Exploring Temporal Features in Health Records for Frailty Detection

• Julia de Enciso García ^[1] ; Matteo Leghissa ^[1] ; Oscar Araque ^[1] ; Álvaro Carrera Barroso ^[1]
  1. [1] Universidad Politécnica de Madrid

     Universidad Politécnica de Madrid

     Madrid, España

     
• Localización: CASEIB 2024. Libro de Actas del XLII Congreso Anual de la Sociedad Española de Ingeniería Biomédica, 2024, ISBN 978-84-09-67332-2, págs. 273-276
• Idioma: inglés
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• Resumen

  • The global population is rapidly aging, which poses significant challenges for healthcare systems worldwide, including increased costs and a rising demand for effective geriatric care. Addressing these challenges requires innovative approaches to improve the early detection and prediction of frailty among elderly individuals, with the aim of alleviating healthcare burdens and enhancing quality of life. This study focuses on the development of a machine learning system designed to improve the early detection and prediction of frailty in elderly populations. To achieve this, we used the FRELSA dataset, a frailty-specific dataset derived from ELSA, an influential longitudinal study on aging with nine waves of data collection and more than 5000 participants. The research begins by optimizing clinical data collection through feature extraction to enhance efficiency in frailty assessment. Various machine learning techniques, including Multilayer Perceptrons (MLPs) and Convolutional Neural Networks (CNNs), are evaluated for their ability to predict frailty based on the identified features. Additionally, the study explores temporal dependencies within the data to gain insights into the progression of frailty and to facilitate more personalized patient care approaches. A comparative analysis with existing baseline models highlights the superior performance of the proposed algorithms in the early detection and prediction of frailty. These findings contribute significantly to advancing the field and lay the foundation for future research aimed at implementing advanced clinical decision support systems in geriatric care settings.