Abstract: A clinical recommendation method, a clinical recommendation apparatus, and a computer-readable recording medium are provided. In the method, Each medical parameter is determined as an independent event. Each medical parameter has an actual diagnosis. The independent event is defined that a medical parameter is independent of the actual diagnosis thereof. Multiple reference probabilities of each medical parameter are determined based on a probabilistic model. Each reference probability is a probability of one medical parameter in a condition where one reference diagnosis occurs. Multiple final probabilities of reference diagnoses are determined according to the reference probabilities of the medical parameters. A recommendation is determined according to the final probabilities of the reference diagnoses. Accordingly, a proper recommendation may be provided.

Abstract: A method and an apparatus related to medication list management are provided. The medication association between one or more diagnoses and one or more corresponding medications recorded in a medical record is determined through an evaluating model. The evaluating model is trained through a machine learning algorithm. Multiple medication categories are integrated into the medical record based on the medication association. The medication includes one or both of a first medication and a second medication. The medication categories include an explained category related to the first medication with higher medication association and an unexplained category related to the second medication with lower medication association. A medication list interface presenting the medical record with multiple medication categories is provided. Accordingly, the medication history would be structured, grouped, and visual-encoded, so as to provide an intuitive medication list.

Abstract: The disclosure provides a method for providing visualized clinical information and an electronic device.

Abstract: An electronic device and a method for providing a recommended diagnosis are provided. The method includes the following. A first similarity between a first diagnosis and a first medical parameter is obtained. A medical record of a patient is obtained. In response to the medical record including the first medical parameter, a recommended diagnosis list corresponding to the medical record is generated based on the first similarity, and the recommended diagnosis list includes the first diagnosis corresponding to the first similarity. The recommended diagnosis list is output.

Abstract: A clinical association evaluating apparatus and a clinical association evaluating method are provided. In the method, association coefficients between multiple diseases and medicines are determined through an evaluating model. The evaluating model is trained through a machine learning algorithm. The first association between each medicine and the disease with the highest association coefficient is maintained, and the second association between each medicine and the disease without the highest association coefficient is disconnected. The association coefficients between the medicines and the diseases are modified according to the maintained first association and the disconnected second association. Accordingly, the modified association coefficients are adapted for clinical application, and false associations could be removed.

Abstract: The present invention relates to a volume-of-interest segmentation system for use with molecular imaging quantization, which includes: a set of template for segmentation of organ/anatomical region-of-interest, an animal size/weight/location calculator, an animal spine position calculator, and a texture analyzer. It can be used for saving time and reducing cost for segmentation of interested regions and increasing the precision of molecular image quantization.

Abstract: The present invention relates to a volume-of-interest segmentation system for use with molecular imaging quantization, which includes: a set of template for segmentation of organ/anatomical region-of-interest, an animal size/weight/location calculator, an animal spine position calculator, and a texture analyzer. It can be used for saving time and reducing cost for segmentation of interested regions and increasing the precision of molecular image quantization.