{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,23]],"date-time":"2025-08-23T00:08:44Z","timestamp":1755907724466,"version":"3.44.0"},"publisher-location":"New York, NY, USA","reference-count":16,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,12,15]],"date-time":"2023-12-15T00:00:00Z","timestamp":1702598400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,12,15]]},"DOI":"10.1145\/3639233.3639251","type":"proceedings-article","created":{"date-parts":[[2024,3,5]],"date-time":"2024-03-05T11:02:10Z","timestamp":1709636530000},"page":"203-209","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Enhancing Medical Specialty Assignment to Patients using NLP Techniques"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7034-5755","authenticated-orcid":false,"given":"Chris","family":"Solomou","sequence":"first","affiliation":[{"name":"Computer Science, University of York, United Kingdom"}]}],"member":"320","published-online":{"date-parts":[[2024,3,5]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"crossref","unstructured":"Ong LML de Haes JCJM Hoos AM Lammes FB. Doctor-patient communication: A review of the literature. Social Science & Medicine [Internet]. 1995 Apr;40(7):903\u201318. Available from: https:\/\/www.sciencedirect.com\/science\/article\/pii\/027795369400155M.","DOI":"10.1016\/0277-9536(94)00155-M"},{"key":"e_1_3_2_1_2_1","volume":"2019","author":"Shen J","unstructured":"Shen J, Zhang CJP, Jiang B, Chen J, Song J, Liu Z, Artificial Intelligence Versus Clinicians in Disease Diagnosis: Systematic Review. JMIR Medical Informatics. 2019 Aug 16;7(3):e10010.","journal-title":"Systematic Review. JMIR Medical Informatics."},{"volume-title":"4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI). 2021 Dec 16","author":"Solomou C","key":"e_1_3_2_1_3_1","unstructured":"Solomou C, Kazakov D. Utilizing Chest X-rays for Age Prediction and Gender Classification. 2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI). 2021 Dec 16."},{"key":"e_1_3_2_1_4_1","first-page":"503","volume":"2013","author":"Wallace BC","unstructured":"Wallace BC, Laws MB, Small K, Wilson IB, Trikalinos TA. Automatically Annotating Topics in Transcripts of Patient-Provider Interactions via Machine Learning. Medical Decision Making. 2013 Nov 27;34(4):503\u201312.","journal-title":"Machine Learning. Medical Decision Making."},{"key":"e_1_3_2_1_5_1","unstructured":"Devlin J Chang M-W Lee K Toutanova K. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Proceedings of the 2019 Conference of the North [Internet]. 2019; Available from: https:\/\/www.aclweb.org\/anthology\/N19-1423\/"},{"key":"e_1_3_2_1_6_1","volume-title":"BioBERT: a pre-trained biomedical language representation model for biomedical text mining","author":"Lee J","year":"2019","unstructured":"Lee J, Yoon W, Kim S, Kim D, Kim S, So CH, BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Wren J, editor. Bioinformatics [Internet]. 2019 Sep 10; Available from: https:\/\/academic.oup.com\/bioinformatics\/advance-article\/doi\/10.1093\/bioinformatics\/btz682\/5566506"},{"key":"e_1_3_2_1_7_1","first-page":"159714","volume":"202","author":"Roitero K","unstructured":"Roitero K, Portelli B, Popescu MH, Mea VD. DiLBERT: Cheap Embeddings for Disease Related Medical NLP. IEEE Access. 2021;9:159714\u201323.","journal-title":"IEEE Access."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"crossref","unstructured":"Zhong J Yi X. Categorizing Patient Disease into ICD-10 with Deep Learning for Semantic Text Classification. Recent Trends in Computational Intelligence. 2020 May 6.","DOI":"10.5772\/intechopen.91292"},{"key":"e_1_3_2_1_9_1","volume-title":"Roberta: A robustly optimized bert pretraining approach.\u00a0arXiv preprint arXiv:1907.11692","author":"Liu Y.","year":"2019","unstructured":"Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., ... & Stoyanov, V. (2019). Roberta: A robustly optimized bert pretraining approach.\u00a0arXiv preprint arXiv:1907.11692."},{"key":"e_1_3_2_1_10_1","first-page":"4","volume-title":"Proc. CLEF","author":"Seva M.","year":"2018","unstructured":"J. Seva, M. S\u00e4nger, and U. Leser, \u2018\u2018WBI at CLEF eHealth 2018 task 1: Language-independent ICD-10 coding using multi-lingual embeddings and recurrent neural networks,\u2019\u2019 in Proc. CLEF, 2018, pp. 1\u20134."},{"key":"e_1_3_2_1_11_1","unstructured":"Tara Boyle. 2018. Medical Transcriptions. Retrieved on 04\/2022 from https:\/\/www.kaggle.com\/datasets\/tboyle10\/medicaltranscriptions"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"crossref","unstructured":"Minaee S Kafieh R Sonka M Yazdani S Jamalipour Soufi G. Deep-COVID: Predicting COVID-19 From Chest X-Ray Images Using Deep Transfer Learning. Medical Image Analysis. 2020 Jul;101794.","DOI":"10.1016\/j.media.2020.101794"},{"key":"e_1_3_2_1_13_1","volume-title":"Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional study","author":"Zech JR","year":"2018","unstructured":"Zech JR, Badgeley MA, Liu M, Costa AB, Titano JJ, Oermann EK. Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional study. Sheikh A, editor. PLOS Medicine [Internet]. 2018 Nov 6;15(11):e1002683. Available from: https:\/\/journals.plos.org\/plosmedicine\/article?id=10.1371\/journal.pmed.1002683"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"crossref","unstructured":"Shelke A Inamdar M Shah V Tiwari A Hussain A Chafekar T Chest X-ray Classification Using Deep Learning for Automated COVID-19 Screening. Sn Computer Science [Internet]. 2021;2(4):300. Available from: https:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC8152712\/","DOI":"10.1007\/s42979-021-00695-5"},{"key":"e_1_3_2_1_15_1","first-page":"1","volume":"2022","author":"Gu Y","unstructured":"Gu Y, Tinn R, Cheng H, Lucas M, Usuyama N, Liu X, Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing. ACM Transactions on Computing for Healthcare. 2022 Jan 31;3(1):1\u201323.","journal-title":"Healthcare."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"crossref","unstructured":"Rasmy L Xiang Y Xie Z Tao C Zhi D. Med-BERT: pretrained contextualized embeddings on large-scale structured electronic health records for disease prediction. npj Digital Medicine. 2021 May 20;4(1)","DOI":"10.1038\/s41746-021-00455-y"}],"event":{"name":"NLPIR 2023: 2023 7th International Conference on Natural Language Processing and Information Retrieval","acronym":"NLPIR 2023","location":"Seoul Republic of Korea"},"container-title":["Proceedings of the 2023 7th International Conference on Natural Language Processing and Information Retrieval"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3639233.3639251","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3639233.3639251","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T19:55:10Z","timestamp":1755892510000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3639233.3639251"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,15]]},"references-count":16,"alternative-id":["10.1145\/3639233.3639251","10.1145\/3639233"],"URL":"https:\/\/doi.org\/10.1145\/3639233.3639251","relation":{},"subject":[],"published":{"date-parts":[[2023,12,15]]},"assertion":[{"value":"2024-03-05","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}