[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125434-en":3,"doc-seo-125434-105":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},125434,687197207919,"Theodora","https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552",8,"Research & Report","Predictive Modeling of Fall Risk in Orthogeriatric Patients using Machine Learning Techniques","The dissertation develops predictive modeling for fall risk in orthogeriatric patients using machine learning techniques. It frames the clinical need for fall-risk assessment, reviews risk stratification approaches for osteosarcopenia and related guidelines, and addresses how gait analysis and multidimensional data support evaluation. The work also examines physical frailty assessment in bedridden patients, highlighting discrepancies between clinical and objective measurements and practical challenges of conducting physical tests.","Aus der  \nKlinik fü r Orthopädie und Unfallchirurgie Klinikum der Ludwig-Maximilians-Universität München  \nPredictive Modeling of Fall Risk in Orthogeriatric Patients using  \nMachine Learning Techniques  \nDissertation  \nzum Erwerb des Doktorgrades der Medizin  \nan der Medizinischen Fakultät der Ludwig-Maximilians-Universität M ünchen  \nvorgelegt von  \nMoritz Kraus  \naus  \nStarnberg  \nJahr  \nMit Genehmigung der Medizinischen Fakultät der Ludwig-Maximilians-Universität München  \nErstes Gutachten: Zweites Gutachten: Drittes Gutachten:  \nProf. Dr. Wolfgang Böcker  \nProf. Dr. Henning Wackerhage Prof. Dr. Michael Ingrisch  \nDekan: [Prof. Dr. med. Thomas Gudermann](Prof. Dr. med. Thomas Gudermann)  \nTag der m ündlichen Prüfung: 24.03.2025  \nAffidavit  \nEidesstattliche Versicherung  \nKraus, Moritz  \n________  \nName, Vorname  \nIch erkläre hiermit an Eides statt, dass ich die vorliegende Dissertation mit dem Titel:  \nPredictive Modeling ofFall Risk in Orthogeriatric Patients using  \nMachine Learning Techniques  \nselbständig verfasst, mich außer der angegebenen keiner weiteren Hilfsmittel bedient und alle Erkenntnisse, die aus dem Schrifttum ganz oder annähernd übernommen sind, als solche kenntlich gemacht und nach ihrer Herkunft unter Bezeichnung der Fundstelle einzeln nachgewiesen habe.  \nIch erkläre des Weiteren, dass die hier vorgelegte Dissertation nicht in gleicher oder in ähnlicher Form bei einer anderen Stelle zur Erlangung eines akademischen Grades eingereicht wurde.  \nMünchen, den 29.06.2025 Kraus, Moritz  \nOrt, Datum Unterschrift Doktorandin bzw. Doktorand  \nInhaltsverzeichnis  \nAffidavit........................................................................................................................................ 3  \nInhaltsverzeichnis ....................................................................................................................... 4  \nAbbildungsverzeichnis............................................................................................................... 5  \nPublikationsliste ......................................................................................................................... 6  \n1.1 Beitrag zur 1. Publikation .................................................................................................. 8  \n1.2 Beitrag zur 2. Publikation .................................................................................................. 9  \n2. Einleitung ...................................................................................................................... 10  \n2.1.1 Introduction ..................................................................................................................... 10  \n2.1.2 Definition of Osteosarcopenia......................................................................................... 10  \n2.1.3 Current Tools for Risk Stratification for Osteosarcopenia............................................... 11  \n2.2 Guidelines for the Assessment and Treatment of Osteosarcopenia .............................. 12  \n2.2.1 Guidelines for the Diagnosis and Management of Osteoporosis.................................... 12  \n2.2.2 Guidelines for the Assessment of Sarcopenia ................................................................ 13  \n2.3 Gait Analysis in Orthogeriatric Patients .......................................................................... 13  \n2.4 Machine Learning ........................................................................................................... 15  \n2.4.1 Machine Learning for Evaluation of Gait-Analysis and Multidimensional Data............... 15  \n2.5 Current State of Physical Frailty Assessment in Bedridden Orthogeriatric Patients ......... 17  \n2.5.1 Discrepancies Between Clinical and Objective Assessments ........................................ 17  \n2.5.2 Challenges in Conducting Physical Tests in Immobilized Patients................................. 17  \n2.5.3 Need for Ass","cbCaieNZCC9V45n7","https://ap.wps.com/l/cbCaieNZCC9V45n7","pdf",13280412,1,58,"English","en",105,"# Einleitung\n## Definition of Osteosarcopenia\n## Current Tools for Risk Stratification for Osteosarcopenia\n## Guidelines for the Assessment and Treatment of Osteosarcopenia\n## Gait Analysis in Orthogeriatric Patients\n## Machine Learning\n## Current State of Physical Frailty Assessment in Bedridden Orthogeriatric Patients\n# Zusammenfassung\n# Abstract (English)\n# Literaturverzeichnis\n# Anhang B: Danksagung","[{\"question\":\"What is the main objective of the dissertation?\",\"answer\":\"To build predictive models for fall risk in orthogeriatric patients using machine learning techniques.\"},{\"question\":\"How does the dissertation connect fall-risk assessment with gait analysis?\",\"answer\":\"It discusses gait analysis in orthogeriatric patients and how machine learning can evaluate gait-analysis and multidimensional data for risk evaluation.\"},{\"question\":\"What challenges are addressed for frailty assessment in bedridden patients?\",\"answer\":\"It highlights discrepancies between clinical and objective assessments and explains difficulties of conducting physical tests in immobilized patients.\"}]","Predictive Modeling of Fall Risk in Orthogeriatric Patients using Machine Learning Techniques | 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