Mobile 5P-Medicine Approach for Cardiovascular Patients.

Medicine is heading towards personalized care based on individual situations and conditions. With smartphones and increasingly miniaturized wearable devices, the sensors available on these devices can perform long-term continuous monitoring of several user health-related parameters, making them a powerful tool for a new medicine approach for these patients. Our proposed system, described in this article, aims to develop innovative solutions based on artificial intelligence techniques to empower patients with cardiovascular disease. These solutions will realize a novel 5P (Predictive, Preventive, Participatory, Personalized, and Precision) medicine approach by providing patients with personalized plans for treatment and increasing their ability for self-monitoring. Such capabilities will be derived by learning algorithms from physiological data and behavioral information, collected using wearables and smart devices worn by patients with health conditions. Further, developing an innovative system of smart algorithms will also focus on providing monitoring techniques, predicting extreme events, generating alarms with varying health parameters, and offering opportunities to maintain active engagement of patients in the healthcare process by promoting the adoption of healthy behaviors and well-being outcomes. The multiple features of this future system will increase the quality of life for cardiovascular diseases patients and provide seamless contact with a healthcare professional.

Towards Detecting Pneumonia Progression in COVID-19 Patients by Monitoring Sleep Disturbance Using Data Streams of Non-Invasive Sensor Networks.

Pneumonia caused by COVID-19 is a severe health risk that sometimes leads to fatal outcomes. Due to constraints in medical care systems, technological solutions should be applied to diagnose, monitor, and alert about the disease's progress for patients receiving care at home. Some sleep disturbances, such as obstructive sleep apnea syndrome, can increase the risk for COVID-19 patients. This paper proposes an approach to evaluating patients' sleep quality with the aim of detecting sleep disturbances caused by pneumonia and other COVID-19-related pathologies. We describe a non-invasive sensor network that is used for sleep monitoring and evaluate the feasibility of an approach for training a machine learning model to detect possible COVID-19-related sleep disturbances. We also discuss a cloud-based approach for the implementation of the proposed system for processing the data streams. Based on the preliminary results, we conclude that sleep disturbances are detectable with affordable and non-invasive sensors.