Abstract: Systems and methods of progression (e.g. continuous ongoing and long term) assessment of a neurological disease of a patient, including: receiving, from a device that is wearable by the patient, 3D acceleration data and at least one physiological signal; determining, by a server, with a dedicated deep learning algorithm a progression of a neurological disease of the patient based on the received 3D acceleration data and the at least one physiological signal, wherein determination of the progression of the neurological disease includes: determining a first metric for patient's activity, determining a second metric based on a measured gait speed, and aggregating the first metric and the second metric into a combined approximation of progression of the neurological disease.

Abstract: A method of predicting a healthcare event includes: receiving via an input device, classifying personal information for each of a plurality of persons; collecting measurements of at least one health indicator during a predefined learning period; creating a personal physiological pattern profile, based on the collected data; associating each of the plurality of persons to a physiological cluster based on each person's personal physiological pattern profile and based on the classifying personal information of each of the plurality of persons; creating, for each physiological cluster, a health indicator deviation pattern for the healthcare event; continuously monitoring values of the health indicator of the person; and determining an occurrence probability of the healthcare event when the monitored indicators deviate from the personal physiological pattern profile. A system for predicting a healthcare event is also disclosed.

Abstract: A method of predicting a healthcare event includes: receiving via an input device, classifying personal information for each of a plurality of persons; collecting measurements of at least one health indicator during a predefined learning period; creating a personal physiological pattern profile, based on the collected data; associating each of the plurality of persons to a physiological cluster based on each person's personal physiological pattern profile and based on the classifying personal information of each of the plurality of persons; creating, for each physiological cluster, a health indicator deviation pattern for the healthcare event; continuously monitoring values of the health indicator of the person; and determining an occurrence probability of the healthcare event when the monitored indicators deviate from the personal physiological pattern profile. A system for predicting a healthcare event is also disclosed.