Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Rajan, Jinu Sara; Vinay, M. Title A name given to the resource ML-Based Fall Risk Prediction to Substitute Personal Assistance for Hospitalized Elderly: Integrating Geriatric Assessment and E-Health Records Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Assistive Technology Solutions for Aging Adults and Individuals with Disabilities;pp.281-308 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.4018/9798369363089.ch013" target="_blank" rel="noreferrer noopener">https://doi.org/10.4018/9798369363089.ch013</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105007277384?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105007277384?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Rajan J.S., Christ University, India; Vinay M., Christ University, India Description An account of the resource Geriatric assessment serves as a holistic evaluation tool, encompassing various aspects of the elderly individual's health, including physical function, cognition, and psychosocial factors. Integration of CGA data with EHRs allows for a comprehensive analysis of the individual's health status and medical history, providing valuable insights into their risk factors for falls. The ML-based predictive model developed in this study utilizes these integrated data sources to identify patterns and trends associated with fall occurrences among hospitalized elderly patients. By analysing various variables, including mobility indicators, medication usage, and previous fall history, the model can generate accurate predictions of fall risks for individual patients. This ML-driven approach has the potential to significantly improve patient safety and quality of care by enabling healthcare providers to pre-emptively identify and address fall risks among hospitalized elderly individuals, thereby reducing the reliance on constant personal assistance while ensuring optimal patient outcomes. 2025 by IGI Global Scientific Publishing. Publisher An entity responsible for making the resource available IGI Global Relation A related resource ISBN: 979-836936309-6; 979-836936308-9; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online