{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,13]],"date-time":"2025-05-13T20:48:07Z","timestamp":1747169287190},"reference-count":18,"publisher":"Springer Science and Business Media LLC","issue":"S10","license":[{"start":{"date-parts":[[2019,12,1]],"date-time":"2019-12-01T00:00:00Z","timestamp":1575158400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2019,12,27]],"date-time":"2019-12-27T00:00:00Z","timestamp":1577404800000},"content-version":"vor","delay-in-days":26,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"published-print":{"date-parts":[[2019,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Family history (FH) information, including family members, side of family of family members (i.e., maternal or paternal), living status of family members, observations (diseases) of family members, etc., is very important in the decision-making process of disorder diagnosis and treatment. However FH information cannot be used directly by computers as it is always embedded in unstructured text in electronic health records (EHRs). In order to extract FH information form clinical text, there is a need of natural language processing (NLP). In the BioCreative\/OHNLP2018 challenge, there is a task regarding FH extraction (i.e., task1), including two subtasks: (1) entity identification, identifying family members and their observations (diseases) mentioned in clinical text; (2) family history extraction, extracting side of family of family members, living status of family members, and observations of family members. For this task, we propose a system based on deep joint learning methods to extract FH information. Our system achieves the highest F1- scores of 0.8901 on subtask1 and 0.6359 on subtask2, respectively.<\/jats:p><\/jats:sec>","DOI":"10.1186\/s12911-019-0995-5","type":"journal-article","created":{"date-parts":[[2019,12,27]],"date-time":"2019-12-27T08:02:42Z","timestamp":1577433762000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Family history information extraction via deep joint learning"],"prefix":"10.1186","volume":"19","author":[{"given":"Xue","family":"Shi","sequence":"first","affiliation":[]},{"given":"Dehuan","family":"Jiang","sequence":"additional","affiliation":[]},{"given":"Yuanhang","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Xiaolong","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Qingcai","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Jun","family":"Yan","sequence":"additional","affiliation":[]},{"given":"Buzhou","family":"Tang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,12,27]]},"reference":[{"key":"995_CR1","unstructured":"Sijia Liu, Majid Rastegar Mojarad, Yanshan Wang, Liwei Wang, Feichen Shen, Sunyang Fu, Hongfang Liu, Overview of the BioCreative\/OHNLP 2018 Family History Extraction Task, BioCreative 2018 Proceedings."},{"key":"995_CR2","unstructured":"Hochreiter S, Schmidhuber J. LSTM can solve hard long time lag problems[C]\/\/Advances in neural information processing systems. 1997: 473-479."},{"key":"995_CR3","unstructured":"Huang Z, Xu W, Yu K, et al. Bidirectional LSTM-CRF Models for Sequence Tagging. arXiv: 1508.01991, Computation and Language, 2015."},{"key":"995_CR4","doi-asserted-by":"crossref","unstructured":"Sahu S K, Anand A, Oruganty K, et al. Relation extraction from clinical texts using domain invariant convolutional neural network[C]\/\/Proceedings of the 15th Workshop on Biomedical Natural Language Processing, 2016:206-215.","DOI":"10.18653\/v1\/W16-2928"},{"key":"995_CR5","doi-asserted-by":"crossref","unstructured":"Luo Y. Recurrent neural networks for classifying relations in clinical notes[J]. Journal of biomedical informatics, 2017, 72:85-95.","DOI":"10.1016\/j.jbi.2017.07.006"},{"key":"995_CR6","doi-asserted-by":"crossref","unstructured":"Uzuner \u00d6, Solti I, Cadag E. Extracting medication information from clinical text. 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