Abstract: A method, computer system, and a computer program product for digital image recognition determination using a learned model is provided. The present invention may include acquiring a first determination result by making a determination concerning first data, using a first learned model. The present invention may include selecting a partial region of the first data. The present invention may then include generating second data obtained by applying a first alteration process to the partial region. The present invention may also include acquiring a second determination result by making a determination concerning the second data, using a second learned model. The present invention may lastly include obtaining a final determination result based on the first determination result and the second determination result.

Abstract: A method, computer system, and a computer program product for predicting a variation of sequential blood glucose levels by using deep learning is provided. The present invention may include training a predictor associated with a user by using a deep learning network. The present invention may further include predicting a plurality of sequential blood glucose levels by the trained predictor based on at least one meal image, at least one time-period, and at least one set of data associated with a plurality of blood glucose levels of the user.

Abstract: A method, computer system, and a computer program product for digital image recognition determination using a learned model is provided. The present invention may include acquiring a first determination result by making a determination concerning first data, using a first learned model. The present invention may include selecting a partial region of the first data. The present invention may then include generating second data obtained by applying a first alteration process to the partial region. The present invention may also include acquiring a second determination result by making a determination concerning the second data, using a second learned model. The present invention may lastly include obtaining a final determination result based on the first determination result and the second determination result.

Abstract: A method, computer system, and computer program product for predicting an occurrence of a symptom in a patient are provided. The embodiment may include reading, into a memory, a plurality of time-series prediction models used for predicting the occurrence of the symptom, wherein the time-series prediction models were trained in advance using plural data sets of training data obtained from a plurality of patients, each training data comprising prodrome data and data associated with the occurrence of the symptom. The embodiment may also include selecting at least one time-series prediction model from the time-series prediction models using historical data sets of prodrome data obtained from a patient and data associated with the occurrence of the symptom. The embodiment may further include inputting, to the at least one selected time-series prediction model, current prodrome data obtained from the patient to output a result predicting the occurrence of the symptom.

Abstract: A method, computer system, and a computer program product for predicting a variation of sequential blood glucose levels by using deep learning is provided. The present invention may include training a predictor associated with a user by using a deep learning network. The present invention may further include predicting a plurality of sequential blood glucose levels by the trained predictor based on at least one meal image, at least one time-period, and at least one set of data associated with a plurality of blood glucose levels of the user.