[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125327-en":3,"doc-seo-125327-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125327,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",7,"Healthcare","Application of machine learning algorithms for functional outcome prediction after aneurysmal subarachnoid hemorrhage - a single-center retrospective study","This single-center retrospective study applies machine learning algorithms to predict functional outcome after aneurysmal subarachnoid hemorrhage. The work collects admission and clinical assessment data, including Glasgow Coma Scale, WFNS and Hunt & Hess grades, and evaluates complications. Prognostic models are built with multiple algorithm families, followed by performance assessment using AUROC/AUPR, calibration, and decision curve analysis, supported by forest plot and nomogram. Statistical analyses quantify model discrimination and clinical usefulness.","DISSERTATION  \nApplication of machine learning algorithms for functional outcome prediction after aneurysmal subarachnoid hemorrhage: a single-center retrospective study  \nAnwendung von “machine learning“ Algorithmen für die Vorhersage der funktionellen Erholung nach subarachnoidaler Blutung-eine monozentrische retrospektive Studie  \nzur Erlangung des akademischen Grades Doctor medicinae (Dr. med. )  \nvorgelegt der Medizinischen Fakultät  \nCharité – Universitätsmedizin Berlin  \nvon  \nHan Wang  \nErstbetreuung: [Prof. Dr. med. Andreas Patzak](Prof. Dr. med. Andreas Patzak)  \nDatum der Promotion: 15. September 2025  \nTable of contents  \nList of tables ................................................................................................................... iv  \nList of figures ................................................................................................................... v  \nList of abbreviations ........................................................................................................ vi  \nAbstract ........................................................................................................................... 1  \n1 Introduction ...............................................................................................................3  \n1.1 Epidemiology of spontaneous subarachnoid hemorrhage (SAH) .......................3  \n1.2 Pathophysiology of spontaneous subarachnoid hemorrhage .............................3  \n1.2.1 Increased intracranial pressure .......................................................................3  \n1.2.2 Early brain injury ..............................................................................................4  \n1.2.3 Delayed cerebral ischemia ..............................................................................4  \n1.3 Machine learning ................................................................................................5  \n1.3.1 The history of machine learning.......................................................................5  \n1.3.2 The application of machine learning in medicine .............................................6  \n1.4 The current challenges of existing prognostic models for aSAH ........................6  \n1.5 Objective ............................................................................................................7  \n2 Methods....................................................................................................................8  \n2.1 Patients ..............................................................................................................8  \n2.2 Clinical data collection ........................................................................................8  \n2.3 The detail of clinical assessments upon admission ............................................9  \n2.3.1 Glasgow Coma Scale (GCS) ...........................................................................9  \n2.3.2 World Federation of Neurosurgical Societies (WFNS) grades.........................9  \n2.3.3 Hunt & Hess (H&H) grades ........................................................................... 10  \n2.4 The diagnosis of complications ........................................................................ 10  \n2.5 Functional outcome assessment ...................................................................... 11  \n2.6 Data processing................................................................................................ 11  \n2.7 Construction of prognostic models ................................................................... 12  \n2.7.1 LR models ..................................................................................................... 12  \n2.7.2 KNN models .................................................................................................. 13  \n2.7.3 XGB models ...........................................................................................","cbCaiv0TUf7s7CX6","https://ap.wps.com/l/cbCaiv0TUf7s7CX6","pdf",2717457,1,67,"English","en",105,"# Abstract\n# Introduction\n## Epidemiology of spontaneous subarachnoid hemorrhage (SAH)\n## Pathophysiology of spontaneous subarachnoid hemorrhage\n## Machine learning\n## Objective\n# Methods\n## Patients\n## Data processing\n## Construction of prognostic models\n## Evaluation metrics and statistical analysis\n# Results\n## Patient characteristics\n## LR analysis\n## Evaluation of the models\n# Discussion","[{\"question\":\"Which data sources and clinical assessments are used for model building?\",\"answer\":\"The study uses clinical data collection and admission assessments, including Glasgow Coma Scale, WFNS grades, and Hunt \\u0026 Hess grades, together with data processing steps and complication diagnosis.\"},{\"question\":\"What machine learning model families are constructed for prognostic prediction?\",\"answer\":\"Prognostic models are built using LR, KNN, XGB, RF, and ANN approaches, followed by selection of parameters for advanced algorithms.\"},{\"question\":\"How is the predictive performance and clinical value of the models evaluated?\",\"answer\":\"Evaluation includes AUROC and AUPR, calibration curves, receiver operating characteristic and precision-recall curves, and decision curve analysis (DCA) with clinical impact considerations.\"}]","Application of machine learning algorithms for functional outcome prediction after aneurysmal subarachnoid hemorrhage - 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