A risk prediction method for aortic dissection surgery based on boosted tree model

A technology for aortic dissection and surgical risk, applied in the fields of medical data mining, character and pattern recognition, instruments, etc., can solve problems such as incompleteness, lack, and incomplete data, and achieve high accuracy.
CN112949685BActive Publication Date: 2022-04-22THE SECOND XIANGYA HOSPITAL OF CENT SOUTH UNIV
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Patent Information

Authority / Receiving Office
CN Β· China
Patent Type
Patents(China)
Current Assignee / Owner
THE SECOND XIANGYA HOSPITAL OF CENT SOUTH UNIV
Publication Date
2022-04-22

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Abstract

The invention discloses a method for predicting the risk of aortic dissection surgery based on a lifting tree model, step 1: data preprocessing; completing the missing part of the data in the operation record to form a database containing the operation record; step 2: data Mining; process the data in the database based on the decision tree model; step 3: result analysis; analyze the processing results of data mining to evaluate the effectiveness of the data mining algorithm; step 4: knowledge application; apply the model to the current main Data corresponding to arterial dissection surgery to predict the risk of current surgery. The model of the present invention has 100% accuracy to the prediction of death risk after aortic dissection surgery, and is more accurate than other prediction methods, and the prediction result can provide the key factors of death after aortic dissection surgery, which can be In the future, doctors and patients will provide decision-making basis and help medical policy makers make full use of medical resources.
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Description

technical field

[0001] The invention relates to a method for predicting the risk of an aortic dissection operation based on a lifting tree model. Background technique

[0002] With the development and progress of technology, various medical data are collected to form a database, and the records in the database have different formats and types. Therefore, it is more difficult to apply these data. To analyze this data, different algorithms are required.

[0003] Most of the data comes from the patient's surgical plan, including conversations, test data, surgical results, medication information, etc., and has the following characteristics:

[0004] (1) Diversity; including various data forms, such as data and images;

[0005] (2) Incomplete type; many data are incomplete or missing;

[0006] (3) Timing; almost all data are generated according to the timeline;

[0007] (4) High-dimensionality; such as routine blood test and urine routine test often produce many sub-items; ...

Claims

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