Method for medical training and examination by using simulated machine patient

Abstract

A method for medical training and examination by using a simulated machine patient is characterized by including the following steps: first, various standardized patient data is input to a simulated machine patient through computer programming, and the standardized patient data of the simulated machine patient is divided into three major modules: a primary module, an intermediate module and an advanced module; second, each level of information in the primary, intermediate and advanced modules is subdivided into various clinical departments such as surgery, internal medicine, general practice,digestive medicine, neurology, orthopedics, pediatrics, gynecology and nursing; and third, a feedback device which makes different responses when the simulated machine patient is differently operatedby an operator and makes the same response when the simulated machine patient is operated by an operator in the same way and an inquiry device which displays the corresponding detection results of thesimulated machine patient are set in the three major modules. The method has the advantage that the method is ingeniously designed, has a high degree of simulation and good interaction and makes medical education, training and examination closer to actual clinical practice.

Description

technical field [0001] The invention relates to the field of intelligent medical technology, in particular to a method for medical training and examination by using simulated robot patients. Background technique [0002] There is an existing Chinese invention patent application with the application number CN201710510408.7 titled "A Method for Intelligent Medical Automatic Question Answering Based on Deep Learning", which discloses a method for intelligent medical automatic question answering based on deep learning, including: 1) building an answer pool , and then vectorize the data in the answer pool; 2) obtain medical question-and-answer data from the Internet, and then vectorize the medical question-answer data to obtain a training text vector, then input its vector into the neural network, and construct Model training cost function, and then train the neural network to obtain the trained neural network; 3) Extract the word vector of each word in the question to be answere...

AI Technical Summary

Problems solved by technology

However, this method has poor interactivity and single function, so this method needs further improvement

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