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This study focuses on enhancing predictive models for atrial fibrillation (AF) recurrence by extracting valuable data from electronic health records (EHRs) and unstructured medical reports. Although existing models show promise, their reliability is hampered by inaccuracies in coded data, with significant false positives and false negatives impacting their performance. To address this, the authors propose an automated system using DL and NLP techniques to process medical reports, extracting key predictive variables, and identifying new AF cases. The main purpose is to improve dataset reliability so future predictive models can respond more accurately Methods and Results: The study analyzed over one million discharge reports, applying regular expressions and DL tools to extract variables and identify AF onset. The performance of DL models, particularly a feedforward neural network combined with tf-idf, demonstrated high accuracy (0.986) in predicting AF onset. The application of DL tools on unstructured text reduced the error rate in AF identification by 50%, achieving an error rate of less than 2%. Conclusions: This work underscores the potential of AI in optimizing dataset accuracy to develop predictive models and consequently improving the healthcare predictions, offering valuable insights for research groups utilizing secondary data for predictive analytics in this particular setting.<\/jats:p>","DOI":"10.3390\/jcm14072297","type":"journal-article","created":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T03:35:28Z","timestamp":1743132928000},"page":"2297","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["The Application of Deep Learning Tools on Medical Reports to Optimize the Input of an Atrial-Fibrillation-Recurrence Predictive Model"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8742-0600","authenticated-orcid":false,"given":"Alain","family":"Garc\u00eda-Olea","sequence":"first","affiliation":[{"name":"Biobizkaia Research Institute, Basurto University Hospital, 48013 Bilbao, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-8202-1099","authenticated-orcid":false,"given":"Ane G","family":"Domingo-Aldama","sequence":"additional","affiliation":[{"name":"Hitz Group, Bilbao School of Engineering, University of the Basque Country, 48940 Bilbao, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-9610-322X","authenticated-orcid":false,"given":"Marcos","family":"Merino","sequence":"additional","affiliation":[{"name":"Hitz Group, Bilbao School of Engineering, University of the Basque Country, 48940 Bilbao, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2116-6611","authenticated-orcid":false,"given":"Koldo","family":"Gojenola","sequence":"additional","affiliation":[{"name":"Hitz Group, Bilbao School of Engineering, University of the Basque Country, 48940 Bilbao, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Josu","family":"Goikoetxea","sequence":"additional","affiliation":[{"name":"Hitz Group, Bilbao School of Engineering, University of the Basque Country, 48940 Bilbao, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aitziber","family":"Atutxa","sequence":"additional","affiliation":[{"name":"Hitz Group, Bilbao School of Engineering, University of the Basque Country, 48940 Bilbao, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6081-2041","authenticated-orcid":false,"given":"Jos\u00e9 Miguel","family":"Ormaetxe","sequence":"additional","affiliation":[{"name":"Biobizkaia Research Institute, Basurto University Hospital, 48013 Bilbao, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,3,27]]},"reference":[{"key":"ref_1","first-page":"2773","article-title":"Prediction of new onset atrial fibrillation recurrence or persistence with artificial intelligence: First insights of the PRAFAI study","volume":"43","year":"2022","journal-title":"Eur. 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Inf., 121.","DOI":"10.1016\/j.jbi.2021.103875"},{"key":"ref_8","first-page":"100","article-title":"Measuring the effect of different types of unsupervised word representations on Medical Named Entity Recognition","volume":"129","author":"Casillas","year":"2019","journal-title":"J. Biomed. Inf."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Hossain, E. (2023). Natural Language Processing in Electronic Health Records in relation to healthcare decision-making: A systematic review. Comput. Biol. Med., 155.","DOI":"10.1016\/j.compbiomed.2023.106649"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1055\/s-0041-1726522","article-title":"A Review of Recent Work in Transfer Learning and Domain Adaptation for Natural Language Processing of Electronic Health Records","volume":"30","author":"Laparra","year":"2021","journal-title":"Yearb. Med. Inform."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Mohanty, S. (2024). Redefining the blanking period after pulsed-field ablation in patients with atrial fibrillation. Heart Rhythm, in press.","DOI":"10.1016\/j.jacep.2024.06.006"}],"container-title":["Journal of Clinical Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2077-0383\/14\/7\/2297\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:03:34Z","timestamp":1760029414000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2077-0383\/14\/7\/2297"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,27]]},"references-count":11,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2025,4]]}},"alternative-id":["jcm14072297"],"URL":"https:\/\/doi.org\/10.3390\/jcm14072297","relation":{},"ISSN":["2077-0383"],"issn-type":[{"type":"electronic","value":"2077-0383"}],"subject":[],"published":{"date-parts":[[2025,3,27]]}}}