Abstract: A method for administering an exposure treatment of a cognitive behavioral therapy (CBT) uses a mobile app and a server application. A user state of a patient undergoing a first step of the CBT based on the patient's condition during the first step is detected by sensors of the patient's smartphone. A situational state of the patient's surroundings during the first step is detected by the smartphone sensors. The mobile app determines whether the patient has made progress performing the first step. A user prompt is generated based on the user state and situational state. A next step of the CBT is configured based on the user state and situational state. The characteristics of the user prompt are generated using machine learning based on past task completions by the patient and other users so as to increase the likelihood that the patient will complete the next step of the CBT.

Abstract: A method for administering an exposure treatment of a cognitive behavioral therapy (CBT) uses a mobile app and a server application. A user state of a patient undergoing a first step of the CBT based on the patient's condition during the first step is detected by sensors of the patient's smartphone. A situational state of the patient's surroundings during the first step is detected by the smartphone sensors. The mobile app determines whether the patient has made progress performing the first step. A user prompt is generated based on the user state and situational state. A next step of the CBT is configured based on the user state and situational state. The characteristics of the user prompt are generated using machine learning based on past task completions by the patient and other users so as to increase the likelihood that the patient will complete the next step of the CBT.

Abstract: A system for triggering mental healthcare services based on prediction of critical events receives both structured data and unstructured data about mental healthcare patients. Feature extraction is performed, thereby generating records of structured data, and strings of vectors of unstructured data. The system makes a quality assessment about the structured data, and a quality assessment about the unstructured data. In a model selection step, the system selects one model out of a plurality of selectable models, where the selection is made based on the quality assessments made. The selected model is trained with records of structured data and with strings of vectors of unstructured data. New real-time data is supplied to the trained model so that the trained model predicts whether a crisis event is likely to occur. If the system predicts that a crisis event is likely to occur, then the system outputs an alert.

Abstract: A system for monitoring the reaction of a user and for adjusting output content based on the user's reaction includes an output unit, a monitoring unit, a synchronization unit, an analysis unit and a control unit. The output unit presents content to the user. The monitoring unit monitors a user parameter during a period during which a first content is presented to the user in order to obtain monitoring data from the user. The monitoring data is synchronized during the period with the first content so as to link in time the monitoring data and the first content. The analysis unit analyzes the monitoring data and links it to the first content in order to determine the user's reaction to the first content. The control unit controls the output unit to present a second content to the user that is selected based on the user's reaction to the first content.

Abstract: A system for determining a mental health state of a user and for adjusting output content accordingly includes a monitoring unit that monitors parameters of the user and acquires corresponding data, an analysis unit that extracts features from data acquired by the monitoring unit, a classification unit that detects change in the mental health state of the user based on the features extracted by the analysis unit and classifies the change in mental state of the user, a control unit that adapts the content to be output to the user by the output unit based on the detected change in the mental health state of the user classified by the classification unit, and an output unit that outputs content to the user based on the detected change in the mental health state of the user. A method determines the mental health state of the user and adjusts output content accordingly.

Abstract: A method for administering an exposure treatment of a cognitive behavioral therapy (CBT) uses a mobile app and a server application. A user state of a patient undergoing a first step of the CBT based on the patient's condition during the first step is detected by sensors of the patient's smartphone. A situational state of the patient's surroundings during the first step is detected by the smartphone sensors. The mobile app determines whether the patient has made progress performing the first step. A user prompt is generated based on the user state and situational state. A next step of the CBT is configured based on the user state and situational state. The characteristics of the user prompt are generated using machine learning based on past task completions by the patient and other users so as to increase the likelihood that the patient will complete the next step of the CBT.

Abstract: A method for detecting abnormal behavior involves constructing words and text documents based on data acquired from mobile phone sensors during defined time intervals. The time intervals are defined based on data from mobile phone sensors indicative of usage patterns of the mobile phone user. Words are constructed for each time interval as a vector including the time interval and sensor-based feature levels. Each sensor-based feature level is mapped to a range of values of a sensor-based feature that are extracted from the sensor data. The text document is constructed from the words based on the time intervals and the sensor-based feature levels. A current routine for each time interval is determined using topic modeling based on the words that most frequently appear in the text document. An alert is generated if the current routine for any time interval deviates from a past routine for a corresponding past time interval.

Abstract: A user is validated using first data from sensors on the user's portable device and second data input by the user. The user's digital behavioral fingerprint is generated using the first data. Whether the user's purported identity is authentic is determined by computing an authenticity score using the fingerprint. The user's identity is authentic if the authenticity score is less than a first threshold and potentially not authentic if greater than the first threshold. If potentially not authentic, whether the second data is invalid is determined using a conformity score that compares the second data to prior data of the user and data associated with others. The second data is not invalid if the conformity score is less than a second threshold, but invalid if greater than the second threshold. Access to a service is granted if either the user's identity is authentic or the second data is not invalid.

Abstract: A preventative healthcare system calibrates a risk model by assigning weights to attributes for the freshness, completeness and uncertainty of a user's medical information. A risk predictive model is implemented based on the medical information. The risk of a specific health outcome of the user is determined using the risk predictive model, which is calibrated by computing attribute scores for freshness, completeness and uncertainty of the medical information and by assigning weights to the attribute scores. A need-for-data (ND) score is computed using the weighted attribute scores. A need-for-checkup (NC) score is computed using traits of the user. The method determines that new medical information related to the user is needed or that the user needs a checkup based on the ND and NC scores. A prompt is delivered to the user indicating that new medical information related to the user is needed or that the user needs a checkup.