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ML-Based Fall Risk Prediction to Substitute Personal Assistance for Hospitalized Elderly: Integrating Geriatric Assessment and E-Health Records
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Author(s): Jinu Sara Rajan (Christ University, India)and M. Vinay (Christ University, India)
Copyright: 2025
Pages: 28
Source title:
Assistive Technology Solutions for Aging Adults and Individuals With Disabilities
Source Author(s)/Editor(s): Rajiv Pandey (Amity University, Lucknow, India), Pratibha Maurya (Amity University, Lucknow, India), Jigna Bhupendra Prajapati (Ganpat University, India), Raju Halder (Indian Institute of Technology, Patna, India)and Kanishka Tyagi (UHV Technologies, USA)
DOI: 10.4018/979-8-3693-6308-9.ch013
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Abstract
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.
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