Publications by authors named "Ping-Yen Yang"
Using large language model (LLM) to identify high-burden informal caregivers in long-term care.
Comput Methods Programs Biomed
October 2024
Article Synopsis
- There are more older people in the world now, which means we need more help for those taking care of them, but current methods might not find all the issues these caregivers face.
- The study looked at how well a computer program called a Large Language Model (LLM) can spot when caregivers are feeling overwhelmed, comparing it to older methods.
- The results showed that the LLM was better at identifying stressed caregivers, suggesting it could improve how we understand and support people in long-term care.
Background: Quantifying the informal caregiver burden is important for understanding the risk factors associated with caregiver overload and for evaluating the effectiveness of services provided in Long-term Care (LTC).
Objective: This study aimed to develop and validate a Caregiver Strain Index (CSI)-based score for quantifying the informal caregiver burden, while the original dataset did not fully cover evaluation items commonly included in international assessments. Subsequently, we utilized the CSI-based score to pinpoint key caregiver burden risk factors, examine the initial timing of LTC services adoption, and assess the impact of LTC services on reducing caregiver burden.
Background: Long-term care (LTC) service demands among cancer patients are significantly understudied, leading to gaps in healthcare resource allocation and policymaking.
Objective: This study aimed to predict LTC service demands for cancer patients and identify the crucial factors.
Methods: 3333 cases of cancers were included.
Background: An aging society incurs great losses due to fall-related injuries and mortalities. The foreseeable increased burden of fall-related injury among older people requires a regular nationwide study on the fall epidemic and prevention strategies.
Methods: The fall epidemic was examined using data from three consecutive waves of the National Health Interview Survey (2005, 2009, and 2013).