A latent variable autoregression model tracking weight, blood pressure, and heart rate detected signs of health decline days earlier than existing rule-based systems in patients with CHF.
Does a latent variable autoregression model improve the early detection of health decline in patients with congestive heart failure compared to existing rule-based systems?
A latent variable autoregression model tracking weight, blood pressure, and heart rate can detect health decline in CHF patients days earlier than traditional rule-based systems.
Sudden weight gain in patients living with Congestive Heart Failure (CHF) is often an indication that the individual is retaining fluid, which often means that patient's heart has weakened leading to increased risk of kidney or cardiac failure. Clinical interventions can be made at this stage, leading to better outcomes, however it is essential that the interventions take place before the patient's health declines too drastically. In this work, we present a latent variable autoregression model that tracks patient weight and blood pressure over time, allowing us to predict weight values into the future. We are also able to model continuous heart-rate signals and evaluate a subject's response to physical activity. This allows us to detect signs of health decline days earlier than existing rule-based systems, leading to the possibility of earlier clinical interventions, potentially preventing deadly medical emergencies.
Fisher et al. (Thu,) conducted a other in Congestive Heart Failure (CHF). Latent variable autoregression model vs. Existing rule-based systems was evaluated on Detection of signs of health decline. A latent variable autoregression model tracking weight, blood pressure, and heart rate detected signs of health decline days earlier than existing rule-based systems in patients with CHF.
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