Treatment effect prediction system with self-learning capability and treatment effect prediction terminal
A technology of treatment effect and prediction system, applied in the field of diagnosis and treatment instruments, to achieve the effect of enhancing the accuracy of model prediction
Active Publication Date: 2020-07-31
PEKING UNIV THIRD HOSPITAL
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[0005]
In view of the above problems, the embodiment of the present application provides a therapeutic effect prediction system and a therapeutic effect prediction terminal with self-learning ability, aiming to solve the problem that the therapeutic effect prediction instrument can only use the original prediction model to predict the therapeutic effect
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[0049] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of them. Based on the embodiments in this application, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of this application.
[0050]Treatment effect prediction equipment refers to medical equipment that can analyze case data and predict treatment effect based on case data. The treatment effect prediction instrument is equipped with a prediction model, which analyzes and calculates the case data based on a machine learning algorithm to obtain the predicted treatment effect. Specifically, neural networks (such as LSTM neural networks, convolutional neural networks, etc.) can be used to learn the fe...
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The invention provides a treatment effect prediction system with a self-learning capability and a treatment effect prediction terminal, and relates to the technical field of diagnosis and treatment instruments. The problem that a treatment effect prediction instrument can only predict the treatment effect through an original prediction model is solved. A case data acquisition module is used for receiving input new case data; a treatment effect prediction module is used for obtaining a predicted treatment effect by using a prediction model according to the new case data, and sending the predicted treatment effect to a treatment effect display module; a case data classification module is used for obtaining a target treatment effect according to the target classification of the new case data,and taking the target treatment effect as a label of the new case data, wherein the database is used for adding new case data with labels to obtain a sample data set; and the treatment effect prediction module is also used for training and updating the prediction model by using the sample data set, and performing next treatment effect prediction by using the updated prediction model.
Description
technical field [0001] This application relates to the technical field of diagnostic and therapeutic instruments, in particular to a therapeutic effect prediction system and a therapeutic effect prediction terminal with self-learning capability. Background technique [0002] Recently, machine learning algorithms have been more and more widely used in fields such as education, transportation, and finance. Medical-related fields are also actively using machine learning technology to explore in auxiliary diagnosis and inspection. Taking the postoperative curative effect evaluation of sinusitis as an example, most studies use the logistic regression algorithm in machine learning to analyze the risk factors that affect the postoperative curative effect of sinusitis, which can help clinicians consider comprehensively and formulate more effective prevention and control measures. However, these algorithm models have the following disadvantages: [0003] 1. Once the algorithm model...
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IPC IPC(8): G16H50/20G16H50/70G06F16/906
CPCG16H50/20G16H50/70G06F16/906Y02A90/10
Inventor 朱丽王玉辉
Owner PEKING UNIV THIRD HOSPITAL