![]() Furthermore, with the proliferation of wireless technology, a patient’s necessary biochemical information can be transmitted directly to a clinic from almost anywhere in the world. Wearable sensors provide a new avenue for ensuring patient health by providing physicians with tools to monitor several biochemical metrics, including blood pressure, oxygen saturation, heart rate, and several others, outside of the treatment facility. In order to carry on this evolution, technological advancements continue to be integrated and adopted into the clinic. The advent of innovative technologies has fueled the progression of cutting-edge treatment and quality patient care. ![]() In recent decades, the field of medicine has evolved at an alarming rate. Future studies will seek to deploy this affordable real time location system in hospitals to improve clinical workflow, efficiency, and patient safety. It outperformed a CNN model (accuracy = 94%), a thresholding model employing majority voting (accuracy = 95%), and a triangulation classifier utilizing majority voting (accuracy = 95%). By utilizing temporal information, a combined CNN+ANN network was capable of correctly identifying the location of the BLE tag with an accuracy of 99.9%. The performance of these networks was compared to relative received signal strength indicator (RSSI) thresholding and triangulation. This study focuses on investigating the feasibility of tracking patients and clinical staff wearing Bluetooth Low Energy (BLE) tags in a radiation oncology clinic using artificial neural networks (ANNs) and convolutional neural networks (CNNs). ![]() Deep learning has started to revolutionize several different industries, and the applications of these methods in medicine are now becoming more commonplace.
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