic health records. Sci. Rep., 13.","DOI":"10.1038\/s41598-023-30657-1"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"102697","DOI":"10.1016\/j.inffus.2024.102697","article-title":"Transformers in biosignal analysis: A review","volume":"114","author":"Anwar","year":"2025","journal-title":"Inf. Fusion"},{"key":"ref_21","unstructured":"Batista, V.A., and Evsukoff, A.G. (2023). Application of Transformers based methods in Electronic Medical Records: A Systematic Literature Review. arXiv."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Choi, E., Xu, Z., Li, Y., Dusenberry, M.W., Flores, G., Xue, Y., and Dai, A.M. (2019). Learning the Graphical Structure of Electronic Health Records with Graph Convolutional Transformer. arXiv.","DOI":"10.1609\/aaai.v34i01.5400"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1007\/s10916-024-02043-5","article-title":"Transformer Models in Healthcare: A Survey and Thematic Analysis of Potentials, Shortcomings and Risks","volume":"48","author":"Denecke","year":"2024","journal-title":"J. Med. Syst."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"140628","DOI":"10.1109\/ACCESS.2021.3119621","article-title":"Machine Learning Techniques for Biomedical Natural Language Processing: A Comprehensive Review","volume":"9","author":"Houssein","year":"2021","journal-title":"IEEE Access"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Li, Y., Rao, S., Solares, J.R.A., Hassaine, A., Canoy, D., Zhu, Y., Rahimi, K., and Salimi-Khorshidi, G. (2019). BEHRT: Transformer for Electronic Health Records. arXiv.","DOI":"10.1038\/s41598-020-62922-y"},{"key":"ref_26","first-page":"2108","article-title":"Transformer-based argument mining for healthcare applications","volume":"325","author":"Mayer","year":"2020","journal-title":"Front. Artif. Intell. Appl."},{"key":"ref_27","unstructured":"Nerella, S., Bandyopadhyay, S., Zhang, J., Contreras, M., Siegel, S., Bumin, A., Silva, B., Sena, J., Shickel, B., and Bihorac, A. (2023). Transformers in Healthcare: A Survey. arXiv."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Rupp, M., Peter, O., and Pattipaka, T. (2023). ExBEHRT: Extended Transformer for Electronic Health Records to Predict Disease Subtypes & Progressions, Springer.","DOI":"10.1007\/978-3-031-39539-0_7"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1007\/s10462-023-10677-z","article-title":"Transformers in health: A systematic review on architectures for longitudinal data analysis","volume":"57","author":"Siebra","year":"2024","journal-title":"Artif. Intell. Rev."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1109\/RBME.2019.2904488","article-title":"Harnessing the Power of Machine Learning in Dementia Informatics Research: Issues, Opportunities and Challenges","volume":"13","author":"Tsang","year":"2019","journal-title":"IEEE Rev. Biomed. Eng."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.gande.2023.07.002","article-title":"Chat Generative Pre-Trained Transformer (ChatGPT) usage in healthcare","volume":"1","author":"Zhang","year":"2023","journal-title":"Gastroenterol. Endosc."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Zoabi, Y., Kehat, O., Lahav, D., Weiss-Meilik, A., Adler, A., and Shomron, N. (2021). Predicting bloodstream infection outcome using machine learning. Sci. Rep., 11.","DOI":"10.1038\/s41598-021-99105-2"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"e17787","DOI":"10.2196\/17787","article-title":"Modified bidirectional encoder representations from Transformers Extractive Summarization Model for hospital Information Systems based on Character-Level Tokens (AlphaBERT): Development and Performance Evaluation","volume":"8","author":"Chen","year":"2020","journal-title":"JMIR Med. Inform."},{"key":"ref_34","first-page":"112","article-title":"Leveraging transformer models for business process optimization","volume":"45","author":"Liu","year":"2020","journal-title":"J. Bus. Intell."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Moher, D., Liberati, A., Tetzlaff, J., Altman, D.G., and The PRISMA Group (2015). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA Statement. PLoS Med., 6.","DOI":"10.1371\/journal.pmed.1000097"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Ribeiro, M.T., Singh, S., and Guestrin, C. (2016, January 13\u201317). Why should I trust you? Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939778"},{"key":"ref_37","first-page":"5753","article-title":"XLNet: Generalized autoregressive pretraining for language understanding","volume":"33","author":"Yang","year":"2020","journal-title":"Proc. NeurIPS"},{"key":"ref_38","first-page":"35","article-title":"A comprehensive survey of evaluation metrics in natural language processing tasks","volume":"56","author":"Zhou","year":"2021","journal-title":"AI Rev."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Alice, M., Niccol\u00f2, C., Andrea, C.P., Francesco, B.A., and Massimiliano, P. (2025). Preventive Pathways for Healthy Ageing: A Systematic Literature Review. Geriatrics, 10.","DOI":"10.3390\/geriatrics10010031"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"n71","DOI":"10.1136\/bmj.n71","article-title":"The PRISMA 2020 statement: An updated guideline for reporting systematic reviews","volume":"372","author":"Page","year":"2021","journal-title":"BMJ"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Orgamb\u00eddez, A., Borrego, Y., Alcalde, F.J., and Dur\u00e1n, A. (2025). Moral Distress and Emotional Exhaustion in Healthcare Professionals: A Systematic Review and Meta-Analysis. Healthcare, 13.","DOI":"10.3390\/healthcare13040393"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Wendy, M., Vivien, S., Kamar, T., and Sarah, H. (2025). An Evaluation of Health Behavior Change Training for Health and Care Professionals in St. Helena. Healthcare, 13.","DOI":"10.3390\/healthcare13040435"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Joana, T., Neuza, R., Ewelina, C., Paula, C., Ana Catarina, G., Gra\u017cyna, B., Jo\u00e3o, A., Krystyna, J., Carlos, F., and Pedro, L. (2025). Current Approaches on Nurse-Performed Interventions to Prevent Healthcare-Acquired Infections: An Umbrella Review. Microorganisms, 13.","DOI":"10.3390\/microorganisms13020463"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Malcolm, K. (2025). ChatGPT Research: A Bibliometric Analysis Based on the Web of Science from 2023 to June 2024. Knowledge, 5.","DOI":"10.3390\/knowledge5010004"}],"container-title":["Computers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-431X\/14\/4\/148\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:14:00Z","timestamp":1760030040000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-431X\/14\/4\/148"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,14]]},"references-count":44,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,4]]}},"alternative-id":["computers14040148"],"URL":"https:\/\/doi.org\/10.3390\/computers14040148","relation":{},"ISSN":["2073-431X"],"issn-type":[{"value":"2073-431X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,14]]}}}