Abstract: A DRGs automatic grouping method based on convolutional neural network, including: collecting case data and grouping according to a major diagnostic broad categories and core diagnosis-related grouping method; performing numerical coding to the data; constructing a shallow convolutional neural network model, using a k-means clustering method to cluster the feature vectors extracted from the convolutional network to obtain k category labels, combining the category labels and classifier to supervise the network performing iterative training; after finishing training the model, perform data grouping application. The method of the present disclosure is used to avoid the disadvantages of manual feature selection and additional data labeling for adding new grouping categories, automatic learning grouping can be performed for data with vague and difficult groupings.

Abstract: A chronic disease prediction system based on a multi-task learning model. The system includes a computer memory, a computer processor and a computer program which is stored in the computer memory and executable on the computer processor, wherein a trained chronic disease prediction model is stored in the computer memory, and the chronic disease prediction model is composed of a shared layer convolutional neural network and a plurality of chronic disease branch networks; and when executing the computer program, the computer processor implements the following steps: preprocessing a to-be-predicted physical examination record and then inputting the record into the shared layer convolutional neural network of the chronic disease prediction model for feature extraction to obtain a feature map, and inputting the obtained feature map into each chronic disease branch network and performing feature extraction and prediction respectively to obtain a chronic disease prediction result.

Abstract: A speaker recognition system comprises (i) at least one microphone operable to output data representing speech of a speaker and (ii) a controller. The controller is operable to: (a) receive the data output from the at least one microphone; (b) process the received data using a first artificial neural network to obtain first output data, the first artificial neural network having been trained based on outputs of a second artificial neural network, the second artificial neural network having been trained to perform speaker recognition; and (c) identify the speaker using the first output data. The first artificial neural network comprises fewer layers and/or fewer parameters than the second artificial neural network. The first artificial neural network is configured to emulate a result derivable using an output of the second artificial neural network.

Abstract: A speaker recognition system comprises (i) at least one microphone operable to output data representing speech of a speaker and (ii) a controller. The controller is operable to: (a) receive the data output from the at least one microphone; (b) process the received data using a first artificial neural network to obtain first output data, the first artificial neural network having been trained based on outputs of a second artificial neural network, the second artificial neural network having been trained to perform speaker recognition; and (c) identify the speaker using the first output data. The first artificial neural network comprises fewer layers and/or fewer parameters than the second artificial neural network. The first artificial neural network is configured to emulate a result derivable using an output of the second artificial neural network.