Abstract: A control method for a reinforcement learning system includes following operations. The reinforcement learning system obtains training data relating to an interaction system. The interaction system interacts with a reinforcement learning agent. A neural network model is utilized by the reinforcement learning agent for selecting sequential actions from a set of candidate actions. The neural network model is trained to maximize cumulative rewards collected by the reinforcement learning agent in response to the sequential actions. During training of the neural network model, auxiliary rewards of the cumulative rewards are provided to the reinforcement learning agent according to a comparison between symptom inquiry actions of the sequential actions and diagnosed symptoms in the training data.

Abstract: A computing method, suitable for computing a transformer model, include following steps. An input matrix corresponding to an input sequence of feature vectors is projected into a query matrix according to first learnable weights. The input matrix is projected into a value matrix according to second learnable weights. A factorized matrix is generated by an incomplete Cholesky factorization according to the query matrix and a transpose of the query matrix. An intermediate matrix is calculated according to a product between a transpose of the factorized matrix and the value matrix. An output matrix is calculated according to a product between the factorized matrix (H) and the intermediate matrix.

Abstract: A computer aided medical method include following steps. An initial symptom is collected through an interaction interface. A representative prediction model is selected from plural candidate prediction models according to the initial symptom. The candidate prediction models are trained by a machine learning algorithm according to clinical data. A series of sequential actions is generated according to the representative prediction model and the initial symptom. The sequential actions are selected from plural candidate actions in the representative prediction model. The candidate actions include plural inquiry actions and plural disease prediction actions. Each of the sequential actions is one of the inquiry actions or the disease prediction actions. The series of sequential actions is displayed on the interaction interface.

Abstract: A medical system is able to provide a symptom query interpretation and/or a disease diagnosis interpretation. The medical system includes an interface and a processor. The interface is configured for receiving an input state. The processor is coupled with the interface. The processor is configured to execute a symptom checker to select a current action, from a plurality of candidate symptom queries and a plurality of candidate disease predictions, according to the input state. In response to the current action is a first symptom query, the processor is configured to execute an interpretable module interacted with the symptom checker to generate a diagnostic tree for simulating possible diagnosis paths, and generate a symptom query interpretation about the first symptom query according to the diagnostic tree.

Abstract: A computer aided medical method includes the following steps. An initial symptom of a patient and context information is collected through an interaction interface. Actions in a series are sequentially generated according to the candidate prediction models and the initial symptom. Each of the actions corresponds to one of the inquiry actions or one of the disease prediction actions. If the latest one of the sequential actions corresponds to one of the disease prediction actions, potential disease predictions are generated in a first ranking evaluated by the candidate prediction models. The first ranking is adjusted into a second ranking according to the context information. A result prediction corresponding to the potential disease predictions is generated in the second ranking.

Abstract: A machine learning method includes steps of: obtaining, by a processor, a model parameter from a memory, and performing, by a processor, a classification model according to the model parameter, wherein the classification model comprises a plurality of neural network structural layers; calculating, by the processor, a first loss and a second loss according to a plurality of training samples, wherein the first loss corresponds to an output layer of the plurality of neural network structural layers, and the second loss corresponds to one, which is before the output layer, of the plurality of neural network structural layers; and performing, by the processor, a plurality of updating operations for the model parameter according to the first loss and the second loss to train the classification model.

Abstract: A training method suitable for a reinforcement learning system with a reward function to train a reinforcement learning model and including: defining at least one reward condition of the reward function; determining at least one reward value range corresponding to the at least one reward condition; searching for at least one reward value from the at least one reward value range by a hyperparameter tuning algorithm; and training the reinforcement learning model according to the at least one reward value.

Abstract: A control method includes following operations. A symptom input status and a test result status are collected. A neural network is utilized to generate a test suggestion, a predicted test result distribution and a predicted disease distribution according to the symptom input status and the test result status. The test suggestion includes a candidate test. Information gains of the candidate test relative to diseases are estimated according to the predicted test result distribution and the predicted disease distribution. An explainable description about the test suggestion is generated according to the information gains of the candidate test. Another explainable description about a predicted disease list can be generated according to an attention input.

Abstract: A computer aided medical method include following steps. An initial symptom is collected through an interaction interface. A representative prediction model is selected from plural candidate prediction models according to the initial symptom. The candidate prediction models are trained by a machine learning algorithm according to clinical data. A series of sequential actions is generated according to the representative prediction model and the initial symptom. The sequential actions are selected from plural candidate actions in the representative prediction model. The candidate actions include plural inquiry actions and plural disease prediction actions. Each of the sequential actions is one of the inquiry actions or the disease prediction actions. The series of sequential actions is displayed on the interaction interface.

Abstract: A computer aided medical method include following steps. An initial symptom is collected through an interaction interface. A representative prediction model is selected from plural candidate prediction models according to the initial symptom. The candidate prediction models are trained by a machine learning algorithm according to clinical data. A series of sequential actions is generated according to the representative prediction model and the initial symptom. The sequential actions are selected from plural candidate actions in the representative prediction model. The candidate actions include plural inquiry actions and plural disease prediction actions. Each of the sequential actions is one of the inquiry actions or the disease prediction actions. The series of sequential actions is displayed on the interaction interface.

Abstract: A computer aided medical method includes the following steps. An initial symptom of a patient and context information is collected through an interaction interface. Actions in a series are sequentially generated according to the candidate prediction models and the initial symptom. Each of the actions corresponds to one of the inquiry actions or one of the disease prediction actions. If the latest one of the sequential actions corresponds to one of the disease prediction actions, potential disease predictions are generated in a first ranking evaluated by the candidate prediction models. The first ranking is adjusted into a second ranking according to the context information. A result prediction corresponding to the potential disease predictions is generated in the second ranking.

Abstract: A computer aided medical method includes the following steps. An initial symptom of a patient and context information is collected through an interaction interface. Actions in a series are sequentially generated according to the candidate prediction models and the initial symptom. Each of the actions corresponds to one of the inquiry actions or one of the disease prediction actions. If the latest one of the sequential actions corresponds to one of the disease prediction actions, potential disease predictions are generated in a first ranking evaluated by the candidate prediction models. The first ranking is adjusted into a second ranking according to the context information. A result prediction corresponding to the potential disease predictions is generated in the second ranking.

Abstract: A method for controlling a medical system includes the following operations. The medical system receives an initial symptom. A neural network model is utilized to select at least one symptom inquiry action. The medical system receives at least one symptom answer to the at least one symptom inquiry action. A neural network model is utilized to select at least one medical test action from candidate test actions according to the initial symptom and the at least one symptom answer. The medical system receives at least one test result of the at least one medical test action. A neural network model is utilized to select a result prediction action from candidate prediction actions according to the initial symptom, the at least one symptom answer and the at least one test result.

Abstract: A control method for a reinforcement learning system includes following operations. The reinforcement learning system obtains training data relating to an interaction system. The interaction system interacts with a reinforcement learning agent. A neural network model is utilized by the reinforcement learning agent for selecting sequential actions from a set of candidate actions. The neural network model is trained to maximize cumulative rewards collected by the reinforcement learning agent in response to the sequential actions. During training of the neural network model, auxiliary rewards of the cumulative rewards are provided to the reinforcement learning agent according to a comparison between symptom inquiry actions of the sequential actions and diagnosed symptoms in the training data.

Abstract: A computer aided medical method includes the following steps. An initial symptom of a patient and context information is collected through an interaction interface. Actions in a series are sequentially generated according to the candidate prediction models and the initial symptom. Each of the actions corresponds to one of the inquiry actions or one of the disease prediction actions. If the latest one of the sequential actions corresponds to one of the disease prediction actions, potential disease predictions are generated in a first ranking evaluated by the candidate prediction models. The first ranking is adjusted into a second ranking according to the context information. A result prediction corresponding to the potential disease predictions is generated in the second ranking.

Abstract: A computer aided medical method include following steps. An initial symptom is collected through an interaction interface. A representative prediction model is selected from plural candidate prediction models according to the initial symptom. The candidate prediction models are trained by a machine learning algorithm according to clinical data. A series of sequential actions is generated according to the representative prediction model and the initial symptom. The sequential actions are selected from plural candidate actions in the representative prediction model. The candidate actions include plural inquiry actions and plural disease prediction actions. Each of the sequential actions is one of the inquiry actions or the disease prediction actions. The series of sequential actions is displayed on the interaction interface.

Abstract: A medical system includes an interaction interface and an analysis engine. The interaction interface is configured for receiving an initial symptom. The analysis engine is communicated with the interaction interface. The analysis engine includes a prediction module. The prediction module is configured for generating symptom inquiries to be displayed on the interaction interface according to a prediction model and the initial symptom. The interaction interface is configured for receiving responses corresponding to the symptom inquiries. The prediction module is configured to generate a result prediction according to the prediction model, the initial symptom and the responses.