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To leverage the potential of such systems in high-risk applications, we need large, structured tabular datasets on which we can build transparent feature-based models. While part of the EHR already contains structured information (e.g. diagnosis codes, medications, and lab results), much of the information is contained within unstructured text (e.g. discharge summaries and nursing notes). In this work, we propose a method for multi-modal patient-level information extraction that leverages both the tabular features available in the patient\u2019s EHR (using an expert-informed Bayesian network) as well as clinical notes describing the patient\u2019s symptoms (using neural text classifiers). We propose the use of\n                    <jats:italic>virtual evidence<\/jats:italic>\n                    augmented with a\n                    <jats:italic>consistency node<\/jats:italic>\n                    to provide an interpretable, probabilistic fusion of the models\u2019 predictions. The consistency node improves the calibration of the final predictions compared to virtual evidence alone, allowing the Bayesian network to better adjust the neural classifier\u2019s output to handle missing information and resolve contradictions between the tabular and text data. We show the potential of our method on the SimSUM dataset, a simulated benchmark linking tabular EHRs with clinical notes through expert knowledge.\n                  <\/jats:p>","DOI":"10.1007\/s10489-026-07322-x","type":"journal-article","created":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T04:24:54Z","timestamp":1781238294000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Patient-level information extraction by consistent integration of textual and tabular evidence with Bayesian networks"],"prefix":"10.1007","volume":"56","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6064-0788","authenticated-orcid":false,"given":"Paloma","family":"Rabaey","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-0003-9960","authenticated-orcid":false,"given":"Adrick","family":"Tench","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1097-4987","authenticated-orcid":false,"given":"Stefan","family":"Heytens","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9901-5768","authenticated-orcid":false,"given":"Thomas","family":"Demeester","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,12]]},"reference":[{"key":"7322_CR1","doi-asserted-by":"crossref","unstructured":"Rabaey, P., Arno, H., Heytens, S., Demeester, T.: SimSUM \u2013 Simulated Benchmark with Structured and Unstructured Medical Records. arXiv (2024). https:\/\/arxiv.org\/abs\/2409.08936","DOI":"10.1186\/s13326-025-00341-6"},{"issue":"5","key":"7322_CR2","doi-asserted-by":"publisher","first-page":"1007","DOI":"10.1093\/jamia\/ocv180","volume":"23","author":"E Ford","year":"2016","unstructured":"Ford E, Carroll JA, Smith HE, Scott D, Cassell JA (2016) Extracting information from the text of electronic medical records to improve case detection: a systematic review. 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