[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126869-en":3,"doc-seo-126869-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126869,1099523885336,"Violet","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",7,"Healthcare","Enhancing the Prediction for Shunt-Dependent Hydrocephalus After Aneurysmal Subarachnoid Hemorrhage Using a Machine Learning Approach","Early and reliable prediction of shunt-dependent hydrocephalus (SDHC) after aneurysmal subarachnoid hemorrhage (aSAH) can shorten in-hospital stays and lower the risk of catheter-associated meningitis. Machine learning (ML) models were developed to improve SDHC prediction compared with traditional non-ML approaches by training on clinical, radiographic, and laboratory variables. Seven algorithms were evaluated, including GLM variants, CatBoost, Naive Bayes, and a multilayer perceptron (MLP). Models using combined feature sets achieved excellent discrimination, and adding CSF drained in the first 14 days further improved ML performance. The results support reliable SDHC risk stratification using admission data and inform future clinical decision optimization.","Technological University Dublin  \nARROW@TU Dublin  \n\n| Articles | School of Computer Science |\n| --- | --- |\n| 2023\u003Cbr>Enhancing the Prediction for Shunt‑Dependent Hydrocephalus After Aneurysmal Subarachnoid Hemorrhage Using a Machine Learning Approach\u003Cbr>Dietmar Frey\u003Cbr>Universität Berlin, Berlin\u003Cbr>Adam Hilbert\u003Cbr>Charité Lab for AI in Medicine, Berlin\u003Cbr>Anton Früh\u003Cbr>Berlin Institute of Health, Berlin\u003Cbr>oelelow tnexispaagnedfaoditdioitniaonl lorahator:s[https://arrow.tudublin.ie/scschcomart](https://arrow.tudublin.ie/scschcomart)\u003Cbr> Part of the Computer Engineering Commons, and the Medicine and Health Sciences Commons |  |\n\nRecommended Citation  \nFrey, Dietmar; Hilbert, Adam; Früh, Anton; Istvan Madai, Vince; Kossen, Tabea; Kiewitz, Julia; Sommerfeld, Jenny; Vajkoczy, Peter; Unteroberdörster, Meike; Zihni, Esra; Brune, Sophie Charlotte; Wolf, Stefan; and Dengler, Nora Franziska, \"Enhancing the Prediction for Shunt‑Dependent Hydrocephalus After Aneurysmal Subarachnoid Hemorrhage Using a Machine Learning Approach\" (2023) . Articles. 218.  \n[https://arrow.tudublin.ie/scschcomart/218](https://arrow.tudublin.ie/scschcomart/218)  \nThis Article is brought to you for free and open access by the School of Computer Science at ARROW@TU Dublin. It has been accepted for inclusion in Articles by an authorized administrator of ARROW@TU Dublin. For more information, please contact [arrow.admin@tudublin.ie](arrow.admin@tudublin.ie), [aisling.coyne@tudublin.ie](aisling.coyne@tudublin.ie), [gerard.connolly@tudublin.ie](gerard.connolly@tudublin.ie),  \n[vera.ki](vera.ki)[lshaw@tudublin.ie](lshaw@tudublin.ie).  \nThis work is licensed under a Creative Commons Attribution-Share Alike 4.0 International License.  \nFunder: Open Access funding enabled and organized by Projekt DEAL. ND was funded by the institutional Rahel Hirsch and Lydia Rabinowitsch scholarships, received public body funding for the project Go Safe (Horizon 2020), accepted speaker honoraria from Integra Life Sciences, and serves as an advisor for Alexion Pharmaceuticals. DF reported receiving grants from the European Commission Horizon2020 PRECISE4Q No. 777107. No funding bodies had any role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.  \nAuthors  \nDietmar Frey, Adam Hilbert, Anton Früh, Vince Istvan Madai, Tabea Kossen, Julia Kiewitz, Jenny Sommerfeld, Peter Vajkoczy, Meike Unteroberdörster, Esra Zihni, Sophie Charlotte Brune, Stefan Wolf, and Nora Franziska Dengler  \nThis article is available at ARROW@TU Dublin: [https://arrow.tudublin.ie/scschcomart/218](https://arrow.tudublin.ie/scschcomart/218)  \nNeurosurgical Review (2023) 46:206  \n[https://doi.org/10.1007/s10143-023-02114-0](https://doi.org/10.1007/s10143-023-02114-0)  \nEnhancing the prediction for shunt‑dependent hydrocephalus after aneurysmal subarachnoid hemorrhage using a machine learning approach  \nDietmar Frey1,2 · Adam Hilbert1 · Anton Früh2 · Vince Istvan Madai3,4 · Tabea Kossen1 · Julia Kiewitz2 · Jenny Sommerfeld2 · Peter Vajkoczy2 · Meike Unteroberdörster2 · Esra Zihni1,5 · Sophie Charlotte Brune2 · Stefan Wolf2 · Nora Franziska Dengler2  \nReceived: 1 June 2023 / Revised: 31 July 2023 / Accepted: 12 August 2023 © The Author(s) 2023  \nAbstract  \nEarly and reliable prediction of shunt-dependent hydrocephalus (SDHC) after aneurysmal subarachnoid hemorrhage (aSAH) may decrease the duration of in-hospital stay and reduce the risk of catheter-associated meningitis. Machine learning (ML) may improve predictions of SDHC in comparison to traditional non-ML methods. ML models were trained for CHESS and SDASH and two combined individual feature sets with clinical, radiographic, and laboratory variables. Seven different algorithms were used including three types of generalized linear models (GLM) as well as a tree boosting (CatBoost) algorithm, a Naive Bayes (NB) classifier, and a multilayer perceptron (MLP) artificial neural net. The discrimination of the area under t","cbCaiuLSQUbekavW","https://ap.wps.com/l/cbCaiuLSQUbekavW","pdf",1061911,3,1,12,"English","en",105,"# Abstract\n## Model training and algorithms\n## Performance metrics and findings\n## Clinical implications\n# Keywords","[{\"question\":\"Why is early prediction of shunt-dependent hydrocephalus after aSAH important?\",\"answer\":\"It may reduce the duration of in-hospital stay and lower the risk of catheter-associated meningitis by enabling timely clinical decisions.\"},{\"question\":\"Which data types and algorithms were used to predict SDHC?\",\"answer\":\"Models were trained on clinical, radiographic, and laboratory variables and evaluated seven algorithms, including GLM variants, CatBoost, Naive Bayes, and a multilayer perceptron.\"},{\"question\":\"How did combining feature sets and adding CSF drainage improve results?\",\"answer\":\"Using combined feature sets beyond CHESS and SDASH produced excellent discrimination with improved AUC. Adding the amount of CSF drained within the first 14 days further increased performance for multiple ML models.\"}]","Enhancing the Prediction for Shunt-Dependent Hydrocephalus After Aneurysmal Subarachnoid Hemorrhage Using a Machine Learning Approach | PDF",1785935318,30,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"enhancing-the-prediction-for-shunt-dependent-hydrocephalus-after-aneurysmal-subarachnoid-hemorrhage-using-a-machine-learning-approach","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":20},"https://docshare.wps.com/document/healthcare/",{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/enhancing-the-prediction-for-shunt-dependent-hydrocephalus-after-aneurysmal-subarachnoid-hemorrhage-using-a-machine-learning-approach/126869/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-26","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why is early prediction of shunt-dependent hydrocephalus after aSAH important?","Question",{"text":76,"@type":77},"It may reduce the duration of in-hospital stay and lower the risk of catheter-associated meningitis by enabling timely clinical decisions.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which data types and algorithms were used to predict SDHC?",{"text":81,"@type":77},"Models were trained on clinical, radiographic, and laboratory variables and evaluated seven algorithms, including GLM variants, CatBoost, Naive Bayes, and a multilayer perceptron.",{"name":83,"@type":74,"acceptedAnswer":84},"How did combining feature sets and adding CSF drainage improve results?",{"text":85,"@type":77},"Using combined feature sets beyond CHESS and SDASH produced excellent discrimination with improved AUC. 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