{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T10:38:18Z","timestamp":1784284698497,"version":"3.55.0"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T00:00:00Z","timestamp":1740096000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T00:00:00Z","timestamp":1740096000000},"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":["Appl Intell"],"published-print":{"date-parts":[[2025,5]]},"DOI":"10.1007\/s10489-025-06349-w","type":"journal-article","created":{"date-parts":[[2025,2,20]],"date-time":"2025-02-20T22:28:03Z","timestamp":1740090483000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["NNBSVR: Neural Network-Based Semantic Vector Representations of ICD-10 codes"],"prefix":"10.1007","volume":"55","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0773-8409","authenticated-orcid":false,"given":"Monah Bou","family":"Hatoum","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jean Claude","family":"Charr","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alia","family":"Ghaddar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christophe","family":"Guyeux","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Laiymani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,2,21]]},"reference":[{"key":"6349_CR1","doi-asserted-by":"crossref","unstructured":"Abbas M, El-Zoghabi A, Shoukry A (2021) Denmune: density peak based clustering using mutual nearest neighbors. http:\/\/dx.doi.org\/10.1016\/j.patcog.2020.107589","DOI":"10.1016\/j.patcog.2020.107589"},{"key":"6349_CR2","doi-asserted-by":"crossref","unstructured":"Al-Bashabsheh E, Alaiad A, Al-Ayyoub M, Beni-Yonis O, Zitar RA, Abualigah L (2023) Improving clinical documentation: automatic inference of icd-10 codes from patient notes using bert model. J Supercomput 1\u201325","DOI":"10.1007\/s11227-023-05160-z"},{"key":"6349_CR3","unstructured":"Arvai K (2023)kneed. Zenodo"},{"key":"6349_CR4","unstructured":"American\u00a0Dental Association et\u00a0al. (2023) CDT 2024: current dental terminology. American Dental Association"},{"key":"6349_CR5","unstructured":"Biseda B, Desai G, Lin H, Philip A (2020) Prediction of icd codes with clinical bert embeddings and text augmentation with label balancing using mimic-iii. arXiv:2008.10492"},{"key":"6349_CR6","doi-asserted-by":"crossref","unstructured":"Bogatinovski J, Todorovski L, D\u017eeroski S, Kocev D (2022) Comprehensive comparative study of multi-label classification methods. http:\/\/dx.doi.org\/10.1016\/j.eswa.2022.117215","DOI":"10.1016\/j.eswa.2022.117215"},{"key":"6349_CR7","doi-asserted-by":"crossref","unstructured":"Cardoso SD, Da\u00a0Silveira M, Lin Y-C, Christen V, Rahm E, Reynaud-Dela\u00eetre C, Pruski C (2018) Combining semantic and lexical measures to evaluate medical terms similarity","DOI":"10.1007\/978-3-030-06016-9_2"},{"key":"6349_CR8","unstructured":"Centers for Medicare & Medicaid Services. List of cpt\/hcpcs codes, 2025. Accessed on 27 Jan 2025"},{"issue":"6","key":"6349_CR9","doi-asserted-by":"publisher","first-page":"e37557","DOI":"10.2196\/37557","volume":"10","author":"P-F Chen","year":"2022","unstructured":"Chen P-F, Chen K-C, Liao W-C, Lai F, He T-L, Lin S-C, Chen W-J, Yang C-Y, Lin Y-C, Tsai I-C et al (2022) Automatic international classification of diseases coding system: deep contextualized language model with rule-based approaches. JMIR Med Inform 10(6):e37557","journal-title":"JMIR Med Inform"},{"key":"6349_CR10","doi-asserted-by":"publisher","first-page":"804","DOI":"10.1162\/tacl_a_00576","volume":"11","author":"Y Chen","year":"2023","unstructured":"Chen Y, Eger S (2023) MENLI: robust evaluation metrics from natural language inference. Trans Assoc Comput Linguistic 11:804\u2013825","journal-title":"Trans Assoc Comput Linguistic"},{"key":"6349_CR11","first-page":"379","volume-title":"Diagnosis-Related Group (DRG)","author":"PL Elkin","year":"2023","unstructured":"Elkin PL, Brown SH (2023) Diagnosis-Related Group (DRG). Springer International Publishing, Cham, pp 379\u2013393"},{"key":"6349_CR12","doi-asserted-by":"crossref","unstructured":"Frank RA, Jarrin R, Pritzker J, Abramoff MD, Repka MX, Baird PD, Grenon SM, Mahoney MR, Mattison JE, Silva E III (2022) Developing current procedural terminology codes that describe the work performed by machines. http:\/\/dx.doi.org\/10.1038\/s41746-022-00723-5","DOI":"10.1038\/s41746-022-00723-5"},{"key":"6349_CR13","doi-asserted-by":"crossref","unstructured":"Grover A, Leskovec J (2016) node2vec: scalable feature learning for networks. arXiv","DOI":"10.1145\/2939672.2939754"},{"key":"6349_CR14","doi-asserted-by":"crossref","unstructured":"Hatoum M, Charr J-C, Guyeux C, Laiymani D, Ghaddar A (2023) Emte: an enhanced medical terms extractor using pattern matching rules","DOI":"10.5220\/0011717300003393"},{"key":"6349_CR15","doi-asserted-by":"crossref","unstructured":"Hatoum MB, Charr JC, Ghaddar A, Guyeux C, Laiymani D (2024) Utp: a unified term presentation tool for clinical textual data using pattern-matching rules and dictionary-based ontologies","DOI":"10.1007\/978-3-031-55326-4_17"},{"key":"6349_CR16","unstructured":"Independent Health and Aged Care\u00a0Pricing Authority (2023) Australian Coding Standards for ICD-10-AM and ACHI. Version 12.0"},{"issue":"4","key":"6349_CR17","doi-asserted-by":"publisher","first-page":"596","DOI":"10.3174\/ajnr.A4696","volume":"37","author":"JA Hirsch","year":"2016","unstructured":"Hirsch JA, Nicola G, McGinty G, Liu RW, Barr RM, Chittle MD, Manchikanti L (2016) Icd-10: history and context. Am J Neuroradiol 37(4):596\u2013599","journal-title":"Am J Neuroradiol"},{"key":"6349_CR18","unstructured":"Huang A (2022) \u2019tis but thy name: semantic question answering evaluation with 11m names for 1m entities. arXiv"},{"key":"6349_CR19","unstructured":"Huang J, Altosaar J, Ranganath R (2019) Clinicalbert: Modeling clinical notes and predicting hospital readmission. arXiv:1904.05342"},{"key":"6349_CR20","unstructured":"icd-codex contributors (2020) icd-codex: Python library for graphical and continuous representations of icd9 and icd10 codes"},{"key":"6349_CR21","unstructured":"Independent Hospital Pricing Authority (2025) Ar-drg version 11.0. Accessed on 27 Jan 2025"},{"issue":"12","key":"6349_CR22","doi-asserted-by":"publisher","first-page":"1105","DOI":"10.1097\/MLR.0b013e3181ef9d3e","volume":"48","author":"N Jett\u00e9","year":"2010","unstructured":"Jett\u00e9 N, Quan H, Hemmelgarn B, Drosler S, Maass C, Moskal L, Paoin W, Sundararajan V, Gao S, Jakob R et al (2010) The development, evolution, and modifications of icd-10: challenges to the international comparability of morbidity data. Med Care 48(12):1105\u20131110","journal-title":"Med Care"},{"key":"6349_CR23","doi-asserted-by":"crossref","unstructured":"Johnson JM, Khoshgoftaar TM (2022) Encoding high-dimensional procedure codes for healthcare fraud detection. http:\/\/dx.doi.org\/10.1007\/s42979-022-01252-4","DOI":"10.1007\/s42979-022-01252-4"},{"key":"6349_CR24","doi-asserted-by":"crossref","unstructured":"Kartchner D, Christensen T, Humpherys J, Wade S (2017) Code2vec: Embedding and clustering medical diagnosis data. http:\/\/dx.doi.org\/10.1109\/ICHI.2017.94","DOI":"10.1109\/ICHI.2017.94"},{"key":"6349_CR25","unstructured":"Scikit learn developers (2023) sklearn.decomposition.truncatedsvd - scikit-learn 1.3.2 documentation. https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.decomposition.TruncatedSVD.html. Accessed on 31 Oct 2023"},{"issue":"4","key":"6349_CR26","doi-asserted-by":"publisher","first-page":"1234","DOI":"10.1093\/bioinformatics\/btz682","volume":"36","author":"J Lee","year":"2020","unstructured":"Lee J, Yoon W, Kim S, Kim D, Kim S, So CH, Kang J (2020) Biobert: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics 36(4):1234\u20131240","journal-title":"Bioinformatics"},{"key":"6349_CR27","first-page":"103500","volume":"110","author":"F Li","year":"2020","unstructured":"Li F, Jin B, Liu W, Rahman MM, Luo G, Zhang Y, Jiang G (2020) Icd coding from clinical text using multi-filter residual convolutional neural network. J Biomed Inform 110:103500","journal-title":"J Biomed Inform"},{"key":"6349_CR28","doi-asserted-by":"crossref","unstructured":"Li M, Zhou X, Ryu KH, Theera-Umpon N (2022) An ensemble semantic textual similarity measure based on multiple evidences for biomedical documents","DOI":"10.1155\/2022\/8238432"},{"key":"6349_CR29","doi-asserted-by":"crossref","unstructured":"Long R (2021) Fairness in machine learning: Against false positive rate equality as a measure of fairness. http:\/\/dx.doi.org\/10.1163\/17455243-20213439","DOI":"10.1163\/17455243-20213439"},{"key":"6349_CR30","doi-asserted-by":"crossref","unstructured":"Della Mea V, Popescu MH, Roitero K (2020) Underlying cause of death identification from death certificates via categorical embeddings and convolutional neural networks","DOI":"10.1109\/ICHI48887.2020.9374316"},{"key":"6349_CR31","doi-asserted-by":"crossref","unstructured":"Mittelstadt B, Wachter S, Russell C (2023) The unfairness of fair machine learning: levelling down and strict egalitarianism by default. arXiv:2302.02404","DOI":"10.36645\/mtlr.30.1.unfairness"},{"key":"6349_CR32","doi-asserted-by":"crossref","unstructured":"Mohammed HH, Dogdu E, Gorur AK, Choupani R (2020) Multi-label classification of text documents using deep learning. http:\/\/dx.doi.org\/10.1109\/BigData50022.2020.9378266","DOI":"10.1109\/BigData50022.2020.9378266"},{"key":"6349_CR33","doi-asserted-by":"crossref","unstructured":"Mullenbach J, Wiegreffe S, Duke J, Sun J, Eisenstein J (2018) Explainable prediction of medical codes from clinical text. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). pp 1112\u20131121","DOI":"10.18653\/v1\/N18-1100"},{"key":"6349_CR34","doi-asserted-by":"crossref","unstructured":"Rajendran P, Zenonos A, Spear J, Pope R (2021) Embed wisely: An ensemble approach to predict icd coding","DOI":"10.1007\/978-3-030-93733-1_26"},{"issue":"S1","key":"6349_CR35","doi-asserted-by":"publisher","first-page":"S32","DOI":"10.5694\/j.1326-5377.1998.tb123473.x","volume":"169","author":"RF Roberts","year":"1998","unstructured":"Roberts RF, Innes KC, Walker SM (1998) Introducing icd-10-am in australian hospitals. Med J Aust 169(S1):S32-5","journal-title":"Med J Aust"},{"key":"6349_CR36","doi-asserted-by":"crossref","unstructured":"Satopaa V, Albrecht J, Irwin D, Raghavan B (2011) Finding a \u201ckneedle\u201d in a haystack: Detecting knee points in system behavior","DOI":"10.1109\/ICDCSW.2011.20"},{"key":"6349_CR37","unstructured":"Van der Maaten l, Hinton G, (2008) Visualizing data using t-sne. J Mach Learn Res 9(11)"},{"key":"6349_CR38","doi-asserted-by":"crossref","unstructured":"Winata GI, Lin Z, Shin J, Liu Z, Fung P (2019) Hierarchical meta-embeddings for code-switching named entity recognition","DOI":"10.18653\/v1\/W19-4320"},{"key":"6349_CR39","unstructured":"Wu Y, Doshi-Velez F, Schwartz M, Zhang B (2021) Icd2vec: mathematical representation of diseases. arXiv:2103.05148"},{"key":"6349_CR40","doi-asserted-by":"crossref","unstructured":"Zhao W, Peyrard M, Liu F, Gao Y, Meyer CM, Eger S (2019) Moverscore: Text generation evaluating with contextualized embeddings and earth mover distance. http:\/\/dx.doi.org\/10.18653\/v1\/D19-1053","DOI":"10.18653\/v1\/D19-1053"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-025-06349-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-025-06349-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-025-06349-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T19:31:04Z","timestamp":1758310264000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-025-06349-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,21]]},"references-count":40,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2025,5]]}},"alternative-id":["6349"],"URL":"https:\/\/doi.org\/10.1007\/s10489-025-06349-w","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,21]]},"assertion":[{"value":"5 February 2025","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 February 2025","order":2,"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 that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}},{"value":"This study was conducted as part of the ongoing research and quality improvement initiatives at Specialized Medical Center, Saudi Arabia. The study protocol was reviewed and approved by the hospital\u2019s Institutional Review Board (IRB).","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval"}}],"article-number":"466"}}