Integration of Artificial Intelligence in Medical Instrumentation for Real-Time Patient Monitoring

The utilization of real-time, artificial intelligence (AI)-integrated patient monitoring instruments is expanding significantly. Throughout the hospital stay, acutely sick patients are at an elevated risk of clinical deterioration. In triage, it is challenging to select the most acutely unwell out of a various population of aged citizens. In major hospitals, the total number of cases treated on a day-to-day basis can exceed 1000. Monitoring and observing patients in these healthcare contexts present a serious effort. On the other hand, the smart expansion of remote patient monitoring (RPM) practices can lead to a more predictive and preventive capacity in hospitals. It is ideal to collect the measurements on signal quality from the patient continuously in the home surroundings using a wearable apparatus.

Several machine learning methods have been integrated to automatically make predictions such as patient repose bed exit, patient repose bed exit present bed future re-admission, patient-specific future step-count, etc. Linear regression, random forest classifier, and logistic regression were incorporated for emulating the analytical calculation of a doctor or a nurse. A Bayesian optimization method was implemented to enable input into a distant setting and accommodate any plausible parameterization in the model inside the patient population. Multiple models generate the patient-specific parameter optimization using machine learning techniques is the most advanced level of RPM. This renders it possible to build patient-specific models that will break a patient’s health status down to the lowest level.