Abstract: The present invention relates generally to wireless, remote, physiological monitoring in the evaluation of health and disease state specifically with respect to cardiac and pulmonary pathologies, including heart failure and sleep apnea. Personalized thresholds are used to enhance performance of classification and prediction methods by utilizing patient clinical history and past empiric sensed data, such as through an initialization period, to learn the biological variation present in each sensed individual. Data from an adherent patch undergoes disseminated processing as data is securely sent from a sensing device ultimately to a remote server. Monitoring, classification, and prediction results are hosted and made accessible through an authenticated system to members of the individual's healthcare team, family, or other caregivers. Learning algorithms are used to characterize physiological attributes and to classify a health or disease state based on an aggregate of features.