Wistron Corporation

Chih-Yi Chien

Zhe-Yu Lin

Kuan-I Chung

Che-Wen Ku

Yu-Hui Lien

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.

Blood pressure prediction method and electronic device using the same

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.

Training data increment method, electronic apparatus and computer-readable medium

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.

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Taipei, Taiwan

Chih-Yi Chien