Abstract: Provided herein is a method for diagnosing and treating a subject at risk for atrial fibrillation (AF) or related health conditions, the method including: collecting one or more physiological signals from the subject in a sleep state or an awake state; extracting time series data from the one or more physiological signals; performing dynamic analyses of the time series data using artificial intelligence, wherein the artificial intelligence calculates a series of dynamic measurements, said dynamic measurements being indicative of a probability of an onset of an abnormal atrial rhythm; providing an integrated personalized risk score including the dynamic measurements, wherein the integrated personalized risk score is indicative of a probability of an onset of AF in the subject; diagnosing the subject as being at risk for AF when the integrated personalized risk score exceeds a threshold value, wherein the threshold value is calculated by the artificial intelligence based on a library of stored data; and treatin

Abstract: Some embodiments of the invention include computer implemented methods for generating dosage alert values for a drug. Some embodiments include dosage alert values xmin and ymin for a drug, as determined by methods described herein. Other embodiments of the invention include non-transitory computer-readable media comprising computer program instructions executable by a computer processor to execute a method for generating dosage alert values for a drug. Still other embodiments of the invention include computer implemented methods for generating dosage alerts for a drug. Yet other embodiments of the invention include non-transitory computer-readable media comprising computer program instructions executable by a computer processor to execute a method for generating dosage alerts for a drug. Additional embodiments of the invention are also discussed herein.