A computer-implemented method for generating a prediction model for detection of panic attacks for a user, comprising: obtaining previously stored first physiological data, which have been generated by a wearable device worn by the user, that indicates a physiological condition of the user; generating a prediction model for the user based on the first physiological data, wherein generating the prediction model comprises: generating panic matrices and non-panic matrices from the first physiological data; generating at least one regression model for the user using second physiological data, which have been obtained while the user was performing physical activities without any panic attacks; generating one or more delta matrices using the at least one regression model and the panic matrices and non-panic matrices, wherein a delta matrix represents a difference between expected values of physiological data with an amount of physical activity and actual physiological values observed; determining a change point within a respective time-series data of each physiological variable to generate change point matrices; generating summary statistics from the change point matrices; creating feature vectors using combinations of the summary statistics and assigning class labels to each feature vector; producing and evaluating a collection of models by enumerating possible combinations of the summary statistics; constructing a normal model using a training dataset; selecting a threshold parameter by using a validation set; and selecting and storing the prediction model. obtaining previously stored first physiological data, which have been generated by a wearable device worn by the user, that indicates a physiological condition of the user; generating a prediction model for the user based on the first physiological data, wherein generating the prediction model comprises: generating panic matrices and non-panic matrices from the first physiological data; generating at least one regression model for the user using second physiological data, which have been obtained while the user was performing physical activities without any panic attacks; generating one or more delta matrices using the at least one regression model and the panic matrices and non-panic matrices, wherein a delta matrix represents a difference between expected values of physiological data with an amount of physical activity and actual physiological values observed; determining a change point within a respective time-series data of each physiological variable to generate change point matrices; generating summary statistics from the change point matrices; creating feature vectors using combinations of the summary statistics and assigning class labels to each feature vector; producing and evaluating a collection of models by enumerating possible combinations of the summary statistics; constructing a normal model using a training dataset; selecting a threshold parameter by using a validation set; and selecting and storing the prediction model. generating panic matrices and non-panic matrices from the first physiological data; generating at least one regression model for the user using second physiological data, which have been obtained while the user was performing physical activities without any panic attacks; generating one or more delta matrices using the at least one regression model and the panic matrices and non-panic matrices, wherein a delta matrix represents a difference between expected values of physiological data with an amount of physical activity and actual physiological values observed; determining a change point within a respective time-series data of each physiological variable to generate change point matrices; generating summary statistics from the change point matrices; creating feature vectors using combinations of the summary statistics and assigning class labels to each feature vector; producing and evaluating a collection of models by enumerating possible combinations of the summary statistics; constructing a normal model using a training dataset; selecting a threshold parameter by using a validation set; and selecting and storing the prediction model.