Abstract: A method of analyzing a patient's mental state, by automatically processing, integrating, and analyzing text-based and audio-based sources from the patient and clinical staff, and generating real-time outcomes and predictions. A system for processing, analyzing, and managing a patient's input including a data pool, model HUB, search service, topic modeling service, mental health related prediction service, and analytics all in electronic communication. A method of analyzing a patient, by processing, analyzing, and managing a patient's and clinical staff's text and audio input, and informing and augmenting diagnostic and prognostic processes, identifying improvement and deterioration of a patient's mental state, and identifying adverse events in psychotherapy, counseling, and other mental health management activities. A system for processing, analyzing, and managing clinical and diagnostic texts, and audio transcripts.

Abstract: A system for monitoring patients during a consciousness-altering therapeutic treatment session including a data collection module in electronic communication with network servers for storage of data on non-transitory computer readable media for monitoring a patient's well-being during and after the treatment session. A method of using the system in treating a patient, by continuously, continually, or at the healthcare professionals discretion monitoring the well-being of the patient during a consciousness-altering therapeutic treatment session through one or more wearable devices and a patient mobile device in electronic communication with a facilitator mobile device, and continuously monitoring the well-being of the patient after the treatment session with the wearable device.

Abstract: A method of data collection of stool data via a mobile device operable to enable monitoring of gastrointestinal function. A related method of long-term monitoring of patient gastrointestinal function, using one or more signal processing tools (e.g. machine learning algorithms) for automatically interpreting patient stool data, including real-time patient-assessments, in order to detect an adverse clinical event from patient stool data. A system for facilitating real-time monitoring the gastrointestinal function, the system comprising: a camera on a mobile device, a user interface that facilitates self-monitoring of stool characteristics, so as to create health-monitoring data; mobile device storage, server storage, and remote storage (with at least one communication link between them) for storing some or all of the health-monitoring data; and a processor for interpreting such health-monitoring data for clinical or other health-monitoring application.

Abstract: Disclosed in the present disclosure is a method, system and apparatus for prediction, estimation and prevention of occurrence of agitation episode in a subject predisposed to agitation. The method comprises receiving, from a first monitoring device attached to a subject, physiological data of sympathetic nervous system activity in the subject and activity data of the subject; receiving, from a computing device, a plurality of indications associated with a plurality of agitation episodes of the subject; analyzing, using at least one machine learning model, the physiological data, the activity data, and the plurality of indications to determine a probability of an occurrence of an agitation episode of the subject; and sending a signal to a second monitoring device to notify the second monitoring device of the probability of the occurrence of the agitation episode of the subject such that treatment can be provided to the subject to decrease sympathetic nervous system activity in the subject.

Abstract: In some embodiments, a method includes receiving first physiological data of sympathetic nervous system activity and establishing a baseline value of at least one physiological parameter by training at least one machine learning model using the first physiological data. The method further includes receiving, from a first monitoring device attached to a subject, second physiological data of sympathetic nervous system activity in the subject. Using the at least one machine learning model and based on the baseline value of at least one physiological parameter, the method includes analyzing the second physiological data to predict an agitation episode of the subject and sending a signal to a second monitoring device to notify of the prediction of the agitation episode of the subject such that treatment can be provided to the subject to decrease sympathetic nervous system activity in the subject.

Abstract: A method of data collection of stool data via a mobile device operable to enable monitoring of gastrointestinal function. A related method of long-term monitoring of patient gastrointestinal function, using one or more signal processing tools (e.g. machine learning algorithms) for automatically interpreting patient stool data, including real-time patient-assessments, in order to detect an adverse clinical event from patient stool data. A system for facilitating real-time monitoring the gastrointestinal function, the system comprising: a camera on a mobile device, a user interface that facilitates self-monitoring of stool characteristics, so as to create health-monitoring data; mobile device storage, server storage, and remote storage (with at least one communication link between them) for storing some or all of the health-monitoring data; and a processor for interpreting such health-monitoring data for clinical or other health-monitoring application.