AIMC Topic: Logistic Models

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[Prediction of depression symptoms in seniors and analysis of influencing factors based on explainable machine learning].

Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi Feb 10, 2025
This study aims to construct a machine learning model to predict depression symptoms in the elderly and analyze the key influencing factors of depression in the elderly using the shapley additive interpretation (SHAP) method. Based on entries from ...
China Depression ROC Curve Logistic Models Support Vector Machine Longitudinal Studies Machine Learning Algorithms Female Aged Male Humans
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Constructing a Predictive Model for Psychological Distress of Young- and Middle-Aged Gynaecological Cancer Patients.

Journal of evaluation in clinical practice Feb 1, 2025
BACKGROUND: Cancer patients experience substantial psychological distress which causes the reduction of the quality of life. However, the risk of psychological distress has not been well predicted especially in young- and middle-aged gynaecological c...
Middle Aged Quality of Life Self Efficacy Stress, Psychological ROC Curve Risk Factors Young Adult Adult Cross-Sectional Studies Logistic Models Age Factors Genital Neoplasms, Female Psychological Distress Humans Neural Networks, Computer Female
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Machine Learning Model Predictors of Intrapleural Tissue Plasminogen Activator and DNase Failure in Pleural Infection: A Multicenter Study.

Annals of the American Thoracic Society Feb 1, 2025
Intrapleural enzyme therapy (IET) with tissue plasminogen activator (tPA) and DNase has been shown to reduce the need for surgical intervention for complicated parapneumonic effusion/empyema (CPPE/empyema). Failure of IET may lead to delayed care an...
Humans Machine Learning Retrospective Studies Female Risk Factors Treatment Failure Empyema, Pleural United States Aged Fibrinolytic Agents Male Pleural Effusion Middle Aged Tissue Plasminogen Activator Logistic Models Deoxyribonucleases
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Machine learning prediction of hepatic encephalopathy for long-term survival after transjugular intrahepatic portosystemic shunt in acute variceal bleeding.

World journal of gastroenterology Jan 28, 2025
BACKGROUND: Transjugular intrahepatic portosystemic shunt (TIPS) is an effective intervention for managing complications of portal hypertension, particularly acute variceal bleeding (AVB). While effective in reducing portal pressure and preventing re...
Male Female Middle Aged Treatment Outcome Humans Risk Assessment Risk Factors Hepatic Encephalopathy Esophageal and Gastric Varices Machine Learning Postoperative Complications Portasystemic Shunt, Transjugular Intrahepatic Gastrointestinal Hemorrhage Aged Retrospective Studies Hypertension, Portal Logistic Models Kaplan-Meier Estimate Adult
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A novel machine learning-based cancer-specific cardiovascular disease risk score among patients with breast, colorectal, or lung cancer.

JNCI cancer spectrum Jan 3, 2025
BACKGROUND: Cancer patients have up to a 3-fold higher risk for cardiovascular disease (CVD) than the general population. Traditional CVD risk scores may be less accurate for them. We aimed to develop cancer-specific CVD risk scores and compare them ...
Aged Humans Cardiovascular Diseases Logistic Models Adult Machine Learning Middle Aged Area Under Curve Colorectal Neoplasms Algorithms Risk Assessment Breast Neoplasms Female Lung Neoplasms Male
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Modeling of injury severity of distracted driving accident using statistical and machine learning models.

PloS one Jan 1, 2025
Distracted Driving (DD) is one of the global causes of high mortality and fatality in road traffic accidents. The increase in the number of distracted driving accidents (DDAs) is one of the concerns among transportation communities. The present study...
Humans Machine Learning Female Young Adult Models, Statistical Accidents, Traffic Automobile Driving Adult Wounds and Injuries Male Distracted Driving Adolescent India Logistic Models
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Preoperative kidney tumor risk estimation with AI: From logistic regression to transformer.

PloS one Jan 1, 2025
We consider the problem of renal mass risk classification to support doctors in adjuvant treatment decisions following nephrectomy. Recommendation of adjuvant therapy based on the mass appearance poses two major challenges: first, morphologic pattern...
Nephrectomy Humans Artificial Intelligence Risk Assessment Prognosis Logistic Models Female Aged Kidney Neoplasms Male Preoperative Period Middle Aged
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Artificial Intelligence Models Could Enhance the Diagnostic Accuracy (DA) of Fecal Immunochemical Test (FIT) in the Detection of Colorectal Adenoma in a Screening Setting.

Anticancer research Jan 1, 2025
BACKGROUND/AIM: This study evaluated the diagnostic accuracy (DA) for colorectal adenomas (CRA), screened by fecal immunochemical test (FIT), using five artificial intelligence (AI) models: logistic regression (LR), support vector machine (SVM), neur...
Early Detection of Cancer Adenoma Occult Blood Immunochemistry Middle Aged Feces ROC Curve Logistic Models Humans Support Vector Machine Artificial Intelligence Female Aged Colorectal Neoplasms Male
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Investigating the Differential Impact of Psychosocial Factors by Patient Characteristics and Demographics on Veteran Suicide Risk Through Machine Learning Extraction of Cross-Modal Interactions.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Jan 1, 2025
Accurate prediction of suicide risk is crucial for identifying patients with elevated risk burden, helping ensure these patients receive targeted care. The US Department of Veteran Affairs' suicide prediction model primarily leverages structured elec...
Humans Machine Learning Electronic Health Records Female Risk Factors Veterans Computational Biology Logistic Models Male United States Middle Aged Natural Language Processing Risk Assessment United States Department of Veterans Affairs Suicide
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Comparison of six natural language processing approaches to assessing firearm access in Veterans Health Administration electronic health records.

Journal of the American Medical Informatics Association : JAMIA Jan 1, 2025
OBJECTIVE: Access to firearms is associated with increased suicide risk. Our aim was to develop a natural language processing approach to characterizing firearm access in clinical records.
Natural Language Processing United States Department of Veterans Affairs Firearms Humans Machine Learning Electronic Health Records Male Female Middle Aged Suicide Logistic Models United States
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