Abstract: A fish identification method is provided. The fish identification method includes capturing an image through a processor, wherein the image includes a fish image. The fish identification method includes identifying a plurality of feature points of the fish image through a coordinate detection model and obtaining a plurality of sets of feature-point coordinates. Each of the plurality of sets of feature-point coordinates corresponds to each of the plurality of feature points. The fish identification method further includes calculating a body length or an overall length of the fish image according to the plurality of sets of feature-point coordinates of the image.

Abstract: An intradialytic analysis method and an analysis apparatus for dialysis are provided. One or more input features are obtained. The input features include variance relation between current data and previous data of an operating parameter related to a dialysis machine and data related to a tester. Future data is predicted according to the input features through one or more prediction models. The future data includes blood pressure information and a predicted result of intradialytic hypotension at a future time point. Therefore, intradialytic hypotension can be predicted with high accuracy.

Abstract: A blood pressure prediction method and an electronic device using the same are provided. The method includes the following steps. A training data set is collected. A first blood pressure prediction model is established according to the training data set. Hemodialysis parameter data of a target patient is received, wherein the hemodialysis parameter data includes a first hemodialysis parameter at a previous time point and a second hemodialysis parameter at a current time point. A hemodialysis parameter variation amount between the first hemodialysis parameter and the second hemodialysis parameter is calculated. The hemodialysis parameter variation amount is provided to the first blood pressure prediction model to generate a prediction blood pressure variation associated with a next time point. An operation is performed according to the prediction blood pressure variation of the target patient.

Abstract: A training data increment method, an electronic apparatus and a computer-readable medium are provided. The training data increment method is adapted for the electronic apparatus and includes the following steps. A training data set is obtained, wherein the training data set includes a first image and a second image. An incremental image is generated based on the first image and the second image. A deep learning model is trained based on the incremental image.

Abstract: A dehydration amount prediction method for hemodialysis and an electronic device using the same are provided, and the method includes the following steps. Physiological data and hemodialysis treatment data of a first patient are obtained. The first patient is determined to belong to one of groups, wherein the groups are respectively associated with a plurality of prediction models. A target prediction model corresponding to the one of the groups is selected from the prediction models. The physiological data and the hemodialysis treatment data are provided to the target prediction model to generate a recommended dehydration amount by the target prediction model.

Abstract: A training data increment method, an electronic apparatus and a computer-readable medium are provided. The training data increment method is adapted for the electronic apparatus and includes the following steps. A training data set is obtained, wherein the training data set includes a first image and a second image. An incremental image is generated based on the first image and the second image. A deep learning model is trained based on the incremental image.

Abstract: A dehydration amount prediction method for hemodialysis and an electronic device using the same are provided, and the method includes the following steps. Physiological data and hemodialysis treatment data of a first patient are obtained. The first patient is determined to belong to one of groups, wherein the groups are respectively associated with a plurality of prediction models. A target prediction model corresponding to the one of the groups is selected from the prediction models. The physiological data and the hemodialysis treatment data are provided to the target prediction model to generate a recommended dehydration amount by the target prediction model.

Abstract: A blood pressure prediction method and an electronic device using the same are provided. The method includes the following steps. A training data set is collected. A first blood pressure prediction model is established according to the training data set. Hemodialysis parameter data of a target patient is received, wherein the hemodialysis parameter data includes a first hemodialysis parameter at a previous time point and a second hemodialysis parameter at a current time point. A hemodialysis parameter variation amount between the first hemodialysis parameter and the second hemodialysis parameter is calculated. The hemodialysis parameter variation amount is provided to the first blood pressure prediction model to generate a prediction blood pressure variation associated with a next time point. An operation is performed according to the prediction blood pressure variation of the target patient.