Aortic dissection operation risk prediction method based on lifting tree model

A technology for aortic dissection and surgical risk, applied in character and pattern recognition, medical data mining, instruments, etc., can solve problems such as incompleteness, missing, and imperfect data, and achieve high accuracy
CN112949685AActive Publication Date: 2021-06-11THE SECOND XIANGYA HOSPITAL OF CENT SOUTH UNIV
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Patent Information

Authority / Receiving Office
CN Β· China
Patent Type
Applications(China)
Current Assignee / Owner
THE SECOND XIANGYA HOSPITAL OF CENT SOUTH UNIV
Publication Date
2021-06-11

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Abstract

The invention discloses an aortic dissection surgery risk prediction method based on a lifting tree model. The method comprises the steps of 1, carrying out data preprocessing; complementing data loss parts in the operation records to form a database containing the operation records; step 2, carrying out data mining; processing the data in the database based on the decision tree model; 3, analyzing a result; analyzing a data mining processing result to evaluate the validity of a data mining algorithm; and 4, carrying out knowledge application; and applying the model to data corresponding to the current aortic dissection surgical operation to predict the risk of the current operation. The model has 100% accuracy rate for predicting the death risk after the aortic dissection surgery, compared with other prediction methods, the accuracy is higher, the prediction result can give key factors of death after the aortic dissection surgery, decision basis can be provided for doctors and patients in the future, and a medical policy maker can fully utilize and arrange 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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