Medical system and method for providing medical prediction

Patent metadata

Field	Value
Publication number	US-2018046773-A1
Application number	US-201715674538-A
Country	US
Kind code	A1
Filing date	Aug 11, 2017
Priority date	Aug 11, 2016
Publication date	Feb 15, 2018
Grant date	—

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.

First claim

What is claimed is:

1 . A medical system, comprising:
an interaction interface, configured for receiving an initial symptom; and an analysis engine, communicated with the interaction interface, the analysis engine comprises:
a prediction module, configured for generating a plurality of symptom inquiries to be displayed on the interaction interface according to a prediction model constructed by training data and the initial symptom, wherein the interaction interface is configured for receiving a plurality of responses corresponding to the symptom inquiries, and the prediction module is configured to generate a result prediction according to the prediction model, the initial symptom and the responses.

2 . The medical system of claim 1 , wherein the prediction module is configured to generate a first symptom inquiry according to the prediction model and the initial symptom, the first symptom inquiry is displayed on the interaction interface, and the interaction interface is configured to receive a first responses corresponding to the first symptom inquiry.

3 . The medical system of claim 2 , wherein the prediction module is further configured to generate a second symptom inquiry according to the prediction model, the initial symptom and the first response, the second symptom inquiry is displayed on the interaction interface, the interaction interface is configured to receive a second response corresponding to the second symptom inquiry, the prediction module is configured to generate the result prediction according to the prediction model, the initial symptom, the first response and the second response.

4 . The medical system of claim 1 , further comprising:
a learning module, configured for generating a prediction model according to the training data, wherein the training data comprises a known medical record, the learning module utilizes the known medical record to train the prediction model.

5 . The medical system of claim 4 , wherein the training data further comprises a user feedback input collected by the interaction interface, a doctor diagnosis record received from an external server or a prediction logfile generated by the prediction module, the learning module further updates the prediction model according to the user feedback input, the doctor diagnosis record or the prediction logfile.

6 . The medical system of claim 1 , wherein the result prediction comprises at least one of a disease prediction and a medical department suggestion matching the disease prediction, the disease prediction comprises a disease name or a list of disease names ranked by probability.

7 . The medical system of claim 6 , wherein the interaction interface is configured to display the result prediction, after the result prediction is displayed on the interaction interface, the interaction interface is configured to receive a user command in response to the result prediction, the medical system is configured to send a medical registration request corresponding to the user command to an external server.

8 . The medical system of claim 1 , wherein the prediction model comprises a first prediction model generated according to a Bayesian inference algorithm, the first prediction model comprises a probability relationship table, the probability relationship table records relative probabilities between different diseases and different symptoms.

9 . The medical system of claim 1 , wherein the prediction model comprises a second prediction model generated according to a decision tree algorithm, the second prediction model comprises a plurality of decision trees constructed in advance according to the training data.

10 . The medical system of claim 1 , wherein the prediction model comprises a third prediction model generated according to a reinforcement learning algorithm, the third prediction model is trained according to the training data to maximize a reward signal, the reward signal is increased or decreased according to a correctness of a training prediction made by the third prediction model, the correctness of the training prediction is verified according to a known medical record in the training data.

11 . A method, comprising:
receiving an initial symptom; generating a plurality of symptom inquiries according to a prediction model and the initial symptom; receiving a plurality of responses corresponding to the symptom inquiries; and generating a result prediction according to the prediction model, the initial symptom and the responses.

12 . The method of claim 11 , wherein the steps of generating the symptom inquiries and receiving the responses comprise:
generating a first symptom inquiry according to the prediction model and the initial symptom receiving a first response corresponding to the first symptom inquiry; generating a second symptom inquiry according to the prediction model, the initial symptom and the first response; and receiving a second response corresponding to the second symptom inquiry.

13 . The method of claim 12 , wherein the step of generating the result prediction comprises:
generating the result prediction at least according to the prediction model, the initial symptom, the first response and the second response.

14 . The method of claim 11 , further comprising:
generating the prediction model according to training data, wherein the training data comprises a known medical record, the prediction model is trained with the known medical record.

15 . The method of claim 14 , wherein the training data further comprises a user feedback input, a doctor diagnosis record or a prediction logfile, the prediction model is further updated according to the user feedback input, the doctor diagnosis record or the prediction logfile.

16 . The method of claim 11 , wherein the result prediction comprises at least one of a disease prediction and a medical department suggestion matching the disease prediction, the disease prediction comprises a disease name or a list of disease names ranked by probability, the method further comprising:
displaying the result prediction.

17 . The method of claim 16 , wherein after the result prediction is displayed on the interaction interface, the method further comprising:
receiving a user command in response to the result prediction; and sending a medical registration request corresponding to the user command to an external server.

18 . The method of claim 11 , wherein the prediction model comprises a first prediction model generated according to a Bayesian inference algorithm, the first prediction model comprises a probability relationship table, the probability relationship table records relative probabilities between different diseases and different symptoms.

19 . The method of claim 11 , wherein the prediction model comprises a second prediction model generated according to a decision tree algorithm, the second prediction model comprises a plurality of decision trees constructed in advance according to the training data.

20 . The method of claim 11 , wherein the prediction model comprises a third prediction model generated according to a reinforcement learning algorithm, the third prediction model is trained according to the training data to maximize a reward signal, the reward signal is increased or decreased according to a correctness of a training prediction made by the third prediction model, the correctness of the training prediction is verified according to a known medical record in the training data.

21 . A non-t

Assignees

• Htc Corp

Inventors

• Tang Kai Fu
• Kao Hao Cheng
• Chou Chun Nan
• Chang Edward
• Cheng Chih Wei
• Chang Ting Jung
• Yu Shan Yi
• Liu Tsung Hsiang
• Sung Cheng Lung
• Yeh Chieh Hsin

Classifications

• G06N7/01

  Probabilistic graphical models, e.g. probabilistic networks · CPC title

• G06N5/01

  Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound · CPC title

• G06N3/044

  Recurrent networks, e.g. Hopfield networks · CPC title

• G06N3/092

  Reinforcement learning · CPC title

• G06N3/0442

  characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU] · CPC title