{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T13:10:11Z","timestamp":1732713011679,"version":"3.28.2"},"reference-count":62,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,8,29]],"date-time":"2024-08-29T00:00:00Z","timestamp":1724889600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,8,29]],"date-time":"2024-08-29T00:00:00Z","timestamp":1724889600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Ministry for Economic Affairs, Industry, Climate Action and Energy of the State of North-Rhine-Westphalia, Germany","award":["5-2011-0041\/2","5-2011-0041\/2","5-2011-0041\/2","5-2011-0041\/2","5-2011-0041\/2","5-2011-0041\/2","5-2011-0041\/2","5-2011-0041\/2","5-2011-0041\/2","5-2011-0041\/2"]},{"name":"Fraunhofer-Institut f\u00fcr Intelligente Analyse- und Informationssysteme IAIS"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"abstract":"Abstract<\/jats:title>Background<\/jats:title>Medical text, as part of an electronic health record, is an essential information source in healthcare. Although natural language processing (NLP) techniques for medical text are developing fast, successful transfer into clinical practice has been rare. Especially the hospital domain offers great potential while facing several challenges including many documents per patient, multiple departments and complex interrelated processes.<\/jats:p><\/jats:sec>Methods<\/jats:title>In this work, we survey relevant literature to identify and classify approaches which exploit NLP in the clinical context. Our contribution involves a systematic mapping of related research onto a prototypical patient journey in the hospital, along which medical documents are created, processed and consumed by hospital staff and patients themselves. Specifically, we reviewed which dataset types, dataset languages, model architectures and tasks are researched in current clinical NLP research. Additionally, we extract and analyze major obstacles during development and implementation. We discuss options to address them and argue for a focus on bias mitigation and model explainability.<\/jats:p><\/jats:sec>Results<\/jats:title>While a patient\u2019s hospital journey produces a significant amount of structured and unstructured documents, certain steps and documents receive more research attention than others. Diagnosis, Admission and Discharge are clinical patient steps that are researched often across the surveyed paper. In contrast, our findings reveal significant under-researched areas such as Treatment, Billing, After Care, and Smart Home. Leveraging NLP in these stages can greatly enhance clinical decision-making and patient outcomes. Additionally, clinical NLP models are mostly based on radiology reports, discharge letters and admission notes, even though we have shown that many other documents are produced throughout the patient journey. There is a significant opportunity in analyzing a wider range of medical documents produced throughout the patient journey to improve the applicability and impact of NLP in healthcare.<\/jats:p><\/jats:sec>Conclusions<\/jats:title>Our findings suggest that there is a significant opportunity to leverage NLP approaches to advance clinical decision-making systems, as there remains a considerable understudied potential for the analysis of patient journey data.<\/jats:p><\/jats:sec>","DOI":"10.1186\/s12911-024-02641-w","type":"journal-article","created":{"date-parts":[[2024,8,29]],"date-time":"2024-08-29T11:03:38Z","timestamp":1724929418000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["From admission to discharge: a systematic review of clinical natural language processing along the patient journey"],"prefix":"10.1186","volume":"24","author":[{"given":"Katrin","family":"Klug","sequence":"first","affiliation":[]},{"given":"Katharina","family":"Beckh","sequence":"additional","affiliation":[]},{"given":"Dario","family":"Antweiler","sequence":"additional","affiliation":[]},{"given":"Nilesh","family":"Chakraborty","sequence":"additional","affiliation":[]},{"given":"Giulia","family":"Baldini","sequence":"additional","affiliation":[]},{"given":"Katharina","family":"Laue","sequence":"additional","affiliation":[]},{"given":"Ren\u00e9","family":"Hosch","sequence":"additional","affiliation":[]},{"given":"Felix","family":"Nensa","sequence":"additional","affiliation":[]},{"given":"Martin","family":"Schuler","sequence":"additional","affiliation":[]},{"given":"Sven","family":"Giesselbach","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,8,29]]},"reference":[{"key":"2641_CR1","doi-asserted-by":"crossref","unstructured":"Paa\u00df G, Giesselbach S. Foundation models for natural language processing\u2013pre-trained language models integrating media. 2023. arXiv preprint arXiv:2302.08575.","DOI":"10.1007\/978-3-031-23190-2"},{"key":"2641_CR2","doi-asserted-by":"crossref","unstructured":"Zhou C, Li Q, Li C, Yu J, Liu Y, Wang G, et\u00a0al. A comprehensive survey on pretrained foundation models: a history from bert to chatgpt. 2023. arXiv preprint arXiv:2302.09419.","DOI":"10.1007\/s13042-024-02443-6"},{"key":"2641_CR3","doi-asserted-by":"publisher","unstructured":"Wu H, Wang M, Wu J, Francis F, Chang YH, Shavick A, et\u00a0al. A survey on clinical natural language processing in the United Kingdom from 2007 to 2022. NPJ Digit Med. 2022;5(1). https:\/\/doi.org\/10.1038\/s41746-022-00730-6.","DOI":"10.1038\/s41746-022-00730-6"},{"issue":"7956","key":"2641_CR4","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1038\/s41586-023-05881-4","volume":"616","author":"M Moor","year":"2023","unstructured":"Moor M, Banerjee O, Abad ZSH, Krumholz HM, Leskovec J, Topol EJ, et al. Foundation models for generalist medical artificial intelligence. Nature. 2023;616(7956):259\u201365.","journal-title":"Nature."},{"key":"2641_CR5","first-page":"26","volume":"9","author":"K Danladi Garba","year":"2018","unstructured":"Danladi Garba K, Yahaya I. Significance and challenges of medical records: a systematic literature review. Adv Librariansh. 2018;9:26\u201331.","journal-title":"Adv Librariansh."},{"key":"2641_CR6","doi-asserted-by":"publisher","unstructured":"Reyes-Ortiz JA, Gonzalez-Beltran BA, Gallardo-Lopez L. Clinical decision support systems: a survey of NLP-based approaches from unstructured data. In: 2015 26th International Workshop on Database and Expert Systems Applications (DEXA). IEEE; 2015. pp. 163\u20137. https:\/\/doi.org\/10.1109\/dexa.2015.47.","DOI":"10.1109\/dexa.2015.47"},{"key":"2641_CR7","doi-asserted-by":"publisher","first-page":"e37805","DOI":"10.2196\/37805","volume":"11","author":"S Tamang","year":"2023","unstructured":"Tamang S, Humbert-Droz M, Gianfrancesco M, Izadi Z, Schmajuk G, Yazdany J. Practical Considerations for Developing Clinical Natural Language Processing Systems for Population Health Management and Measurement. JMIR Med Inform. 2023;11:e37805. https:\/\/doi.org\/10.2196\/37805.","journal-title":"JMIR Med Inform."},{"key":"2641_CR8","doi-asserted-by":"publisher","unstructured":"Pandey B, Kumar Pandey D, Pratap Mishra B, Rhmann W. A comprehensive survey of deep learning in the field of medical imaging and medical natural language processing: Challenges and research directions. J King Saud Univ Comput Inform Sci. 2022;34(8, Part A):5083\u20135099. https:\/\/doi.org\/10.1016\/j.jksuci.2021.01.007.","DOI":"10.1016\/j.jksuci.2021.01.007"},{"key":"2641_CR9","doi-asserted-by":"crossref","unstructured":"Valizadeh M, Parde N. The AI Doctor Is In: A Survey of Task-Oriented Dialogue Systems for Healthcare Applications. In: Muresan S, Nakov P, Villavicencio A, editors. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Dublin: Association for Computational Linguistics; 2022. pp. 6638\u20136660. https:\/\/aclanthology.org\/2022.acl-long.458. Accessed 1 Jan 2023.","DOI":"10.18653\/v1\/2022.acl-long.458"},{"key":"2641_CR10","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1016\/j.jbi.2017.07.012","volume":"73","author":"K Kreimeyer","year":"2017","unstructured":"Kreimeyer K, Foster M, Pandey A, Arya N, Halford G, Jones SF, et al. Natural language processing systems for capturing and standardizing unstructured clinical information: A systematic review. J Biomed Inform. 2017;73:14\u201329.","journal-title":"J Biomed Inform."},{"key":"2641_CR11","doi-asserted-by":"crossref","unstructured":"Nazir T, Mushhood Ur\u00a0Rehman M, Asghar MR, Kalia JS. Artificial intelligence assisted acute patient journey. Front Artif Intell. 2022;5:962165.","DOI":"10.3389\/frai.2022.962165"},{"issue":"18","key":"2641_CR12","doi-asserted-by":"publisher","first-page":"6586","DOI":"10.3390\/ijerph17186586","volume":"17","author":"M Arias","year":"2020","unstructured":"Arias M, Rojas E, Aguirre S, Cornejo F, Munoz-Gama J, Sep\u00falveda M, et al. Mapping the patient\u2019s journey in healthcare through process mining. Int J Environ Res Public Health. 2020;17(18):6586.","journal-title":"Int J Environ Res Public Health."},{"issue":"3","key":"2641_CR13","doi-asserted-by":"publisher","first-page":"209","DOI":"10.3322\/caac.21660","volume":"71","author":"H Sung","year":"2021","unstructured":"Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021;71(3):209\u201349.","journal-title":"CA Cancer J Clin."},{"key":"2641_CR14","doi-asserted-by":"crossref","unstructured":"Postmus PE, Kerr KM, Oudkerk M, Senan S, Waller DA, Vansteenkiste JF, et\u00a0al. Early and locally advanced non-small-cell lung cancer (NSCLC): ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol. 2017;28, Suppl 4:iv1\u2013iv21.","DOI":"10.1093\/annonc\/mdx222"},{"key":"2641_CR15","doi-asserted-by":"crossref","unstructured":"Planchard D, Popat S, Kerr KM, Novello S, Smit EF, Faivre-Finn C, et\u00a0al. Metastatic non-small cell lung cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol. 2018;29, Suppl 4:iv192\u2013iv237.","DOI":"10.1093\/annonc\/mdy275"},{"issue":"10","key":"2641_CR16","doi-asserted-by":"publisher","first-page":"1993","DOI":"10.3390\/healthcare10101993","volume":"10","author":"S Abdulmalek","year":"2022","unstructured":"Abdulmalek S, Nasir A, Jabbar WA, Almuhaya MAM, Bairagi AK, Khan MAM, et al. IoT-Based Healthcare-Monitoring System towards Improving Quality of Life: A Review. Healthcare. 2022;10(10):1993.","journal-title":"Healthcare."},{"key":"2641_CR17","doi-asserted-by":"publisher","unstructured":"Wornow M, Xu Y, Thapa R, Patel B, Steinberg E, Fleming S, et\u00a0al. The shaky foundations of large language models and foundation models for electronic health records. NPJ Digit Med. 2023;6(1). https:\/\/doi.org\/10.1038\/s41746-023-00879-8.","DOI":"10.1038\/s41746-023-00879-8"},{"key":"2641_CR18","doi-asserted-by":"crossref","unstructured":"Rojas M, Dunstan J, Villena F. Clinical Flair: A Pre-Trained Language Model for Spanish Clinical Natural Language Processing. In: Proceedings of the 4th Clinical Natural Language Processing Workshop. Seattle: Association for Computational Linguistics; 2022. pp. 87\u201392. https:\/\/aclanthology.org\/2022.clinicalnlp-1.9. Accessed 1 Jan 2023.","DOI":"10.18653\/v1\/2022.clinicalnlp-1.9"},{"key":"2641_CR19","doi-asserted-by":"crossref","unstructured":"Pilan I, Brekke PH, Dahl FA, Gundersen T, Husby H, Nytr\u00f8 \u00d8, et al. Classification of Syncope Cases in Norwegian Medical Records. In: Proceedings of the 3rd Clinical Natural Language Processing Workshop. Online: Association for Computational Linguistics; 2020. pp. 79\u201384. https:\/\/aclanthology.org\/2020.clinicalnlp-1.9. Accessed 1\u00a0Jan 2023.","DOI":"10.18653\/v1\/2020.clinicalnlp-1.9"},{"key":"2641_CR20","doi-asserted-by":"crossref","unstructured":"Ehsani R, Niemi T, Khullar G, Leivo T. Clinical Data Classification using Conditional Random Fields and Neural Parsing for Morphologically Rich Languages. In: Proceedings of the 2nd Clinical Natural Language Processing Workshop. Minneapolis: Association for Computational Linguistics; 2019. pp. 149\u2013155. https:\/\/aclanthology.org\/W19-1919. Accessed 1\u00a0Jan 2023.","DOI":"10.18653\/v1\/W19-1919"},{"key":"2641_CR21","unstructured":"Magge A, Klein A, Miranda-Escalada A, Al-garadi MA, Alimova I, Miftahutdinov Z, et al., editors. Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task. Mexico City: Association for Computational Linguistics; 2021. https:\/\/aclanthology.org\/2021.smm4h-1.0. Accessed 1\u00a0Jan 2023."},{"key":"2641_CR22","unstructured":"Gonzalez-Hernandez G, Weissenbacher D, editors. Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task. Gyeongju: Association for Computational Linguistics; 2022. https:\/\/aclanthology.org\/2022.smm4h-1.0. Accessed 1\u00a0Jan 2023."},{"key":"2641_CR23","unstructured":"Jin-Dong K, Claire N, Robert B, Louise D, editors. Proceedings of the 5th Workshop on BioNLP Open Shared Tasks. Hong Kong: Association for Computational Linguistics; 2019. https:\/\/aclanthology.org\/D19-5700. Accessed 1\u00a0Jan 2023."},{"key":"2641_CR24","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, et\u00a0al. Attention Is All You Need. CoRR. 2017. arXiv:1706.03762."},{"key":"2641_CR25","doi-asserted-by":"crossref","unstructured":"Kim G, Cho K. Length-Adaptive Transformer: Train Once with Length Drop, Use Anytime with Search. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). Online: Association for Computational Linguistics; 2021. pp. 6501\u20136511. https:\/\/aclanthology.org\/2021.acl-long.508. Accessed 1\u00a0Jan 2023.","DOI":"10.18653\/v1\/2021.acl-long.508"},{"key":"2641_CR26","doi-asserted-by":"crossref","unstructured":"Patel P, Davey D, Panchal V, Pathak P. Annotation of a Large Clinical Entity Corpus. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Brussels: Association for Computational Linguistics; 2018. pp. 2033\u20132042. https:\/\/aclanthology.org\/D18-1228. Accessed 1\u00a0Jan 2023.","DOI":"10.18653\/v1\/D18-1228"},{"key":"2641_CR27","unstructured":"Raghavan P, Patwardhan S. Question Answering on Electronic Medical Records. In: Proceedings of the 2016 Summit on Clinical Research Informatics. San Francisco: AMIA; 2016. http:\/\/knowledge.amia.org\/amia-59309-cri2016-1.3011827\/t004-1.3012641\/f004-1.3012642\/a103-1.3012719\/a105-1.3012714."},{"key":"2641_CR28","doi-asserted-by":"crossref","unstructured":"Pampari A, Raghavan P, Liang J, Peng J. emrQA: A Large Corpus for Question Answering on Electronic Medical Records. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Brussels: Association for Computational Linguistics; 2018. pp. 2357\u20132368. https:\/\/aclanthology.org\/D18-1258. Accessed 1\u00a0Jan 2023.","DOI":"10.18653\/v1\/D18-1258"},{"key":"2641_CR29","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1038\/s41591-018-0307-0","volume":"25","author":"J He","year":"2019","unstructured":"He J, Baxter SL, Xu J, Xu J, Zhou X, Zhang K. The practical implementation of artificial intelligence technologies in medicine. Nat Med. 2019;25:30\u20136.","journal-title":"Nat Med."},{"key":"2641_CR30","unstructured":"Tonekaboni S, Joshi S, McCradden MD, Goldenberg A. What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use. In: Proceedings of the 4th Machine Learning for Healthcare Conference, vol. 106. PMLR; 2019. pp. 359\u2013380."},{"issue":"4","key":"2641_CR31","doi-asserted-by":"publisher","first-page":"1746","DOI":"10.1109\/TETC.2022.3171314","volume":"10","author":"Y Jia","year":"2022","unstructured":"Jia Y, McDermid J, Lawton T, Habli I. The Role of Explainability in Assuring Safety of Machine Learning in Healthcare. IEEE Trans Emerg Top Comput. 2022;10(4):1746\u201360. https:\/\/doi.org\/10.1109\/TETC.2022.3171314.","journal-title":"IEEE Trans Emerg Top Comput."},{"issue":"11","key":"2641_CR32","doi-asserted-by":"publisher","first-page":"e14758","DOI":"10.1111\/ijcp.14758","volume":"75","author":"O Kobo","year":"2021","unstructured":"Kobo O, Brown SA, Nafee T, Mohamed MO, Sharma K, Istanbuly S, et al. Impact of malignancy on In-hospital mortality, stratified by the cause of admission: An analysis of 67 million patients from the National Inpatient Sample. Int J Clin Pract. 2021;75(11):e14758.","journal-title":"Int J Clin Pract."},{"key":"2641_CR33","unstructured":"Savaliya V, Bhatnagar A, Bhavsar N, Singh M. Innovators@SMM4H\u201922: An Ensembles Approach for Stance and Premise Classification of COVID-19 Health Mandates Tweets. In: Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task. Gyeongju: Association for Computational Linguistics; 2022. pp. 126\u2013129. https:\/\/aclanthology.org\/2022.smm4h-1.35. Accessed 1\u00a0Jan 2023."},{"key":"2641_CR34","doi-asserted-by":"crossref","unstructured":"Khanpour H, Caragea C. Fine-Grained Emotion Detection in Health-Related Online Posts. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Brussels: Association for Computational Linguistics; 2018. pp. 1160\u20131166. https:\/\/aclanthology.org\/D18-1147. Accessed 1\u00a0Jan 2023.","DOI":"10.18653\/v1\/D18-1147"},{"key":"2641_CR35","doi-asserted-by":"crossref","unstructured":"Jiang Z, Levitan SI, Zomick J, Hirschberg J. Detection of Mental Health from Reddit via Deep Contextualized Representations. In: Proceedings of the 11th International Workshop on Health Text Mining and Information Analysis. Online: Association for Computational Linguistics; 2020. pp. 147\u2013156. https:\/\/aclanthology.org\/2020.louhi-1.16. Accessed 1\u00a0Jan 2023.","DOI":"10.18653\/v1\/2020.louhi-1.16"},{"key":"2641_CR36","unstructured":"Kulkarni A, Hengle A, Kulkarni P, Marathe M. Cluster Analysis of Online Mental Health Discourse using Topic-Infused Deep Contextualized Representations. In: Proceedings of the 12th International Workshop on Health Text Mining and Information Analysis. Online: Association for Computational Linguistics; 2021. pp. 83\u201393. https:\/\/aclanthology.org\/2021.louhi-1.10. Accessed 1\u00a0Jan 2023."},{"key":"2641_CR37","doi-asserted-by":"crossref","unstructured":"Alimova I, Tutubalina E. Detecting Adverse Drug Reactions from Biomedical Texts with Neural Networks. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop. Florence: Association for Computational Linguistics; 2019. pp. 415\u2013421. https:\/\/aclanthology.org\/P19-2058. Accessed 1\u00a0Jan 2023.","DOI":"10.18653\/v1\/P19-2058"},{"key":"2641_CR38","doi-asserted-by":"crossref","unstructured":"Portelli B, Lenzi E, Chersoni E, Serra G, Santus E. BERT Prescriptions to Avoid Unwanted Headaches: A Comparison of Transformer Architectures for Adverse Drug Event Detection. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume. Online: Association for Computational Linguistics; 2021. pp. 1740\u20131747. https:\/\/aclanthology.org\/2021.eacl-main.149. Accessed 1\u00a0Jan 2023.","DOI":"10.18653\/v1\/2021.eacl-main.149"},{"key":"2641_CR39","doi-asserted-by":"crossref","unstructured":"Mesbah S, Yang J, Sips RJ, Valle Torre M, Lofi C, Bozzon A, et al. Training Data Augmentation for Detecting Adverse Drug Reactions in User-Generated Content. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Hong Kong: Association for Computational Linguistics; 2019. pp. 2349\u20132359. https:\/\/aclanthology.org\/D19-1239. Accessed 1\u00a0Jan 2023.","DOI":"10.18653\/v1\/D19-1239"},{"key":"2641_CR40","doi-asserted-by":"crossref","unstructured":"Miftahutdinov Z, Tutubalina E. Deep Neural Models for Medical Concept Normalization in User-Generated Texts. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop. Florence: Association for Computational Linguistics; 2019. pp. 393\u2013399. https:\/\/aclanthology.org\/P19-2055. Accessed 1\u00a0Jan 2023.","DOI":"10.18653\/v1\/P19-2055"},{"key":"2641_CR41","unstructured":"Santurkar S, Tsipras D, Madry A. Breeds: Benchmarks for subpopulation shift. 2020. arXiv preprint arXiv:2008.04859."},{"key":"2641_CR42","doi-asserted-by":"crossref","unstructured":"Shim H, Lowet D, Luca S, Vanrumste B. An exploratory data analysis: the performance differences of a medical code prediction system on different demographic groups. In: Proceedings of the 4th Clinical Natural Language Processing Workshop. Seattle: Association for Computational Linguistics; 2022. pp. 93\u2013102. https:\/\/aclanthology.org\/2022.clinicalnlp-1.10\/.","DOI":"10.18653\/v1\/2022.clinicalnlp-1.10"},{"key":"2641_CR43","doi-asserted-by":"crossref","unstructured":"Holderness E, Cawkwell P, Bolton K, Pustejovsky J, Hall MH. Distinguishing clinical sentiment: The importance of domain adaptation in psychiatric patient health records. 2019. arXiv preprint arXiv:1904.03225.","DOI":"10.18653\/v1\/W19-1915"},{"key":"2641_CR44","doi-asserted-by":"crossref","unstructured":"Wang Z, Qu Y, Chen L, Shen J, Zhang W, Zhang S, et\u00a0al. Label-aware double transfer learning for cross-specialty medical named entity recognition. 2018. arXiv preprint arXiv:1804.09021.","DOI":"10.18653\/v1\/N18-1001"},{"key":"2641_CR45","doi-asserted-by":"crossref","unstructured":"Liu M, Han J, Zhang H, Song Y. Domain adaptation for disease phrase matching with adversarial networks. In: Proceedings of the BioNLP 2018 workshop. Melbourne: Association for Computational Linguistics; 2018. pp. 137\u2013141. https:\/\/aclanthology.org\/W18-2315.","DOI":"10.18653\/v1\/W18-2315"},{"issue":"11","key":"2641_CR46","doi-asserted-by":"publisher","first-page":"4793","DOI":"10.1109\/TNNLS.2020.3027314","volume":"32","author":"E Tjoa","year":"2020","unstructured":"Tjoa E, Guan C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE Trans Neural Netw Learn Syst. 2020;32(11):4793\u2013813.","journal-title":"IEEE Trans Neural Netw Learn Syst."},{"key":"2641_CR47","unstructured":"Doshi-Velez F, Kim B. Towards a rigorous science of interpretable machine learning. 2017. arXiv preprint arXiv:1702.08608."},{"key":"2641_CR48","doi-asserted-by":"crossref","unstructured":"Beckh K, M\u00fcller S, Jakobs M, Toborek V, Tan H, Fischer R, et\u00a0al. Harnessing Prior Knowledge for Explainable Machine Learning: An Overview. In: First IEEE Conference on Secure and Trustworthy Machine Learning. 2023. pp. 450\u2013463. In Press","DOI":"10.1109\/SaTML54575.2023.00038"},{"key":"2641_CR49","doi-asserted-by":"publisher","first-page":"110787","DOI":"10.1016\/j.ejrad.2023.110787","volume":"162","author":"K Borys","year":"2023","unstructured":"Borys K, Schmitt YA, Nauta M, Seifert C, Kr\u00e4mer N, Friedrich CM, et al. Explainable AI in medical imaging: An overview for clinical practitioners \u2013 Saliency-based XAI approaches. Eur J Radiol. 2023;162:110787. https:\/\/doi.org\/10.1016\/j.ejrad.2023.110787.","journal-title":"Eur J Radiol."},{"key":"2641_CR50","doi-asserted-by":"publisher","first-page":"110786","DOI":"10.1016\/j.ejrad.2023.110786","volume":"162","author":"K Borys","year":"2023","unstructured":"Borys K, Schmitt YA, Nauta M, Seifert C, Kr\u00e4mer N, Friedrich CM, et al. Explainable AI in medical imaging: An overview for clinical practitioners \u2013 Beyond saliency-based XAI approaches. Eur J Radiol. 2023;162:110786. https:\/\/doi.org\/10.1016\/j.ejrad.2023.110786.","journal-title":"Eur J Radiol."},{"key":"2641_CR51","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12911-019-1002-x","volume":"20","author":"J Amann","year":"2020","unstructured":"Amann J, Blasimme A, Vayena E, Frey D, Madai VI. Explainability for artificial intelligence in healthcare: a multidisciplinary perspective. BMC Med Inform Dec Making. 2020;20:1\u20139.","journal-title":"BMC Med Inform Dec Making."},{"key":"2641_CR52","unstructured":"Shen H, Huang TH. Explaining the Road Not Taken. 2021. arXiv preprint arXiv:2103.14973."},{"key":"2641_CR53","doi-asserted-by":"crossref","unstructured":"Jacovi A, Goldberg Y. Towards Faithfully Interpretable NLP Systems: How Should We Define and Evaluate Faithfulness? In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Online: Association for Computational Linguistics; 2020. pp. 4198\u20134205. https:\/\/aclanthology.org\/2020.acl-main.386.","DOI":"10.18653\/v1\/2020.acl-main.386"},{"key":"2641_CR54","doi-asserted-by":"publisher","unstructured":"Nauta M, Trienes J, Pathak S, Nguyen E, Peters M, Schmitt Y, et al. From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI. ACM Comput Surv. 2023. https:\/\/doi.org\/10.1145\/3583558.","DOI":"10.1145\/3583558"},{"issue":"11","key":"2641_CR55","doi-asserted-by":"publisher","first-page":"e745","DOI":"10.1016\/S2589-7500(21)00208-9","volume":"3","author":"M Ghassemi","year":"2021","unstructured":"Ghassemi M, Oakden-Rayner L, Beam AL. The false hope of current approaches to explainable artificial intelligence in health care. Lancet Digit Health. 2021;3(11):e745\u201350.","journal-title":"Lancet Digit Health."},{"key":"2641_CR56","doi-asserted-by":"crossref","unstructured":"Van Aken B, Herrmann S, L\u00f6ser A. What Do You See in this Patient? Behavioral Testing of Clinical NLP Models. In: Proceedings of the 4th Clinical Natural Language Processing Workshop. Seattle: Association for Computational Linguistics; 2022. pp. 63\u201373. https:\/\/aclanthology.org\/2022.clinicalnlp-1.7.","DOI":"10.18653\/v1\/2022.clinicalnlp-1.7"},{"key":"2641_CR57","doi-asserted-by":"crossref","unstructured":"Shwartz V, Choi Y. Do Neural Language Models Overcome Reporting Bias? In: Scott D, Bel N, Zong C, editors. Proceedings of the 28th International Conference on Computational Linguistics. Barcelona: International Committee on Computational Linguistics; 2020. pp. 6863\u20136870. https:\/\/aclanthology.org\/2020.coling-main.605. Accessed 1\u00a0Mar 2023.","DOI":"10.18653\/v1\/2020.coling-main.605"},{"issue":"5","key":"2641_CR58","doi-asserted-by":"publisher","first-page":"e185","DOI":"10.2196\/jmir.9134","volume":"20","author":"RA Verheij","year":"2018","unstructured":"Verheij RA, Curcin V, Delaney BC, McGilchrist MM. Possible Sources of Bias in Primary Care Electronic Health Record Data Use and Reuse. J Med Internet Res. 2018;20(5):e185. https:\/\/doi.org\/10.2196\/jmir.9134.","journal-title":"J Med Internet Res."},{"key":"2641_CR59","doi-asserted-by":"publisher","unstructured":"Khadzhynov D, Schmidt D, Hardt J, Rauch G, Gocke P, Eckardt KU, et al. The Incidence of Acute Kidney Injury and Associated Hospital Mortality. Deutsches \u00c4rzteblatt Int. 2019. https:\/\/doi.org\/10.3238\/arztebl.2019.0397.","DOI":"10.3238\/arztebl.2019.0397"},{"key":"2641_CR60","doi-asserted-by":"publisher","unstructured":"Liu F, Ge S, Wu X. Competence-based Multimodal Curriculum Learning for Medical Report Generation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). Association for Computational Linguistics; 2021. pp. 3001\u20133012. https:\/\/doi.org\/10.18653\/v1\/2021.acl-long.234.","DOI":"10.18653\/v1\/2021.acl-long.234"},{"key":"2641_CR61","unstructured":"Barocas S, Hardt M, Narayanan A. Fairness and Machine Learning: Limitations and Opportunities. fairmlbook.org; 2019. http:\/\/www.fairmlbook.org. Accessed 1\u00a0Mar 2023."},{"key":"2641_CR62","doi-asserted-by":"crossref","unstructured":"Chen J, Berlot-Attwell I, Wang X, Hossain S, Rudzicz F. Exploring Text Specific and Blackbox Fairness Algorithms in Multimodal Clinical NLP. In: Proceedings of the 3rd Clinical Natural Language Processing Workshop. Online: Association for Computational Linguistics; 2020. pp. 301\u2013312. https:\/\/aclanthology.org\/2020.clinicalnlp-1.33.","DOI":"10.18653\/v1\/2020.clinicalnlp-1.33"}],"container-title":["BMC Medical Informatics and Decision Making"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-024-02641-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12911-024-02641-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-024-02641-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T12:31:33Z","timestamp":1732710693000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedinformdecismak.biomedcentral.com\/articles\/10.1186\/s12911-024-02641-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,29]]},"references-count":62,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["2641"],"URL":"https:\/\/doi.org\/10.1186\/s12911-024-02641-w","relation":{},"ISSN":["1472-6947"],"issn-type":[{"type":"electronic","value":"1472-6947"}],"subject":[],"published":{"date-parts":[[2024,8,29]]},"assertion":[{"value":"22 December 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 August 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 August 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"238"}}