Document Type
Article
Disciplines
1.2 COMPUTER AND INFORMATION SCIENCE, 3. MEDICAL AND HEALTH SCIENCES
Abstract
Early and reliable prediction of shunt-dependent hydrocephalus (SDHC) after aneurysmal subarachnoid haemorhage (a SAH) 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 (Cat Boost) algorithm, a Nave Bayes (NB) classifier, and a multilayer perceptron (MLP) artificial neural net. The discrimination of the area under the curve (AUC) was classified (0.7≤AUC
DOI
https://doi.org/10.1007/s10143-023-02114-0
Recommended Citation
Frey, 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.
https://arrow.tudublin.ie/scschcomart/218
Funder
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.
Creative Commons License
This work is licensed under a Creative Commons Attribution-Share Alike 4.0 International License.
Publication Details
https://link.springer.com/article/10.1007/s10143-023-02114-0#:~:text=Early%20and%20reliable%20prediction%20of,to%20traditional%20non%2DML%20methods.
Frey, D., Hilbert, A., Früh, A. et al. Enhancing the prediction for shunt-dependent hydrocephalus after aneurysmal subarachnoid hemorrhage using a machine learning approach. Neurosurg Rev 46, 206 (2023).
https://doi.org/10.1007/s10143-023-02114-0