Method for scoring curing probability of potential treatment scheme based on patient detection information

Abstract

The invention belongs to the field of deep learning analysis, and discloses a method for scoring the curing probability of a potential treatment scheme based on patient detection information. The method comprises the following steps: (S1) collecting training data; (S2) establishing a logistic regression model and training the logistic regression model; (S3) respectively establishing a unet model and a three-dimensional convolutional neural network model and training the unet model and the three-dimensional convolutional neural network model; (S4) complementing the training data to obtain a survival condition score, corresponding to the training data, of any patient in each day in the treatment process; (S5) establishing a long short-term memory (LSTM) network model and training; and (S6) randomly selecting the potential treatment scheme, and predicting to obtain a survival condition score of the (N + 2) day, thereby completing scoring of the potential treatment scheme. The overall process design and the like of the method and the system are improved, the condition of a patient can be specifically quantified from the angle of big data analysis, and the curing probability of a potential treatment scheme is scored.

Description

technical field [0001] The present invention belongs to the field of deep learning analysis, and more specifically, relates to a method for scoring the cure probability of a potential treatment plan based on patient detection information. The system corresponding to the method can also evaluate the cure probability of a potential treatment plan based on patient detection information score. Background technique [0002] For a class of diseases, the test values ​​of patients often contain a lot of information. At present, doctors' treatment of patients is based on experience and their own judgment of patients' conditions. Individual differences are large, and misdiagnosis may occur due to various reasons, and they may not consider well when giving a diagnosis plan. [0003] Lung diseases, in addition to being reflected in clinical examination information (such as blood tests, urine tests), can often be judged by CT images of the patient's lungs. Combining various data can of...

AI Technical Summary

Problems solved by technology

At the same time, there is currently no real-time update prediction method for the treatment plan. This invention hopes to use the patient's examination results and treatment plan to give a treatment plan with a higher probability of cure, and provide reasonable suggestions and references for doctors.

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