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Medical data processing method and device, and prediction model training method and device

A medical data and processing method technology, applied in the field of medical data processing, can solve the problems of difficulty in sample collection and poor training accuracy of artificial intelligence models.

Active Publication Date: 2020-10-23
颐保医疗科技(上海)有限公司
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AI Technical Summary

Problems solved by technology

Therefore, a large amount of sample data is required in the training process of the artificial intelligence model. For small sample data, especially small sample medical data, it is difficult to collect samples, which further leads to poor training accuracy of the artificial intelligence model.

Method used

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  • Medical data processing method and device, and prediction model training method and device
  • Medical data processing method and device, and prediction model training method and device
  • Medical data processing method and device, and prediction model training method and device

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Embodiment 1

[0034] figure 1 It is a schematic flowchart of a medical data processing method provided in Embodiment 1 of the present invention. This embodiment is applicable to the situation of processing medical data. The method can be executed by the medical data processing device provided in the embodiment of the present invention. The device Can be integrated into electronic equipment such as computers or servers. The method specifically includes the following steps:

[0035] S110. Acquire first medical data parameters of the first subject and second medical data parameters of the second subject.

[0036] S120. Perform parameter expansion on the first medical data parameter and the second medical data parameter based on the first expansion rule, and expand the first medical data parameter and the second medical data parameter on the basis of the second expansion rule The associated parameters are expanded to obtain the expanded medical data parameters.

[0037] S130. Determine the d...

Embodiment 2

[0082] figure 2 It is a schematic flowchart of a medical data processing method provided in Embodiment 2 of the invention, which is optimized on the basis of the above embodiments, and the method includes:

[0083] S210. Acquire first medical data parameters of the first subject and second medical data parameters of the second subject.

[0084] S220. Perform parameter expansion on the first medical data parameter and the second medical data parameter based on the first expansion rule, and expand the first medical data parameter and the second medical data parameter on the basis of the second expansion rule The associated parameters are expanded to obtain the expanded medical data parameters.

[0085] S230. Determine the distribution status of each expanded medical data parameter based on the prior distribution of each expanded medical data parameter, and iteratively sample the expanded medical data parameters based on the distribution status.

[0086] S240. Randomly sample ...

Embodiment 3

[0107] image 3 It is a schematic flow chart of the method for training a prediction model provided in Embodiment 3 of the present invention. The method is used to train a prediction model with a target prediction function. The method includes:

[0108] S310. Acquire sample data formed by valid medical data parameters corresponding to the target prediction function, wherein the valid medical data parameters are determined according to the medical data processing method provided in the above-mentioned embodiment.

[0109] S320. Train the prediction model to be trained based on the sample data to obtain a prediction model with a target prediction function.

[0110] In this embodiment, sample data is obtained by performing sample sampling based on the effective medical data parameters obtained in the above embodiments. For example, it may be obtained by extracting from parameter sets of multiple sample objects according to effective medical data parameters. The sample data form...

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Abstract

The invention discloses a medical data processing method and device, and a prediction model training method and device. The medical data processing method comprises the steps of acquiring a first medical data parameter of a first object and a second medical data parameter of a second object; performing parameter extension on the first medical data parameter and the second medical data parameter based on a first extension rule, and performing associated parameter extension on the first medical data parameter and the second medical data parameter based on a second extension rule to obtain extended medical data parameters; determining the distribution state of each expanded medical data parameter based on the prior distribution of each expanded medical data parameter, and performing iterativesampling on each expanded medical data parameter based on the distribution state; and screening the expanded medical data parameters according to a sampling result to determine effective medical dataparameters. According to the invention, invalid medical data parameters are eliminated, so the number of required samples in the process of prediction model training is reduced, and small sample training of a prediction model is realized.

Description

technical field [0001] The embodiments of the present invention relate to the technical field of medical data processing, in particular to a medical data processing method, a prediction model training method and a device. Background technique [0002] With the rapid development of information science, big data processing methods based on artificial intelligence are widely used, especially intelligent model processing methods such as deep neural network models. [0003] The current data processing method is generally to input the collected data into the artificial intelligence model, and the artificial intelligence model will identify, screen and process the input data. Therefore, a large amount of sample data is required in the training process of the artificial intelligence model. For small sample data, especially small sample medical data, it is difficult to collect samples, which further leads to poor training accuracy of the artificial intelligence model. Contents of t...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G16H50/20G16H10/00G16H50/50
CPCG16H10/00G16H50/20G16H50/50
Inventor 贺云鹏
Owner 颐保医疗科技(上海)有限公司
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