{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T15:00:48Z","timestamp":1781276448623,"version":"3.54.1"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031665370","type":"print"},{"value":"9783031665387","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-66538-7_24","type":"book-chapter","created":{"date-parts":[[2024,7,25]],"date-time":"2024-07-25T17:02:07Z","timestamp":1721926927000},"page":"229-250","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Clinical Reasoning over\u00a0Tabular Data and\u00a0Text with\u00a0Bayesian Networks"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6064-0788","authenticated-orcid":false,"given":"Paloma","family":"Rabaey","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Johannes","family":"Deleu","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":[[2024,7,25]]},"reference":[{"key":"24_CR1","doi-asserted-by":"crossref","unstructured":"Ankan, A., Panda, A.: pgmpy: Probabilistic graphical models using Python. In: Proceedings of the 14th Python in Science Conference, pp. 6\u201311 (2015)","DOI":"10.25080\/Majora-7b98e3ed-001"},{"issue":"3","key":"24_CR2","doi-asserted-by":"publisher","first-page":"638","DOI":"10.1111\/jep.12852","volume":"24","author":"B Chin-Yee","year":"2018","unstructured":"Chin-Yee, B., Upshur, R.: Clinical judgement in the era of big data and predictive analytics. J. Eval. Clin. Pract. 24(3), 638\u2013645 (2018)","journal-title":"J. Eval. Clin. Pract."},{"key":"24_CR3","doi-asserted-by":"crossref","unstructured":"Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, p. 233\u2013240 (2006)","DOI":"10.1145\/1143844.1143874"},{"key":"24_CR4","doi-asserted-by":"crossref","unstructured":"Edye, E.O., et al.: Applying Bayesian networks to help physicians diagnose respiratory diseases in the context of COVID-19 pandemic. In: 2021 IEEE URUCON, pp. 368\u2013371 (2021)","DOI":"10.1109\/URUCON53396.2021.9647280"},{"issue":"5","key":"24_CR5","doi-asserted-by":"publisher","first-page":"1007","DOI":"10.1093\/jamia\/ocv180","volume":"23","author":"E Ford","year":"2016","unstructured":"Ford, E., Carroll, J.A., Smith, H.E., Scott, D., Cassell, J.A.: Extracting information from the text of electronic medical records to improve case detection: a systematic review. J. Am. Med. Inform. Assoc. 23(5), 1007\u20131015 (2016)","journal-title":"J. Am. Med. Inform. Assoc."},{"issue":"1","key":"24_CR6","doi-asserted-by":"publisher","first-page":"4","DOI":"10.5811\/westjem.2016.11.33191","volume":"18","author":"LD Gruppen","year":"2017","unstructured":"Gruppen, L.D.: Clinical reasoning: defining it, teaching it, assessing it, studying it. West J. Emerg. Med. 18(1), 4\u20137 (2017)","journal-title":"West J. Emerg. Med."},{"key":"24_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2021.102079","volume":"116","author":"E Kyrimi","year":"2021","unstructured":"Kyrimi, E., et al.: Bayesian networks in healthcare: what is preventing their adoption? Artif. Intell. Med. 116, 102079 (2021)","journal-title":"Artif. Intell. Med."},{"key":"24_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2021.102108","volume":"117","author":"E Kyrimi","year":"2021","unstructured":"Kyrimi, E., McLachlan, S., Dube, K., Neves, M.R., Fahmi, A., Fenton, N.: A comprehensive scoping review of Bayesian networks in healthcare: past, present and future. Artif. Intell. Med. 117, 102108 (2021)","journal-title":"Artif. Intell. Med."},{"key":"24_CR9","unstructured":"Manhaeve, R., Dumancic, S., Kimmig, A., Demeester, T., De\u00a0Raedt, L.: DeepProbLog: neural probabilistic logic programming. In: Advances in Neural Information Processing Systems (NeurIPS), vol.\u00a031 (2018)"},{"key":"24_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.artint.2023.104062","volume":"328","author":"G Marra","year":"2024","unstructured":"Marra, G., Duman\u010di\u0107, S., Manhaeve, R., De Raedt, L.: From statistical relational to neurosymbolic artificial intelligence: a survey. Artif. Intell. 328, 104062 (2024)","journal-title":"Artif. Intell."},{"key":"24_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2020.101912","volume":"107","author":"S McLachlan","year":"2020","unstructured":"McLachlan, S., Dube, K., Hitman, G.A., Fenton, N.E., Kyrimi, E.: Bayesian networks in healthcare: distribution by medical condition. Artif. Intell. Med. 107, 101912 (2020)","journal-title":"Artif. Intell. Med."},{"key":"24_CR12","doi-asserted-by":"publisher","first-page":"494","DOI":"10.1016\/j.eswa.2018.09.034","volume":"116","author":"G Mujtaba","year":"2019","unstructured":"Mujtaba, G., et al.: Clinical text classification research trends: systematic literature review and open issues. Expert Syst. Appl. 116, 494\u2013520 (2019)","journal-title":"Expert Syst. Appl."},{"issue":"1","key":"24_CR13","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1097\/ACM.0000000000001421","volume":"92","author":"GR Norman","year":"2017","unstructured":"Norman, G.R., Monteiro, S.D., Sherbino, J., Ilgen, J.S., Schmidt, H.G., Mamede, S.: The causes of errors in clinical reasoning: cognitive biases, knowledge deficits, and dual process thinking. Acad. Med. 92(1), 23\u201330 (2017)","journal-title":"Acad. Med."},{"key":"24_CR14","unstructured":"Ouyang, L., Wu, J., Jiang, X., Almeida, D., et\u00a0al.: Training language models to follow instructions with human feedback. In: Advances in Neural Information Processing Systems, vol.\u00a035, pp. 27730\u201327744 (2022)"},{"issue":"5","key":"24_CR15","doi-asserted-by":"publisher","first-page":"584","DOI":"10.1016\/j.cmi.2019.09.009","volume":"26","author":"N Peiffer-Smadja","year":"2020","unstructured":"Peiffer-Smadja, N., Rawson, T., Ahmad, R., Buchard, A., et al.: Machine learning for clinical decision support in infectious diseases: a narrative review of current applications. Clin. Microbiol. Infect. 26(5), 584\u2013595 (2020)","journal-title":"Clin. Microbiol. Infect."},{"issue":"5","key":"24_CR16","doi-asserted-by":"publisher","DOI":"10.1136\/bmjopen-2016-011664","volume":"6","author":"SJ Price","year":"2016","unstructured":"Price, S.J., Stapley, S.A., Shephard, E., Barraclough, K., Hamilton, W.T.: Is omission of free text records a possible source of data loss and bias in clinical practice research datalink studies? A case\u2013control study. BMJ Open 6(5), e011664 (2016)","journal-title":"BMJ Open"},{"key":"24_CR17","unstructured":"Remy, F., Demuynck, K., Demeester, T.: BioLORD: semantic textual representations fusing LLM and clinical knowledge graph insights. arXiv preprint (2023)"},{"issue":"1","key":"24_CR18","doi-asserted-by":"publisher","first-page":"5994","DOI":"10.1038\/s41598-017-05778-z","volume":"7","author":"M Rotmensch","year":"2017","unstructured":"Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Sci. Rep. 7(1), 5994 (2017)","journal-title":"Sci. Rep."},{"key":"24_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2020.103544","volume":"110","author":"L Sterckx","year":"2020","unstructured":"Sterckx, L., Vandewiele, G., Dehaene, I., Janssens, O., et al.: Clinical information extraction for preterm birth risk prediction. J. Biomed. Inform. 110, 103544 (2020)","journal-title":"J. Biomed. Inform."},{"key":"24_CR20","unstructured":"Strauss, S.E., Glasziou, P., Richardson, W.S., Haynes, R.B.: Evidence-based medicine. In: How to Practice and Teach EBM, vol.\u00a05. Elsevier, Amsterdam (2018)"},{"key":"24_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2019.112821","volume":"138","author":"J Yanase","year":"2019","unstructured":"Yanase, J., Triantaphyllou, E.: A systematic survey of computer-aided diagnosis in medicine: past and present developments. Expert Syst. Appl. 138, 112821 (2019)","journal-title":"Expert Syst. Appl."},{"key":"24_CR22","doi-asserted-by":"publisher","first-page":"703","DOI":"10.2147\/AMEP.S213492","volume":"10","author":"S Yazdani","year":"2019","unstructured":"Yazdani, S., Hoseini Abardeh, M.: Five decades of research and theorization on clinical reasoning: a critical review. Adv. Med. Educ. Pract. 10, 703\u2013716 (2019)","journal-title":"Adv. Med. Educ. Pract."},{"issue":"4","key":"24_CR23","first-page":"177","volume":"5","author":"S Yazdani","year":"2017","unstructured":"Yazdani, S., Hosseinzadeh, M., Hosseini, F.: Models of clinical reasoning with a focus on general practice: a critical review. J. Adv. Med. Educ. Prof. 5(4), 177\u2013184 (2017)","journal-title":"J. Adv. Med. Educ. Prof."},{"issue":"5","key":"24_CR24","doi-asserted-by":"publisher","first-page":"815","DOI":"10.1136\/amiajnl-2013-001934","volume":"21","author":"Y Ye","year":"2014","unstructured":"Ye, Y., Tsui, F., Wagner, M., Espino, J., Li, Q.: Influenza detection from emergency department reports using natural language processing and Bayesian network classifiers. J. Am. Med. Inform. Assoc. 21(5), 815\u2013823 (2014)","journal-title":"J. Am. Med. Inform. Assoc."},{"issue":"1","key":"24_CR25","doi-asserted-by":"publisher","first-page":"280","DOI":"10.1186\/s12911-020-01297-6","volume":"20","author":"D Zhang","year":"2020","unstructured":"Zhang, D., Yin, C., Zeng, J., Yuan, X., Zhang, P.: Combining structured and unstructured data for predictive models: a deep learning approach. BMC Med. Inform. Decis. Mak. 20(1), 280 (2020)","journal-title":"BMC Med. Inform. Decis. Mak."}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-66538-7_24","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,25]],"date-time":"2024-07-25T17:04:43Z","timestamp":1721927083000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-66538-7_24"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031665370","9783031665387"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-66538-7_24","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"25 July 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"AIME","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Intelligence in Medicine","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Salt Lake City, UT","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"USA","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 July 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 July 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aime2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/aime24.aimedicine.info\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}