Abstract: An exemplary method for predicting one or more adipose depots for a patient includes receiving one or more Dual-energy X-ray Absorptiometry (DEXA) scans comprising at least a portion of a torso of the patient; providing at least one or more portions of the one or more DEXA scans to a trained machine-learning model, wherein the machine-learning model is trained using a training dataset comprising: a plurality of training DEXA scans of a plurality of subjects and a plurality of corresponding Magnetic Resonance Imaging (MRI)-image-based adiposity scores of the plurality of subjects; and predicting the one or more adipose depots for the patient utilizing the trained machine-learning model.

Abstract: Provided herein are lists of combined host-viral gene markers that can be used for identifying COVID-19 in a subject and/or determining severity of disease. The host-viral diagnostic methods, compositions and systems disclosed herein are used to classify human subjects pre-diagnosis, with or without symptoms of COVID-19, based on the expression levels of the identified gene markers.

Abstract: According to one or more embodiments herein, a machine-learning algorithm is provided that uses current and past clinical data in order to accurately and precisely predict the future onset of mild cognitive impairment (MCI) and dementia for individual patients, thus enabling early identification of those having high risk for Alzheimer's disease. In particular, a newly defined “All-Pairs” technique combines data from each doctor's visit for each patient with data from each of the patient's other doctor's visits. By correlating clinical data obtained from patients at one time point with the progression of Alzheimer's disease in the future, the techniques herein are able to increase the likelihood of identifying Alzheimer's disease patients at early stages.

Abstract: According to one or more embodiments herein, a machine-learning algorithm is provided that uses current and past clinical data in order to accurately and precisely predict the future onset of mild cognitive impairment (MCI) and dementia for individual patients, thus enabling early identification of those having high risk for Alzheimer's disease. In particular, a newly defined “All-Pairs” technique combines data from each doctor's visit for each patient with data from each of the patient's other doctor's visits. By correlating clinical data obtained from patients at one time point with the progression of Alzheimer's disease in the future, the techniques herein are able to increase the likelihood of identifying Alzheimer's disease patients at early stages.