Ignite Creation Date:
2024-05-06 @ 8:28 PM
Last Modification Date:
2024-10-26 @ 3:28 PM
Study NCT ID:
NCT06399081
Status:
COMPLETED
Last Update Posted:
2024-05-03
First Post:
2024-04-29
Brief Title:
Construction of a Predictive Model of Gangrenous Cholecystitis Based on Machine Learning
Sponsor:
Dalian Medical University
Organization:
Dalian Medical University
Study Overview
Official Title:
A Real-world Study of Predictive Models of Gangrenous Cholecystitis Based on Machine Learning
Status:
COMPLETED
Status Verified Date:
2024-05
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
Gangrenous cholecystitis is the most common complication of acute cholecystitis
There is no research using machine learning models to construct predictive diagnostic models for gangrenous cholecystitis
There is no research using machine learning models to construct predictive diagnostic models for gangrenous cholecystitis
Detailed Description:
This study reviewed the clinical data of 2023 cholecystectomy patients admitted to our center between January 1 2015 and May 31 2015 it includes demographic clinical features laboratory and imaging indexes and constructs five commonly used Decision Tree SVM Random Forest XGBoost AdaBoost models feature subsets are selected by Recursive Feature Elimination with Cross-Validation and the importance of variables in each model model performance is evaluated by Balanced accuracy Recall Precision F1score and the Precision-RecallPR curve and the final results are verified by independent external validation sets
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None