Abstract: A training data processing method and an electronic device are provided. The method includes: obtaining medical history data including at least one first disease suffered by a user; setting a plurality of disease types according to a target disease; setting a time interval; obtaining at least one second disease in the time interval from the medical history data; performing a pre-processing operation on the second disease according to the disease types to obtain processed data; and inputting the processed data to a neural network to train the neural network.

Abstract: A training data processing method and an electronic device are provided. The method includes: obtaining medical history data including at least one first disease suffered by a user; setting a plurality of disease types according to a target disease; setting a time interval; obtaining at least one second disease in the time interval from the medical history data; performing a pre-processing operation on the second disease according to the disease types to obtain processed data; and inputting the processed data to a neural network to train the neural network.

Abstract: A method and an electronic device of checking drug interaction are provided. The method includes: generating a first odds ratio (OR) between a first drug combination and a hospitalization event, a second OR between a second drug combination and the hospitalization event, and a third OR between a third drug combination and the hospitalization event according to a plurality of medical records; generating a first fraction corresponding to a first drug according to the second OR; generating a second fraction corresponding to a second drug according to the third OR; and outputting the first drug combination in response to the first OR being greater than a first threshold, a sum of the first fraction and the second fraction being greater than a second threshold, and a quotient of the first fraction and the second fraction being less than a third threshold.

Abstract: A medication risk evaluation method and a medication risk evaluation device are provided. The medication risk evaluation method includes: obtaining first medication route information related to a first medicine combination in a medication database; obtaining second medication route information related to a second medicine combination in the medication database; obtaining overlapping medication information between the first medication route information and the second medication route information; determining whether the overlapping medication information meets a noise exclusion condition; and if the overlapping medication information meets the noise exclusion condition, establishing a risk evaluation model based on other medication route information in the medication database excluding the first medication route information, where the risk evaluation model is adapted to evaluate a risk of using at least one medicine in the medication database.

Abstract: A method for calculating a high risk route of administration is provided. Multiple arrangement routes composed of every two medicines among multiple medicines included in a medical record database are listed. A risk value of each arrangement route is calculated by querying the medical record database based on a specified medication result. A risk score of each arrangement route is calculated according to the risk value, and the arrangement routes are sorted based on the risk scores. Starting from the arrangement route with the highest risk score, N arrangement routes are retrieved and a combination on N of the arrangement routes is performed to obtain multiple strung routes. The number of medicines included in each strung route matches a specified medication number.

Abstract: A training data processing method and an electronic device are provided. The method includes: obtaining medical history data including at least one first disease suffered by a user; setting a plurality of disease types according to a target disease; setting a time interval; obtaining at least one second disease in the time interval from the medical history data; performing a pre-processing operation on the second disease according to the disease types to obtain processed data; and inputting the processed data to a neural network to train the neural network.