Health risk assessment and decision-making for patient monitoring and decision-support using Wireless Body Sensor Networks

This paper proposes a generalized multi-sensor fusion approach and a health risk assessment and decision-making (Health-RAD) algorithm for continuous and remote patient monitoring purposes using a Wireless Body Sensor Network (WBSN). Health-RAD determines the patient's health condition severity level routinely and each time a critical issue is detected based on vital signs scores. Hence, a continuous health assessment and a monitoring of the improvement or the deterioration of the state of the patient is ensured. The severity level is represented by a risk variable whose values range between 0 and 1. The higher the risk value, the more critical the patient's health condition is and the more it requires medical attention. Moreover, we calculate the score of a vital sign using its past and current value, thus assessing its status based on its evolution during a period of time and not only on sudden deviations. We propose a generalized multi-sensor data fusion approach regardless of the number of monitored vital signs. The latter is employed by Health-RAD to find the severity level of the patient's health condition based on his/her vital signs scores. It is based on a fuzzy inference system (FIS) and early warning score systems (EWS). This approach is tested with a previously proposed energy-efficient data collection approach, thus forming a complete framework. The proposed approach is evaluated on real healthcare datasets and the results are compared with another approach from the literature in terms of data reduction, energy consumption, risk assessment of vital signs, the patient's health risk level determination and accuracy. The results show that both approaches have coherently assessed the health condition of different Intensive Care Unit (ICU) patients. Yet, our proposed approach overcomes the other approach in terms of energy consumption (around 86% less energy consumption) and data reduction (around 70% for sensing and more than 90% for transmission). Additionally, contrary to our proposed framework, the approach taken from the literature requires an offline model building and depends on available patient datasets.