Machine learning approach to predict patient position for preventing bedsores

Abstract

The Agency for Healthcare Research & Quality estimates more than 2.5 million individuals in the United States develop bedsores annually that cost $9.1-$11.6 billion to the healthcare system. In this paper, we develop a low-cost solution to reduce the risk of bedsores for bedridden patients using machine learning. Elderly patients with mobility impairments are the highest risk population segment for bedsores (also known as pressure ulcers). Currently, smart beds that send alarms when patients have not changed their position in bed for a long time are used to manage the risk of developing bedsores. However, such smart devices are cost prohibitive. The proposed affordable solution uses low-cost load-cells’ readings to accurately estimate the patient position with an accuracy of 98.8%. Specifically, the solution manages bedsore risk by deriving meaningful, intuitive features that are used by the machine learning model to generate alerts when a patient has been in the same position for a prolonged period of time.