Grouping model construction method based on causal inference and medical data processing method

Abstract

The invention relates to the technical field of artificial intelligence and digital medical treatment, and provides a causal inference-based grouping model construction method, which comprises the following steps of: inputting multiple pieces of sample data of multiple sample patients into a to-be-trained model, outputting a tendency score of each sample patient for the corresponding sample patient grouping result data and a plurality of sample expected accumulated reward values corresponding to each sample patient through the to-be-trained model; determining a target sample expected cumulative reward value of each sample patient from the plurality of sample expected cumulative reward values; and on the basis of a preset loss function, the tendency score of each sample patient and the corresponding expected cumulative reward value of the target sample, adjusting model parameters in the to-be-trained model to obtain a grouping model. According to the method, the to-be-trained model is combined with causal inference analysis to train the multiple sample data, so that the selection deviation of the patient grouping result data is eliminated, the model fitting is more reasonable, and the trained model application accuracy is higher.

Description

technical field [0001] The embodiment of the present invention relates to the technical field of artificial intelligence, and in particular to a method for constructing a grouping model based on causal inference and a method for processing medical data. Background technique [0002] In the medical field, patient grouping is of great significance to disease diagnosis, disease prediction, and drug treatment. Currently, deep reinforcement learning models are commonly used to segment patient populations. Most deep reinforcement learning models use multi-layer neural networks to capture the correlation dependence between features to estimate "revenue". [0003] However, in the actual medical field application scenarios, there is a high correlation between the actual grouping decision of patients and certain characteristics. Doctors will make targeted grouping decisions for patients based on diagnostic guidelines or clinical experience. This distributional bias in taking decisi...

AI Technical Summary

Problems solved by technology

[0004] In view of this, the embodiment of the present invention provides a method for constructing a grouping model based on causal inference, a system, a computer device, a computer-readable storage medium, and a medical data processing method, which are used to solve the problems of learning and processing of existing deep reinforcement learning models. During the training process, due to the distribution deviation of decision-making in the sample data, the grouping model trained based on the deep reinforcement learning model has low accuracy and unreasonable problems in making patient grouping decisions

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