A method for predicting preoperative pulmonary complications in cardiac surgery

By employing hierarchical progressive data preprocessing and multi-model comparison, combined with feature importance analysis, a predictive model for preoperative pulmonary complications in cardiac surgery was constructed. This model addresses the shortcomings of existing models in identifying high-risk populations and key factors, enabling accurate prediction and efficient pre-rehabilitation treatment, and reducing the incidence of postoperative pulmonary complications.

CN121075661BActive Publication Date: 2026-02-17WEST CHINA HOSPITAL SICHUAN UNIV
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Patent Information

Application Number
CN202511613033.8
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-11-06
Publication Date
2026-02-17
Estimated Expiration
2045-11-06

AI Technical Summary

Technical Problem

Existing models for predicting pulmonary complications after cardiac surgery are complex and limited, failing to accurately identify high-risk individuals and key risk factors. This results in a lack of targeted pre-rehabilitation treatment, wasting medical resources and limiting the effectiveness of pre-rehabilitation.

Method used

A hierarchical and progressive data preprocessing workflow is adopted, including outlier handling, missing value handling, feature correlation analysis, and data discretization. It combines various machine learning algorithm models (such as logistic regression, random forest, CatBoost, etc.) and constructs a predictive model based on preoperative and intraoperative indicators through feature importance analysis and cross-validation, providing accurate pre-rehabilitation treatment plans.

Benefits of technology

It improves the accuracy and stability of the prediction model, enabling more precise identification of high-risk individuals for PPCs, reducing the incidence of postoperative pulmonary complications, minimizing waste of medical resources, and enhancing the effectiveness of pre-rehabilitation treatment.

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Abstract

The application belongs to the field of heart surgery, and particularly relates to a preoperative lung complication prediction method for heart surgery, which comprises the following steps: data preprocessing, including abnormal value processing, missing value processing, feature correlation analysis, data discretization and data standardization; abnormal value processing, filtering to remove abnormal values and irrelevant data in the case data set, so as to avoid interference of abnormal values on subsequent model learning; missing value processing, in which the application guarantees data quality and feature effectiveness from the source through a hierarchical progressive data preprocessing process: filtering of abnormal values and irrelevant data avoids noise interference on model learning, and missing values are processed differently according to missing rate (features are removed when the missing rate is greater than 30%, and features are filled with median, regression prediction or mode according to variable types when the missing rate is less than 30%), which greatly reduces data bias compared with the existing model simple filling method, and improves data integrity and reliability.
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