Towards the Use of Artificial Intelligence Techniques in Biomedical Data from an Integrated Portable Medical Assistant to Infer Asymptomatic Cases of COVID-19

This work presents the proposal of usage of Artificial Intelligence (AI) algorithms based on Deep Neural Network (DNN) models in biomedical data collected from an Integrated Portable Medical Assistant (IPMA) to aid the diagnosis of COVID-19. IPMA uses AI, Telemedicine technology, and sensors to measure the user’s Heart Rate (HR), Blood Pressure (BP), Oxygen Saturation Level (SPO2 - Peripheral Capillary Oxygen Saturation) and Temperature (T). Oxygen saturation level is associated with the difficulty in breathing and shortness of breath reported by patients with more severe cases of COVID-19, and body temperature allows the detection of fever, another of the symptoms of COVID-19. On the other hand, blood pressure is a risk factor for patients with COVID-19, and changes in HR can also be an indicator of contamination by COVID-19 of asymptomatic people. AI algorithms are used to analyze the data collected from asymptomatic users and infer a possible contamination by COVID-19, also indicating to the user when it is time to go to the emergency room or seek hospital care.