Abstract: Various examples are provided related to mHealth based risk stratification. In one example, a system includes a handheld echocardiography device that can generate ultrasound (US) images of a patient and processing circuitry comprising a processor and memory. The processing circuitry can receive the US images from the handheld echocardiography device; generate enhanced echo images from the US images using a generative adversarial network (GAN) model; and determine a major adverse cardiac event (MACE) risk for the patient based upon the enhanced echo images. In another example, a method includes receiving US images of a patient obtained with a handheld echocardiography device; generating enhanced echo images from the US images using a generative adversarial network (GAN) model; and determining a major adverse cardiac event (MACE) risk for the patient based upon the enhanced echo images.

Abstract: Various examples are provided related to synthetic echocardiography. In one example, a method includes receiving surface electrocardiography (ECG) signals obtained from a patient; synthesizing, through a machine learning model, a 3D model of a heart based upon the surface ECG signals; and generating a rendering of the heart based upon the synthesized model of the heart. In another example, a system includes a wearable monitoring device that can collect and transmit surface ECG signals; and a computing device that can receive the surface ECG signals obtained from a patient using the wearable monitoring device; synthesize, through a machine learning model, a 3D model of a heart based upon the surface ECG signals; and generate a rendering of the heart based upon the synthesized model of the heart. The rendering of the heart can be displayed locally (e.g., by the computing device) or transmitted to a user device for display.

Abstract: An appointment scheduling system configured to predict the likelihood of a missed appointment during the scheduling process, thereby minimizing missed appointments and improving access to care is discussed. In particular, artificial intelligence/machine-learning techniques are used to more efficiently utilize a provider's time and a patient's needs and to help optimize scheduling at healthcare facilities. A predictive model can aid in prioritizing the design and implementation of interventions that may improve efficiency towards more timely access to care at healthcare facilities. In particular, patients can be provided with an earliest appointment, closer to his or her home. In addition, patient and provider satisfaction with scheduling services can be improved.

Abstract: Systems and methods for sleep apnea and nocturnal hypoxia detection are described. In one non-limiting example, a pulse oximetry device can include a sensor and a computing device. The computing device can be attached to a patient for a sleep apnea diagnosis. The computing device can be configured to generate pulse oximetry data by measuring pulse oximetry of a patient with the sensor. Multiple apnea indicators can be identified from the pulse oximetry data and from information associated with the patient. The multiple sleep apnea indicators can be provided to a machine learning model trained for sleep apnea prediction. A sleep apnea classification can be determined from the machine learning model.