{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T04:09:34Z","timestamp":1754021374631,"version":"3.40.3"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031240935"},{"type":"electronic","value":"9783031240942"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[[2022]]},"DOI":"10.1007\/978-3-031-24094-2_7","type":"book-chapter","created":{"date-parts":[[2023,1,28]],"date-time":"2023-01-28T20:03:08Z","timestamp":1674936188000},"page":"104-116","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Deep Learning Based Approach to\u00a0Automate Clinical Coding of\u00a0Electronic Health Records"],"prefix":"10.1007","author":[{"given":"Ashutosh","family":"Kumar","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2087-1666","authenticated-orcid":false,"given":"Santosh Singh","family":"Rathore","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,1,29]]},"reference":[{"issue":"10","key":"7_CR1","doi-asserted-by":"publisher","first-page":"2287","DOI":"10.1093\/jamia\/ocab143","volume":"28","author":"M Wang","year":"2021","unstructured":"Wang, M., Wang, M., Fei, Y., Yang, Y., Walker, J., Mostafa, J.: A systematic review of automatic text summarization for biomedical literature and EHRs. J. Am. Med. Inform. Assoc. 28(10), 2287\u20132297 (2021)","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"7_CR2","doi-asserted-by":"crossref","unstructured":"Subotin, M., Davis, A.: A system for predicting ICD-10-pcs codes from electronic health records. In: 2014 Proceedings of BioNLP, pp. 59\u201367 (2014)","DOI":"10.3115\/v1\/W14-3409"},{"key":"7_CR3","doi-asserted-by":"crossref","unstructured":"J., Teng, F., Ma, Z., Chen, L., Huang, L., Li, X.: A multi-channel convolutional neural network for ICD coding. In: 2019 IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering (ISKE), pp. 1178\u20131184. IEEE (2019)","DOI":"10.1109\/ISKE47853.2019.9170305"},{"key":"7_CR4","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Liu, J., Razavian, N.: BERT-XML: large scale automated ICD coding using BERT pretraining. arXiv preprint arXiv:2006.03685 (2020)","DOI":"10.18653\/v1\/2020.clinicalnlp-1.3"},{"key":"7_CR5","doi-asserted-by":"crossref","unstructured":"Xie, X., Xiong, Y., Yu, P.S., Zhu, Y.: EHR coding with multi-scale feature attention and structured knowledge graph propagation. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp. 649\u2013658 (2019)","DOI":"10.1145\/3357384.3357897"},{"key":"7_CR6","doi-asserted-by":"publisher","first-page":"705","DOI":"10.1016\/j.ijcard.2015.03.075","volume":"187","author":"B Rubbo","year":"2015","unstructured":"Rubbo, B., et al.: Use of electronic health records to ascertain, validate and phenotype acute myocardial infarction: a systematic review and recommendations. Int. J. Cardiol. 187, 705\u2013711 (2015)","journal-title":"Int. J. Cardiol."},{"issue":"4","key":"7_CR7","doi-asserted-by":"publisher","first-page":"1323","DOI":"10.1109\/JBHI.2017.2743824","volume":"22","author":"A Atutxa","year":"2017","unstructured":"Atutxa, A., P\u00e9rez, A., Casillas, A.: Machine learning approaches on diagnostic term encoding with the ICD for clinical documentation. IEEE J. Biomed. Health Inform. 22(4), 1323\u20131329 (2017)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"7_CR8","unstructured":"Xu, K., et al.: Multimodal machine learning for automated ICD coding. In: Machine Learning for Healthcare Conference, pp. 197\u2013215. PMLR (2019)"},{"issue":"1","key":"7_CR9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13075-018-1791-9","volume":"21","author":"L Jamian","year":"2019","unstructured":"Jamian, L., Wheless, L., Crofford, L.J., Barnado, A.: Rule-based and machine learning algorithms identify patients with systemic sclerosis accurately in the electronic health record. Arthritis Res. Therp. 21(1), 1\u20139 (2019)","journal-title":"Arthritis Res. Therp."},{"key":"7_CR10","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1016\/j.ijmedinf.2018.09.021","volume":"120","author":"F Catling","year":"2018","unstructured":"Catling, F., Spithourakis, G.P., Riedel, S.: Towards automated clinical coding. Int. J. Med. Inform. 120, 50\u201361 (2018)","journal-title":"Int. J. Med. Inform."},{"key":"7_CR11","doi-asserted-by":"crossref","unstructured":"Sonabend, A., et al.: Automated ICD coding via unsupervised knowledge integration (unite). Int. J. Med. Inform. 139, 104135 (2020)","DOI":"10.1016\/j.ijmedinf.2020.104135"},{"key":"7_CR12","doi-asserted-by":"crossref","unstructured":"Diao, X., et al.: Automated ICD coding for primary diagnosis via clinically interpretable machine learning. Int. J. Med. Inform. 153, 104543 (2021)","DOI":"10.1016\/j.ijmedinf.2021.104543"},{"key":"7_CR13","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1016\/j.jbi.2018.04.007","volume":"83","author":"S Purushotham","year":"2018","unstructured":"Purushotham, S., Meng, C., Che, Z., Liu, Y.: Benchmarking deep learning models on large healthcare datasets. J. Biomed. Inform. 83, 112\u2013134 (2018)","journal-title":"J. Biomed. Inform."},{"key":"7_CR14","unstructured":"Shi, H., Xie, P., Hu, Z., Zhang, M., Xing, E.P.: Towards automated ICD coding using deep learning. arXiv preprint arXiv:1711.04075 (2017)"},{"key":"7_CR15","doi-asserted-by":"crossref","unstructured":"Dong, H., Su\u00e1rez-Paniagua, V., Whiteley, W., Honghan, W.: Explainable automated coding of clinical notes using hierarchical label-wise attention networks and label embedding initialisation. J. Biomed. Inform. 116, 103728 (2021)","DOI":"10.1016\/j.jbi.2021.103728"},{"issue":"2","key":"7_CR16","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1136\/amiajnl-2013-002159","volume":"21","author":"A Perotte","year":"2014","unstructured":"Perotte, A., Pivovarov, R., Natarajan, K., Weiskopf, N., Wood, F., Elhadad, N.: Diagnosis code assignment: models and evaluation metrics. J. Am. Med. Inform. Assoc. 21(2), 231\u2013237 (2014)","journal-title":"J. Am. Med. Inform. Assoc."},{"issue":"9","key":"7_CR17","doi-asserted-by":"publisher","first-page":"1892","DOI":"10.1093\/jamia\/ocab090","volume":"28","author":"Y Zhang","year":"2021","unstructured":"Zhang, Y., Zhang, Y., Qi, P., Manning, C.D., Langlotz, C.P.: Biomedical and clinical English model packages for the stanza python NLP library. J. Am. Med. Inform. Assoc. 28(9), 1892\u20131899 (2021)","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"7_CR18","unstructured":"Ayyar, S., Don, O., Iv, W.: Tagging patient notes with ICD-9 codes. In: Proceedings of the 29th Conference on Neural Information Processing Systems, pp. 1\u20138 (2016)"},{"key":"7_CR19","doi-asserted-by":"crossref","unstructured":"Mullenbach, J., Wiegreffe, S., Duke, J., Sun, J., Eisenstein, J.: Explainable prediction of medical codes from clinical text. arXiv preprint arXiv:1802.05695 (2018)","DOI":"10.18653\/v1\/N18-1100"},{"issue":"15","key":"7_CR20","doi-asserted-by":"publisher","first-page":"5262","DOI":"10.3390\/app10155262","volume":"10","author":"E Moons","year":"2020","unstructured":"Moons, E., Khanna, A., Akkasi, A., Moens, M.-F.: A comparison of deep learning methods for ICD coding of clinical records. Appl. Sci. 10(15), 5262 (2020)","journal-title":"Appl. Sci."},{"issue":"3","key":"7_CR21","doi-asserted-by":"publisher","first-page":"e223","DOI":"10.1016\/j.ijrobp.2021.07.771","volume":"111","author":"Z Jiang","year":"2021","unstructured":"Jiang, Z., et al.: A light gradient boosting machine-enabled early prediction of cardiotoxicity for breast cancer patients. Int. J. Radiat. Oncol. Biol. Phys. 111(3), e223 (2021)","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"issue":"10","key":"7_CR22","doi-asserted-by":"publisher","first-page":"3804","DOI":"10.1109\/JBHI.2021.3099755","volume":"25","author":"SA Moqurrab","year":"2021","unstructured":"Moqurrab, S.A., Ayub, U., Anjum, A., Asghar, S., Srivastava, G.: An accurate deep learning model for clinical entity recognition from clinical notes. IEEE J. Biomed. Health Inform. 25(10), 3804\u20133811 (2021)","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"1","key":"7_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12913-020-05207-4","volume":"20","author":"MY Wei","year":"2020","unstructured":"Wei, M.Y., Luster, J.E., Chan, C.-L., Min, L.: Comprehensive review of ICD-9 code accuracies to measure multimorbidity in administrative data. BMC Health Serv. Res. 20(1), 1\u201311 (2020)","journal-title":"BMC Health Serv. Res."},{"key":"7_CR24","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Lu, Z., Wang, S.: Unsupervised feature selection via transformed auto-encoder. Knowl.-Based Syst. 215, 106748 (2021)","DOI":"10.1016\/j.knosys.2021.106748"},{"key":"7_CR25","doi-asserted-by":"crossref","unstructured":"Tenney, I., Das, D., Pavlick, E.: BERT rediscovers the classical NLP pipeline. arXiv preprint arXiv:1905.05950 (2019)","DOI":"10.18653\/v1\/P19-1452"},{"issue":"8","key":"7_CR26","doi-asserted-by":"publisher","first-page":"5789","DOI":"10.1007\/s10462-021-09958-2","volume":"54","author":"FA Acheampong","year":"2021","unstructured":"Acheampong, F.A., Nunoo-Mensah, H., Chen, W.: Transformer models for text-based emotion detection: a review of BERT-based approaches. Artif. Intell. Rev. 54(8), 5789\u20135829 (2021)","journal-title":"Artif. Intell. Rev."},{"key":"7_CR27","doi-asserted-by":"crossref","unstructured":"Peng, Y., Chen, Q., Lu, Z.: An empirical study of multi-task learning on BERT for biomedical text mining. arXiv preprint arXiv:2005.02799 (2020)","DOI":"10.18653\/v1\/2020.bionlp-1.22"},{"issue":"1","key":"7_CR28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/sdata.2016.35","volume":"3","author":"AEW Johnson","year":"2016","unstructured":"Johnson, A.E.W., et al.: MIMIC-III, a freely accessible critical care database. Sci. Data 3(1), 1\u20139 (2016)","journal-title":"Sci. Data"}],"container-title":["Lecture Notes in Computer Science","Big Data Analytics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-24094-2_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,13]],"date-time":"2024-10-13T06:00:17Z","timestamp":1728799217000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-24094-2_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031240935","9783031240942"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-24094-2_7","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"29 January 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"BDA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Big Data Analytics","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hyderabad","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 December 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 December 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"bigda2022a","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/bda2022.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"36","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"7","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"7","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"19% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}