{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T18:59:43Z","timestamp":1770749983981,"version":"3.50.0"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"S2","license":[{"start":{"date-parts":[[2024,5,31]],"date-time":"2024-05-31T00:00:00Z","timestamp":1717113600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,5,31]],"date-time":"2024-05-31T00:00:00Z","timestamp":1717113600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF-2023R1A2C1008156"],"award-info":[{"award-number":["NRF-2023R1A2C1008156"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002460","name":"Bio & Medical Technology Development Program of the National Research Foundation","doi-asserted-by":"publisher","award":["2024"],"award-info":[{"award-number":["2024"]}],"id":[{"id":"10.13039\/501100002460","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>Epilepsy, a chronic brain disorder characterized by abnormal brain activity that causes seizures and other symptoms, is typically treated using anti-epileptic drugs (AEDs) as the first-line therapy. However, due to the variations in their modes of action, identification of effective AEDs often relies on ad hoc trials, which is particularly challenging for pediatric patients. Thus, there is significant value in computational methods capable of assisting in the selection of AEDs, aiming to minimize unnecessary medication and improve treatment efficacy.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>In this study, we collected 7,507 medical records from 1,000 pediatric epilepsy patients and developed a computational clinical decision-supporting system for AED selection. This system leverages three multi-channel convolutional neural network (CNN) models tailored to three specific AEDs (vigabatrin, prednisolone, and clobazam). Each CNN model predicts whether a respective AED is effective on a given patient or not. The CNN models showed AUROCs of 0.90, 0.80, and 0.92 in 10-fold cross-validation, respectively. Evaluation on a hold-out test dataset further revealed positive predictive values (PPVs) of 0.92, 0.97, and 0.91 for the three respective CNN models, representing that suggested AEDs by our models would be effective in controlling epilepsy with a high accuracy and thereby reducing unnecessary medications for pediatric patients.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>Our CNN models in the system demonstrated high PPVs for the three AEDs, which signifies the potential of our approach to support the clinical decision-making by assisting doctors in recommending effective AEDs within the three AEDs for patients based on their medical history. This would result in a reduction in the number of unnecessary ad hoc attempts to find an effective AED for pediatric epilepsy patients.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12911-024-02552-w","type":"journal-article","created":{"date-parts":[[2024,5,31]],"date-time":"2024-05-31T13:04:38Z","timestamp":1717160678000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["A computational clinical decision-supporting system to suggest effective anti-epileptic drugs for pediatric epilepsy patients based on deep learning models using patient\u2019s medical history"],"prefix":"10.1186","volume":"24","author":[{"given":"Daeahn","family":"Cho","sequence":"first","affiliation":[]},{"given":"Myeong-Sang","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Jeongyoon","family":"Shin","sequence":"additional","affiliation":[]},{"given":"Jingyu","family":"Lee","sequence":"additional","affiliation":[]},{"given":"Yubin","family":"Kim","sequence":"additional","affiliation":[]},{"given":"Hoon-Chul","family":"Kang","sequence":"additional","affiliation":[]},{"given":"Se Hee","family":"Kim","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9107-7040","authenticated-orcid":false,"given":"Dokyun","family":"Na","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,5,31]]},"reference":[{"key":"2552_CR1","unstructured":"World Health Organization (WHO). Epilepsy. 2023. https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/epilepsy. Accessed 27 Oct 2023."},{"key":"2552_CR2","doi-asserted-by":"publisher","first-page":"821","DOI":"10.15585\/mmwr.mm6631a1","volume":"66","author":"MM Zack","year":"2017","unstructured":"Zack MM, Kobau R. National and state estimates of the numbers of adults and children with active epilepsy - United States, 2015. MMWR Morb Mortal Wkly Rep. 2017;66:821\u20135.","journal-title":"MMWR Morb Mortal Wkly Rep"},{"key":"2552_CR3","doi-asserted-by":"publisher","first-page":"117","DOI":"10.2147\/nedt.2007.3.1.117","volume":"3","author":"J Eatock","year":"2007","unstructured":"Eatock J, Baker GA. Managing patient adherence and quality of life in epilepsy. Neuropsychiatr Dis Treat. 2007;3:117\u201331.","journal-title":"Neuropsychiatr Dis Treat"},{"key":"2552_CR4","first-page":"295","volume":"63","author":"S Yoshida","year":"2011","unstructured":"Yoshida S, Sugawara T, Nishio T, Kaneko S. [Personalized medicine for epilepsy based on the pharmacogenomic testing]. Brain Nerve Shinkei Kenkyu No Shinpo. 2011;63:295\u20139.","journal-title":"Brain Nerve Shinkei Kenkyu No Shinpo"},{"key":"2552_CR5","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1016\/j.eplepsyres.2011.09.009","volume":"98","author":"HH Sonmezturk","year":"2012","unstructured":"Sonmezturk HH, Arain AM, Paolicchi JM, Abou-Khalil BW. Similar response to anti-epileptic medications among epileptic siblings. Epilepsy Res. 2012;98:187\u201393.","journal-title":"Epilepsy Res"},{"key":"2552_CR6","doi-asserted-by":"publisher","first-page":"481","DOI":"10.7861\/clinmedicine.16-5-481","volume":"16","author":"JJ Coleman","year":"2016","unstructured":"Coleman JJ, Pontefract SK. Adverse drug reactions. Clin Med. 2016;16:481\u20135.","journal-title":"Clin Med"},{"key":"2552_CR7","first-page":"1987","volume":"20","author":"JC Res\u00e9ndiz-Aparicio","year":"2021","unstructured":"Res\u00e9ndiz-Aparicio JC, Padilla-Huicab JM, Mart\u00ednez-Ju\u00e1rez IE, Hern\u00e1ndez-Mart\u00ednez G, L\u00f3pez-Correa E, V\u00e1zquez-Ju\u00e1rez B, et al. Clinical guideline: antiepileptic drugs of choice for epileptic syndromes and epilepsies in pediatric patients. Rev Mex Neurocienc. 2021;20:1987.","journal-title":"Rev Mex Neurocienc"},{"key":"2552_CR8","doi-asserted-by":"publisher","first-page":"217","DOI":"10.2174\/1574886314666190311112710","volume":"14","author":"S Kaushik","year":"2019","unstructured":"Kaushik S, Chopra D, Sharma S, Aneja S. Adverse drug reactions of anti-epileptic drugs in children with epilepsy: a cross-sectional study. Curr Drug Saf. 2019;14:217\u201324.","journal-title":"Curr Drug Saf"},{"key":"2552_CR9","doi-asserted-by":"publisher","first-page":"e000116","DOI":"10.1136\/bmjpo-2017-000116","volume":"1","author":"O Egunsola","year":"2017","unstructured":"Egunsola O, Sammons HM, Ojha S, Whitehouse W, Anderson M, Hawcutt D, et al. Protocol for a prospective observational study of adverse drug reactions of anti-epileptic drugs in children in the UK. BMJ Paediatr Open. 2017;1:e000116.","journal-title":"BMJ Paediatr Open"},{"key":"2552_CR10","doi-asserted-by":"publisher","first-page":"541","DOI":"10.1162\/neco.1989.1.4.541","volume":"1","author":"Y LeCun","year":"1989","unstructured":"LeCun Y, Boser B, Denker JS, Henderson D, Howard RE, Hubbard W, et al. Backpropagation applied to handwritten zip code recognition. Neural Comput. 1989;1:541\u201351.","journal-title":"Neural Comput"},{"key":"2552_CR11","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput. 1997;9:1735\u201380.","journal-title":"Neural Comput"},{"key":"2552_CR12","doi-asserted-by":"publisher","first-page":"331","DOI":"10.1016\/j.eplepsyres.2016.09.015","volume":"127","author":"DM Goldenholz","year":"2016","unstructured":"Goldenholz DM, Jow A, Khan OI, Bagi\u0107 A, Sato S, Auh S, et al. Preoperative prediction of temporal lobe epilepsy surgery outcome. Epilepsy Res. 2016;127:331\u20138.","journal-title":"Epilepsy Res"},{"key":"2552_CR13","doi-asserted-by":"publisher","first-page":"634","DOI":"10.1016\/j.nicl.2017.09.015","volume":"16","author":"GM Ibrahim","year":"2017","unstructured":"Ibrahim GM, Sharma P, Hyslop A, Guillen MR, Morgan BR, Wong S, et al. Presurgical thalamocortical connectivity is associated with response to vagus nerve stimulation in children with intractable epilepsy. NeuroImage Clin. 2017;16:634\u201342.","journal-title":"NeuroImage Clin"},{"key":"2552_CR14","doi-asserted-by":"publisher","first-page":"560","DOI":"10.4103\/0028-3886.317233","volume":"69","author":"T Kaur","year":"2021","unstructured":"Kaur T, Diwakar A, Kirandeep, Mirpuri P, Tripathi M, Chandra PS, et al. Artificial intelligence in epilepsy. Neurol India. 2021;69:560.","journal-title":"Neurol India"},{"key":"2552_CR15","doi-asserted-by":"crossref","unstructured":"Kerr W, Nguyen S, Cho A, Lau E, Silverman D, Douglas P et al. Computer-aided diagnosis and localization of lateralized temporal lobe epilepsy using interictal FDG-PET. Front Neurol. 2013;4:31.","DOI":"10.3389\/fneur.2013.00031"},{"key":"2552_CR16","doi-asserted-by":"crossref","unstructured":"Chen C, Zhang L, Fan X, Wang Y, Xu C, Liu R. A epilepsy drug recommendation system by implicit feedback and crossing recommendation. In: 2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld\/SCALCOM\/UIC\/ATC\/CBDCom\/IOP\/SCI). IEEE; 2018. pp. 1134\u20139.","DOI":"10.1109\/SmartWorld.2018.00197"},{"key":"2552_CR17","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1097\/FPC.0b013e32831d1dfa","volume":"19","author":"S Petrovski","year":"2009","unstructured":"Petrovski S, Szoeke CE, Sheffield LJ, D\u2019souza W, Huggins RM, O\u2019brien TJ. Multi-SNP pharmacogenomic classifier is superior to single-SNP models for predicting drug outcome in complex diseases. Pharmacogenet Genomics. 2009;19:147\u201352.","journal-title":"Pharmacogenet Genomics"},{"key":"2552_CR18","doi-asserted-by":"publisher","first-page":"1797","DOI":"10.1016\/j.eplepsyres.2014.08.022","volume":"108","author":"K Shazadi","year":"2014","unstructured":"Shazadi K, Petrovski S, Roten A, Miller H, Huggins RM, Brodie MJ, et al. Validation of a multigenic model to predict seizure control in newly treated epilepsy. Epilepsy Res. 2014;108:1797\u2013805.","journal-title":"Epilepsy Res"},{"key":"2552_CR19","doi-asserted-by":"publisher","first-page":"e418","DOI":"10.1212\/WNL.0000000000006850","volume":"92","author":"I S\u00e1nchez Fern\u00e1ndez","year":"2019","unstructured":"S\u00e1nchez Fern\u00e1ndez I, Loddenkemper T, Ga\u00ednza-Lein M, Sheidley BR, Poduri A. Diagnostic yield of genetic tests in epilepsy. Neurology. 2019;92:e418\u201328.","journal-title":"Neurology"},{"key":"2552_CR20","doi-asserted-by":"publisher","first-page":"100575","DOI":"10.1016\/j.ebr.2022.100575","volume":"20","author":"F Akbar","year":"2022","unstructured":"Akbar F, Saleh R, Kirmani S, Chand P, Mukhtiar K, Jan F, et al. Utility of genetic testing in pediatric epilepsy: experience from a low to middle-income country. Epilepsy Behav Rep. 2022;20:100575.","journal-title":"Epilepsy Behav Rep"},{"key":"2552_CR21","volume-title":"Natural language processing with Python: analyzing text with the natural language toolkit","author":"B Steven","year":"2009","unstructured":"Steven B, Ewan K, Edward L. Natural language processing with Python: analyzing text with the natural language toolkit. O\u2019Reily Media, Inc.; 2009."},{"key":"2552_CR22","unstructured":"Keras CF. GitHub. 2015. http:\/\/github.com\/fchollet\/keras."},{"key":"2552_CR23","doi-asserted-by":"crossref","unstructured":"Pennington J, Socher R, Manning CD. GloVe: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). ACL Anthology; 2014. p. 1532\u201343.","DOI":"10.3115\/v1\/D14-1162"},{"key":"2552_CR24","doi-asserted-by":"publisher","first-page":"djv153","DOI":"10.1093\/jnci\/djv153","volume":"107","author":"R Simon","year":"2015","unstructured":"Simon R. Sensitivity, specificity, PPV, and NPV for predictive biomarkers. JNCI J Natl Cancer Inst. 2015;107:djv153.","journal-title":"JNCI J Natl Cancer Inst"},{"key":"2552_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s42494-020-00035-9","volume":"3","author":"S Yang","year":"2021","unstructured":"Yang S, Wang B, Han X. Models for predicting treatment efficacy of antiepileptic drugs and prognosis of treatment withdrawal in epilepsy patients. Acta Epileptol. 2021;3:1.","journal-title":"Acta Epileptol"},{"key":"2552_CR26","doi-asserted-by":"crossref","unstructured":"Ladani DJ, Desai NP. Stopword identification and removal techniques on TC and IR applications: a survey. In: 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS). IEEE; 2020. p. 466\u201372.","DOI":"10.1109\/ICACCS48705.2020.9074166"},{"key":"2552_CR27","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1016\/j.ieri.2014.09.084","volume":"10","author":"M Esther Hannah","year":"2014","unstructured":"Esther Hannah M, Mukherjee S, Balaramar S. A redundancy elimination approach towards summary refinement. IERI Procedia. 2014;10:245\u201351.","journal-title":"IERI Procedia"},{"key":"2552_CR28","doi-asserted-by":"crossref","unstructured":"Kim Y. Convolutional neural networks for sentence classification. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). ACL Anthology; 2014. p. 1746\u201351.","DOI":"10.3115\/v1\/D14-1181"},{"key":"2552_CR29","first-page":"246","volume":"235","author":"M Hughes","year":"2017","unstructured":"Hughes M, Li I, Kotoulas S, Suzumura T. Medical text classification using convolutional neural networks. Stud Health Technol Inf. 2017;235:246\u201350.","journal-title":"Stud Health Technol Inf"},{"issue":"Suppl 3","key":"2552_CR30","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1186\/s12911-019-0781-4","volume":"19","author":"L Yao","year":"2019","unstructured":"Yao L, Mao C, Luo Y. Clinical text classification with rule-based features and knowledge-guided convolutional neural networks. BMC Med Inf Decis Mak. 2019;19(Suppl 3):71.","journal-title":"BMC Med Inf Decis Mak."},{"key":"2552_CR31","doi-asserted-by":"publisher","first-page":"429","DOI":"10.3233\/IDA-2002-6504","volume":"6","author":"N Japkowicz","year":"2002","unstructured":"Japkowicz N, Stephen S. The class imbalance problem: a systematic study. Intell Data Anal. 2002;6:429\u201349.","journal-title":"Intell Data Anal"},{"key":"2552_CR32","doi-asserted-by":"crossref","unstructured":"Kavitha M, Prabhavathy P. A review on machine learning techniques for text classification. In: 2021 4th International Conference on Computing and Communications Technologies (ICCCT). IEEE; 2021. p. 605\u201310.","DOI":"10.1109\/ICCCT53315.2021.9711858"}],"container-title":["BMC Medical Informatics and Decision Making"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-024-02552-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12911-024-02552-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-024-02552-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,11]],"date-time":"2024-06-11T08:05:49Z","timestamp":1718093149000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedinformdecismak.biomedcentral.com\/articles\/10.1186\/s12911-024-02552-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,31]]},"references-count":32,"journal-issue":{"issue":"S2","published-online":{"date-parts":[[2024,4]]}},"alternative-id":["2552"],"URL":"https:\/\/doi.org\/10.1186\/s12911-024-02552-w","relation":{},"ISSN":["1472-6947"],"issn-type":[{"value":"1472-6947","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,31]]},"assertion":[{"value":"9 April 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 May 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 May 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The study protocol was approved by the Institutional Review Board of Yonsei University Health System (IRB number: 4-2021-1163).","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"149"}}