{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T22:58:38Z","timestamp":1742943518462,"version":"3.40.3"},"publisher-location":"Cham","reference-count":35,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031263897"},{"type":"electronic","value":"9783031263903"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-26390-3_29","type":"book-chapter","created":{"date-parts":[[2023,3,16]],"date-time":"2023-03-16T09:04:46Z","timestamp":1678957486000},"page":"505-520","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["AutoMap: Automatic Medical Code Mapping for\u00a0Clinical Prediction Model Deployment"],"prefix":"10.1007","author":[{"given":"Zhenbang","family":"Wu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cao","family":"Xiao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lucas M.","family":"Glass","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"David M.","family":"Liebovitz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jimeng","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,3,17]]},"reference":[{"key":"29_CR1","doi-asserted-by":"publisher","unstructured":"Artetxe, M., Labaka, G., Agirre, E.: Learning bilingual word embeddings with (almost) no bilingual data. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, Vancouver, Canada, (Volume 1: Long Papers), pp. 451\u2013462. Association for Computational Linguistics (2017). https:\/\/doi.org\/10.18653\/v1\/P17-1042. https:\/\/www.aclweb.org\/anthology\/P17-1042","DOI":"10.18653\/v1\/P17-1042"},{"key":"29_CR2","doi-asserted-by":"publisher","unstructured":"Artetxe, M., Labaka, G., Agirre, E.: A robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, Melbourne, Australia, (Volume 1: Long Papers), pp. 789\u2013798. Association for Computational Linguistics (2018). https:\/\/doi.org\/10.18653\/v1\/P18-1073. https:\/\/www.aclweb.org\/anthology\/P18-1073","DOI":"10.18653\/v1\/P18-1073"},{"key":"29_CR3","doi-asserted-by":"publisher","unstructured":"Birkhead, G.S., Klompas, M., Shah, N.R.: Uses of electronic health records for public health surveillance to advance public health. Ann. Rev. Public Health 36(1), 345\u2013359 (2015). https:\/\/doi.org\/10.1146\/annurev-publhealth-031914-122747, pMID: 25581157","DOI":"10.1146\/annurev-publhealth-031914-122747"},{"key":"29_CR4","doi-asserted-by":"crossref","unstructured":"Bodenreider, O.: The Unified Medical Language System (UMLS): integrating biomedical terminology. Nucleic Acids Res. 32(Database issue), D267\u2013270 (2004)","DOI":"10.1093\/nar\/gkh061"},{"key":"29_CR5","unstructured":"Choi, E., Bahadori, M.T., Kulas, J.A., Schuetz, A., Stewart, W.F., Sun, J.: Retain: an interpretable predictive model for healthcare using reverse time attention mechanism. In: Proceedings of the 30th International Conference on Neural Information Processing Systems, NIPS 2016, pp. 3512\u20133520. Curran Associates Inc., Red Hook (2016)"},{"key":"29_CR6","unstructured":"Choi, E., Bahadori, M.T., Schuetz, A., Stewart, W.F., Sun, J.: Doctor AI: predicting clinical events via recurrent neural networks. In: Doshi-Velez, F., Fackler, J., Kale, D., Wallace, B., Wiens, J. (eds.) Proceedings of the 1st Machine Learning for Healthcare Conference. Proceedings of Machine Learning Research, vol.\u00a056, pp. 301\u2013318. PMLR, Northeastern University, Boston, MA, USA (2016). https:\/\/proceedings.mlr.press\/v56\/Choi16.html"},{"key":"29_CR7","doi-asserted-by":"publisher","unstructured":"Choi, E., et al.: Multi-layer representation learning for medical concepts. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016, pp. 1495\u20131504. Association for Computing Machinery, New York (2016). https:\/\/doi.org\/10.1145\/2939672.2939823","DOI":"10.1145\/2939672.2939823"},{"key":"29_CR8","doi-asserted-by":"publisher","unstructured":"Choi, E., et al.: Learning the graphical structure of electronic health records with graph convolutional transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 606\u2013613 (2020). https:\/\/doi.org\/10.1609\/aaai.v34i01.5400","DOI":"10.1609\/aaai.v34i01.5400"},{"key":"29_CR9","unstructured":"Conneau, A., Lample, G., Ranzato, M., Denoyer, L., J\u00e9gou, H.: Word translation without parallel data (2018)"},{"key":"29_CR10","doi-asserted-by":"crossref","unstructured":"Gupta, P., Malhotra, P., Narwariya, J., Vig, L., Shroff, G.: Transfer learning for clinical time series analysis using deep neural networks (2019)","DOI":"10.1007\/s41666-019-00062-3"},{"key":"29_CR11","doi-asserted-by":"publisher","unstructured":"Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver\u00a0Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Sci. Data 6(1) (2019). https:\/\/doi.org\/10.1038\/s41597-019-0103-9","DOI":"10.1038\/s41597-019-0103-9"},{"key":"29_CR12","first-page":"574","volume":"216","author":"G Hripcsak","year":"2015","unstructured":"Hripcsak, G., et al.: Observational health data sciences and informatics (OHDSI): opportunities for observational researchers. Stud. Health Technol. Inform. 216, 574\u2013578 (2015)","journal-title":"Stud. Health Technol. Inform."},{"key":"29_CR13","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2016.35","volume":"3","author":"AE Johnson","year":"2016","unstructured":"Johnson, A.E., et al.: Mimic-iii, a freely accessible critical care database. Sci. Data 3, 160035 (2016)","journal-title":"Sci. Data"},{"key":"29_CR14","doi-asserted-by":"publisher","unstructured":"Li, Y., et al.: BEHRT: transformer for electronic health records. Sci. Rep. 10(1), 7155 (2020). https:\/\/doi.org\/10.1038\/s41598-020-62922-y","DOI":"10.1038\/s41598-020-62922-y"},{"key":"29_CR15","doi-asserted-by":"publisher","unstructured":"Luo, J., Ye, M., Xiao, C., Ma, F.: HiTANet: hierarchical time-aware attention networks for risk prediction on electronic health records, pp. 647\u2013656. Association for Computing Machinery, New York (2020). https:\/\/doi.org\/10.1145\/3394486.3403107","DOI":"10.1145\/3394486.3403107"},{"key":"29_CR16","doi-asserted-by":"publisher","unstructured":"Ma, F., Chitta, R., Zhou, J., You, Q., Sun, T., Gao, J.: Dipole: diagnosis prediction in healthcare via attention-based bidirectional recurrent neural networks. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2017, pp. 1903\u20131911. Association for Computing Machinery, New York (2017). https:\/\/doi.org\/10.1145\/3097983.3098088","DOI":"10.1145\/3097983.3098088"},{"key":"29_CR17","doi-asserted-by":"crossref","unstructured":"Ma, L., et al.: AdaCare: explainable clinical health status representation learning via scale-adaptive feature extraction and recalibration. In: The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, The Thirty-Second Innovative Applications of Artificial Intelligence Conference, IAAI 2020, The Tenth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2020, New York, NY, USA, 7\u201312 February 2020, pp. 825\u2013832. AAAI Press (2020). https:\/\/aaai.org\/ojs\/index.php\/AAAI\/article\/view\/5427","DOI":"10.1609\/aaai.v34i01.5427"},{"key":"29_CR18","unstructured":"Ma, L., et al.: CovidCare: transferring knowledge from existing EMR to emerging epidemic for interpretable prognosis (2020)"},{"key":"29_CR19","unstructured":"Maas, A.L., Hannun, A.Y., Ng, A.Y.: Rectifier nonlinearities improve neural network acoustic models. In: ICML Workshop on Deep Learning for Audio, Speech and Language Processing (2013)"},{"issue":"5","key":"29_CR20","doi-asserted-by":"publisher","first-page":"899","DOI":"10.1093\/jamia\/ocv189","volume":"23","author":"JC Mandel","year":"2016","unstructured":"Mandel, J.C., Kreda, D.A., Mandl, K.D., Kohane, I.S., Ramoni, R.B.: SMART on FHIR: a standards-based, interoperable apps platform for electronic health records. J. Am. Med. Inform. Assoc. 23(5), 899\u2013908 (2016)","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"29_CR21","unstructured":"Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space (2013)"},{"key":"29_CR22","unstructured":"Mikolov, T., Le, Q.V., Sutskever, I.: Exploiting similarities among languages for machine translation (2013)"},{"issue":"1","key":"29_CR23","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1109\/JBHI.2016.2633963","volume":"21","author":"P Nguyen","year":"2017","unstructured":"Nguyen, P., Tran, T., Wickramasinghe, N., Venkatesh, S.: $$\\texttt{Deepr}$$: a convolutional net for medical records. IEEE J. Biomed. Health Inform. 21(1), 22\u201330 (2017). https:\/\/doi.org\/10.1109\/JBHI.2016.2633963","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"29_CR24","doi-asserted-by":"crossref","unstructured":"Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: Empirical Methods in Natural Language Processing (EMNLP), pp. 1532\u20131543 (2014). http:\/\/www.aclweb.org\/anthology\/D14-1162","DOI":"10.3115\/v1\/D14-1162"},{"issue":"1","key":"29_CR25","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2018.178","volume":"5","author":"TJ Pollard","year":"2018","unstructured":"Pollard, T.J., Johnson, A.E.W., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eICU collaborative research database, a freely available multi-center database for critical care research. Sci. Data 5(1), 180178 (2018). https:\/\/doi.org\/10.1038\/sdata.2018.178","journal-title":"Sci. Data"},{"key":"29_CR26","doi-asserted-by":"publisher","unstructured":"Rajkomar, A., et al.: Scalable and accurate deep learning with electronic health records. NPJ Digit. Med. 1(1), 18 (2018). https:\/\/doi.org\/10.1038\/s41746-018-0029-1. http:\/\/www.nature.com\/articles\/s41746-018-0029-1","DOI":"10.1038\/s41746-018-0029-1"},{"key":"29_CR27","doi-asserted-by":"publisher","unstructured":"Sch\u00f6nemann, P.H.: A generalized solution of the orthogonal procrustes problem. Psychometrika 31(1), 1\u201310 (1966). https:\/\/doi.org\/10.1007\/BF02289451","DOI":"10.1007\/BF02289451"},{"key":"29_CR28","doi-asserted-by":"publisher","unstructured":"Shang, J., Xiao, C., Ma, T., Li, H., Sun, J.: GameNet: graph augmented memory networks for recommending medication combination. In: The Thirty-Third AAAI Conference on Artificial Intelligence, AAAI 2019, The Thirty-First Innovative Applications of Artificial Intelligence Conference, IAAI 2019, The Ninth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019, Honolulu, Hawaii, USA, 27 January\u20131 February 2019, pp. 1126\u20131133. AAAI Press (2019). https:\/\/doi.org\/10.1609\/aaai.v33i01.33011126","DOI":"10.1609\/aaai.v33i01.33011126"},{"key":"29_CR29","doi-asserted-by":"publisher","unstructured":"S\u00f8gaard, A., Ruder, S., Vuli\u0107, I.: On the limitations of unsupervised bilingual dictionary induction. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, Melbourne, Australia, (Volume 1: Long Papers), pp. 778\u2013788. Association for Computational Linguistics (2018). https:\/\/doi.org\/10.18653\/v1\/P18-1072. https:\/\/www.aclweb.org\/anthology\/P18-1072","DOI":"10.18653\/v1\/P18-1072"},{"key":"29_CR30","unstructured":"Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(56), 1929\u20131958 (2014). http:\/\/jmlr.org\/papers\/v15\/srivastava14a.html"},{"key":"29_CR31","doi-asserted-by":"crossref","unstructured":"Tang, S., Davarmanesh, P., Song, Y., Koutra, D., Sjoding, M.W., Wiens, J.: Democratizing EHR analyses with FIDDLE: a flexible data- driven preprocessing pipeline for structured clinical data. J. Am. Med. Inform. Assoc. 14 (2020)","DOI":"10.1093\/jamia\/ocaa139"},{"key":"29_CR32","doi-asserted-by":"publisher","unstructured":"Wojcik, B.E., Stein, C.R., Devore, R.B., Hassell, L.H.: The challenge of mapping between two medical coding systems. Mil. Med. 171(11), 1128\u20131136 (2006). https:\/\/doi.org\/10.7205\/MILMED.171.11.1128. https:\/\/academic.oup.com\/milmed\/article\/171\/11\/1128-1136\/4578127","DOI":"10.7205\/MILMED.171.11.1128"},{"issue":"10","key":"29_CR33","doi-asserted-by":"publisher","first-page":"1419","DOI":"10.1093\/jamia\/ocy068","volume":"25","author":"C Xiao","year":"2018","unstructured":"Xiao, C., Choi, E., Sun, J.: Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review. J. Am. Med. Inform. Assoc. 25(10), 1419\u20131428 (2018). https:\/\/doi.org\/10.1093\/jamia\/ocy068","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"29_CR34","doi-asserted-by":"crossref","unstructured":"Zhang, C., Gao, X., Ma, L., Wang, Y., Wang, J., Tang, W.: GRASP: generic framework for health status representation learning based on incorporating knowledge from similar patients. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, no. 1, pp. 715\u2013723 (2021). https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/16152","DOI":"10.1609\/aaai.v35i1.16152"},{"key":"29_CR35","doi-asserted-by":"publisher","unstructured":"Zhang, H., Dullerud, N., Seyyed-Kalantari, L., Morris, Q., Joshi, S., Ghassemi, M.: An empirical framework for domain generalization in clinical settings. In: Proceedings of the Conference on Health, Inference, and Learning, CHIL 2021, pp. 279\u2013290. Association for Computing Machinery, New York (2021). https:\/\/doi.org\/10.1145\/3450439.3451878","DOI":"10.1145\/3450439.3451878"}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-26390-3_29","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,13]],"date-time":"2023-10-13T07:08:18Z","timestamp":1697180898000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-26390-3_29"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031263897","9783031263903"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-26390-3_29","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"17 March 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Grenoble","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","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 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 September 2022","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":"ecml2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2022.ecmlpkdd.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-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":"1060","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":"236","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":"0","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":"22% - 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":"3-4","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":"3-4","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"17 demo track papers have been accepted from 28 submissions","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}