HoPreM Platform: Efficient Multimodal Multi-Task Prediction of Perioperative Events Following Hip Replacement Surgery
HoPreM Platform: Efficient Multimodal Multi-Task Prediction of Perioperative Events Following Hip Replacement Surgery
Purpose:
The aim of this study is to develop the Holistic Predictive Multi-Tasking Platform for Clinical Data Analysis (HoPreM) to accurately predict perioperative events following hip replacement surgery by integrating various types of data, including demographic, surgical, medical history, and laboratory information. The events targeted for prediction include acute kidney injury (AKI), blood transfusion requirements, 48-hour postoperative discharge (48hPOD), Intensive Care Unit (ICU) transfer, and length of hospital stay (LOS).
Key Questions:
Can the HoPreM platform reduce the risk of complications after hip replacement surgery? How accurate is the platform in predicting the specified perioperative events?
Participants:
Participants will include patients undergoing hip replacement surgery, aged 18 and above, with less than 10% missing values in their medical records. The collected data will be used to train and test the predictive models of the HoPreM platform.
Study Procedures:
Patient data will be collected from Xi'an Honghui Hospital, including creatinine values recorded before and after surgery.
The HoPreM platform will process multimodal data, including demographic, surgical, medical history, and laboratory test data.
Various ensemble learning algorithms (including XGBoost, random forest, LightGBM, and CatBoost) will be applied to predict different perioperative outcomes.
Expected Outcomes:
The HoPreM platform is expected to demonstrate its capability in predicting complications after hip replacement surgery, particularly acute kidney injury and blood transfusion requirements. Through SHAP value analysis, the study aims to reveal relationships between features and clinical outcomes, enhancing the model's interpretability and clinical utility.
Contact Information:
For any questions about this study or for more information, please contact the research team.
This study aims to develop the Holistic Predictive Multi-Tasking Platform for Clinical Data Analysis (HoPreM) to accurately predict perioperative events following hip replacement surgery. The HoPreM platform integrates various types of patient data, including demographic, surgical, medical history, and laboratory information. Utilizing a multi-task learning framework, the platform is designed to predict multiple perioperative complications, such as acute kidney injury (AKI), blood transfusion requirements, 48-hour postoperative discharge (48hPOD), Intensive Care Unit (ICU) transfer, and length of hospital stay (LOS). To enhance predictive accuracy, feature selection techniques like Lasso regression and random forest models are employed, followed by ensemble learning algorithms, including CatBoost. This predictive platform is expected to support personalized postoperative management, reduce complication rates, and improve clinical outcomes for hip replacement patients.
Inclusion Criteria:
Exclusion Criteria: