{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T23:02:30Z","timestamp":1740178950079,"version":"3.37.3"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2021,6,5]],"date-time":"2021-06-05T00:00:00Z","timestamp":1622851200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,6,5]],"date-time":"2021-06-05T00:00:00Z","timestamp":1622851200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"name":"Multidisciplinary Institute in Artificial Intelligence","award":["ANR-19-P3IA-0003"],"award-info":[{"award-number":["ANR-19-P3IA-0003"]}]},{"name":"Fundacao de Amparo a Pesquisa do Estado de Sao Paulo","award":["2018\/17620-5 and 2016\/17078-0"],"award-info":[{"award-number":["2018\/17620-5 and 2016\/17078-0"]}]},{"DOI":"10.13039\/501100003593","name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico","doi-asserted-by":"publisher","award":["406550\/2018-2 and 305580\/2017-5"],"award-info":[{"award-number":["406550\/2018-2 and 305580\/2017-5"]}],"id":[{"id":"10.13039\/501100003593","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Healthc Inform Res"],"published-print":{"date-parts":[[2021,12]]},"DOI":"10.1007\/s41666-021-00100-z","type":"journal-article","created":{"date-parts":[[2021,6,5]],"date-time":"2021-06-05T19:02:34Z","timestamp":1622919754000},"page":"474-496","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Real-world Patient Trajectory Prediction from Clinical Notes Using Artificial Neural Networks and UMLS-Based Extraction of Concepts"],"prefix":"10.1007","volume":"5","author":[{"given":"Jamil","family":"Zaghir","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8318-1780","authenticated-orcid":false,"given":"Jose F","family":"Rodrigues-Jr","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lorraine","family":"Goeuriot","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sihem","family":"Amer-Yahia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,6,5]]},"reference":[{"issue":"3","key":"100_CR1","doi-asserted-by":"publisher","first-page":"201","DOI":"10.19030\/ajhs.v3i3.7139","volume":"3","author":"T Seymour","year":"2012","unstructured":"Seymour T, Frantsvog D, Graeber T, et al. (2012) Electronic health records (EHR). Am J Health Sci (AJHS) 3(3):201","journal-title":"Am J Health Sci (AJHS)"},{"issue":"1","key":"100_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3377164","volume":"1","author":"A Bellot","year":"2020","unstructured":"Bellot A, Schaar MVD (2020) Flexible modelling of longitudinal medical data: A Bayesian nonparametric approach. ACM Trans Comput Healthcare 1(1):1","journal-title":"ACM Trans Comput Healthcare"},{"key":"100_CR3","volume-title":"Deep Learning, vol 1","author":"I Goodfellow","year":"2016","unstructured":"Goodfellow I, Bengio Y, Courville A (2016) Deep Learning, vol 1. MIT Press, Cambridge"},{"issue":"3","key":"100_CR4","doi-asserted-by":"publisher","first-page":"457","DOI":"10.1093\/jamia\/ocz200","volume":"27","author":"S Wu","year":"2020","unstructured":"Wu S, Roberts K, Datta S, Du J, Ji Z, Si Y, Soni S, Wang Q, Wei Q, Xiang Y, et al. (2020) Deep learning in clinical natural language processing: a methodical review. J Am Med Inform Assoc 27(3):457","journal-title":"J Am Med Inform Assoc"},{"issue":"2","key":"100_CR5","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1136\/jamia.2010.007237","volume":"18","author":"ST Rosenbloom","year":"2011","unstructured":"Rosenbloom ST, Denny JC, Xu H, Lorenzi N, Stead WW, Johnson KB (2011) Data from clinical notes: a perspective on the tension between structure and flexible documentation. J Am Med Inform Assoc 18(2):181","journal-title":"J Am Med Inform Assoc"},{"key":"100_CR6","unstructured":"Grnarova P, Schmidt F, Hyland SL, Eickhoff C (2016) Neural document embeddings for intensive care patient mortality prediction. arXiv:1612.00467"},{"key":"100_CR7","unstructured":"Huang K, Altosaar J, Ranganath R (2019) Clinicalbert: Modeling clinical notes and predicting hospital readmission. arXiv:1904.05342"},{"issue":"suppl_1","key":"100_CR8","doi-asserted-by":"publisher","first-page":"D267","DOI":"10.1093\/nar\/gkh061","volume":"32","author":"O Bodenreider","year":"2004","unstructured":"Bodenreider O (2004) The unified medical language system (UMLS): integrating biomedical terminology. Nucleic acids research 32(suppl_1):D267","journal-title":"Nucleic acids research"},{"key":"100_CR9","unstructured":"Official ICD reference. https:\/\/www.who.int\/standards\/classiffications\/classiffication-of-diseases. Accessed March, 2021"},{"issue":"1-4","key":"100_CR10","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1007\/s13042-010-0001-0","volume":"1","author":"Y Zhang","year":"2010","unstructured":"Zhang Y, Jin R, Zhou ZH (2010) Understanding bag-of-words model: a statistical framework. Int J Mach Learn Cybern 1(1-4):43","journal-title":"Int J Mach Learn Cybern"},{"key":"100_CR11","unstructured":"Unified medical language system (UMLS). https:\/\/www.nlm.nih.gov\/research\/umls\/index.html, accessed March, 2021"},{"key":"100_CR12","unstructured":"Metathesaurus\u2019s unique identifiers. https:\/\/www.nlm.nih.gov\/research\/umls\/new_users\/online_learning\/Meta_005.html, accessed March, 2021"},{"key":"100_CR13","unstructured":"Clinical classifications software databases. https:\/\/www.hcup-us.ahrq.gov\/toolssoftware\/ccs\/ccs.jsp, accessed March, 2021"},{"key":"100_CR14","unstructured":"Choi E, Bahadori MT, Schuetz A, Stewart WF, Sun J (2016) Doctor ai: Predicting clinical events via recurrent neural networks"},{"key":"100_CR15","unstructured":"Rodrigues-Jr JF, Spadon G, Brandoli B, Amer-Yahia S (2019) Patient trajectory prediction in the Mimic-III dataset, challenges and pitfalls. arXiv:1909.04605"},{"key":"100_CR16","first-page":"15","volume":"1050","author":"S Dubois","year":"2017","unstructured":"Dubois S, Romano N, Kale DC, Shah N, Jung K (2017) Learning effective representations from clinical notes. Stat 1050:15","journal-title":"Stat"},{"key":"100_CR17","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1016\/j.jbi.2018.06.016","volume":"84","author":"M Sushil","year":"2018","unstructured":"Sushil M, \u0160uster S., Luyckx K, Daelemans W (2018) Patient representation learning and interpretable evaluation using clinical notes. J. Biomed. Informatics 84:103","journal-title":"J. Biomed. Informatics"},{"key":"100_CR18","unstructured":"MIT\u2019s MIMIC-III (). https:\/\/mimic.physionet.org\/, accessed March, 2021"},{"key":"100_CR19","unstructured":"Clinical language annotation, modeling, and processing toolkit. https:\/\/clamp.uth.edu\/, accessed March, 2021"},{"key":"100_CR20","doi-asserted-by":"crossref","unstructured":"Pennington J, Socher R, Manning CD (2014) Glove: Global vectors for word representation. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pp 1532\u20131543","DOI":"10.3115\/v1\/D14-1162"},{"key":"100_CR21","unstructured":"Devlin J, Chang MW, Lee K, Toutanova K (2018) Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805"},{"issue":"1","key":"100_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/sdata.2016.35","volume":"3","author":"AE Johnson","year":"2016","unstructured":"Johnson AE, Pollard TJ, Shen L, Li-Wei HL, Feng M, Ghassemi M, Moody B, Szolovits P, Celi LA, Mark RG (2016) MIMIC-III, a freely accessible critical care database. Scientific Data 3(1):1","journal-title":"Scientific Data"},{"key":"100_CR23","unstructured":"Soldaini L, Goharian N (2016) Quickumls: a fast, unsupervised approach for medical concept extraction. MedIR workshop, sigir 1\u20134"},{"key":"100_CR24","unstructured":"Quickumls github link. https:\/\/github.com\/Georgetown-IR-Lab\/QuickUMLS, Accessed March, 2021"},{"key":"100_CR25","unstructured":"Kingma DP, Ba J (2014) Adam: A method for stochastic optimization. arXiv:1412.6980"},{"key":"100_CR26","volume-title":"Optimization for Machine Learning","author":"S Sra","year":"2012","unstructured":"Sra S, Nowozin S, Wright SJ (2012) Optimization for Machine Learning. MIT Press, Cambridge"},{"key":"100_CR27","unstructured":"Kumar SK (2017) On weight initialization in deep neural networks. arXiv:1704.08863"},{"key":"100_CR28","doi-asserted-by":"publisher","first-page":"218","DOI":"10.1016\/j.jbi.2017.04.001","volume":"69","author":"T Pham","year":"2017","unstructured":"Pham T, Tran T, Phung D, Venkatesh S (2017) Predicting healthcare trajectories from medical records: A deep learning approach. J Biomed Informa 69:218","journal-title":"J Biomed Informa"},{"issue":"832","key":"100_CR29","doi-asserted-by":"publisher","first-page":"360","DOI":"10.1259\/bjr.70.832.9166071","volume":"70","author":"N Hawass","year":"1997","unstructured":"Hawass N (1997) Comparing the sensitivities and specificities of two diagnostic procedures performed on the same group of patients. British J Radiology 70(832):360","journal-title":"British J Radiology"},{"key":"100_CR30","unstructured":"Le Q, Mikolov T (2014) Distributed representations of sentences and documents. In: International conference on machine learning (PMLR), pp 1188\u20131196"},{"issue":"3","key":"100_CR31","doi-asserted-by":"publisher","first-page":"331","DOI":"10.1093\/jamia\/ocx132","volume":"25","author":"E Soysal","year":"2018","unstructured":"Soysal E, Wang J, Jiang M, Wu Y, Pakhomov S, Liu H, Xu H (2018) CLAMP\u2013a toolkit for efficiently building customized clinical natural language processing pipelines. J Am Med Inform Assoc 25(3):331","journal-title":"J Am Med Inform Assoc"},{"key":"100_CR32","unstructured":"Vincent P, Larochelle H, Lajoie I, Bengio Y, Manzagol PA, Bottou L (2010) Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. J Mach Learn Res 11(12)"},{"key":"100_CR33","doi-asserted-by":"crossref","unstructured":"Li L, Liu G (2020) In-hospital mortality prediction for ICU patients on large healthcare MIMIC datasets using class imbalance learning, IEEE","DOI":"10.1109\/ICBDA49040.2020.9101272"},{"key":"100_CR34","first-page":"677","volume":"192","author":"WW Chapman","year":"2013","unstructured":"Chapman WW, Hilert D, Velupillai S, Kvist M, Skeppstedt M, Chapman BE, Conway M, Tharp M, Mowery DL, Deleger L (2013) Extending the NegEx lexicon for multiple languages. Studies in Health Technology and Informatics 192:677","journal-title":"Studies in Health Technology and Informatics"}],"container-title":["Journal of Healthcare Informatics Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41666-021-00100-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s41666-021-00100-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41666-021-00100-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,10,30]],"date-time":"2021-10-30T21:08:36Z","timestamp":1635628116000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s41666-021-00100-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,5]]},"references-count":34,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2021,12]]}},"alternative-id":["100"],"URL":"https:\/\/doi.org\/10.1007\/s41666-021-00100-z","relation":{},"ISSN":["2509-4971","2509-498X"],"issn-type":[{"type":"print","value":"2509-4971"},{"type":"electronic","value":"2509-498X"}],"subject":[],"published":{"date-parts":[[2021,6,5]]},"assertion":[{"value":"2 October 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 April 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 May 2021","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 June 2021","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"<!--Emphasis Type='Bold' removed-->Conflict of Interest"}}]}}