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Enhancing Clinical Decision Making by Predicting Readmission Risk in Patients With Heart Failure Using Machine Learning: Predictive Model Development Study.
JMIR medical informatics
Dec 31, 2024
BACKGROUND: Patients with heart failure frequently face the possibility of rehospitalization following an initial hospital stay, placing a significant burden on both patients and health care systems. Accurate predictive tools are crucial for guiding ...
Leveraging Machine Learning to Identify Subgroups of Misclassified Patients in the Emergency Department: Multicenter Proof-of-Concept Study.
Journal of medical Internet research
Dec 31, 2024
BACKGROUND: Hospitals use triage systems to prioritize the needs of patients within available resources. Misclassification of a patient can lead to either adverse outcomes in a patient who did not receive appropriate care in the case of undertriage o...
EC and EC of Remifentanil for Inhibiting Bronchoscopy Responses in Elderly Patients During Fiberoptic Bronchoscopy Under Ciprofol Sedation: An Up-and-Down Sequential Allocation Trial.
Drug design, development and therapy
Dec 31, 2024
BACKGROUND: Opioids are used to suppress cough during fiberoptic bronchoscopy (FOB). However, evidence regarding the optimal dose of remifentanil during FOB under ciprofol sedation is limited. This study aimed to investigate the effective concentrati...
Exploring determinant factors influencing muscle quality and sarcopenia in Bilbao's older adult population through machine learning: A comprehensive analysis approach.
PloS one
Dec 31, 2024
BACKGROUND: Sarcopenia and reduced muscle quality index have garnered special attention due to their prevalence among older individuals and the adverse effects they generate. Early detection of these geriatric pathologies holds significant potential,...
Mortality prediction of inpatients with NSTEMI in a premier hospital in China based on stacking model.
PloS one
Dec 31, 2024
BACKGROUND: Acute myocardial infarction (AMI) remains a leading cause of hospitalization and death in China. Accurate mortality prediction of inpatient is crucial for clinical decision-making of non-ST-segment elevation myocardial infarction (NSTEMI)...
A novel artificial intelligence segmentation model for early diagnosis of bladder tumors.
Abdominal radiology (New York)
Dec 30, 2024
OBJECTIVE: Despite cystoscopy plays an important role in bladder tumors diagnosis, it often falls short in flat cancerous tissue and minuscule satellite lesions. It can easily lead to a missed diagnosis by the urologist, which can lead to a swift tum...
Machine Learning Prediction of Early Recurrence in Gastric Cancer: A Nationwide Real-World Study.
Annals of surgical oncology
Dec 30, 2024
BACKGROUND: Patients with gastric cancer (GC) who experience early recurrence (ER) within 2 years postoperatively have poor prognoses. This study aimed to analyze and predict ER after curative surgery for patients with GC using machine learning (ML) ...
Machine learning-based forecast of Helmet-CPAP therapy failure in Acute Respiratory Distress Syndrome patients.
Computer methods and programs in biomedicine
Dec 30, 2024
BACKGROUND AND OBJECTIVE: Helmet-Continuous Positive Airway Pressure (H-CPAP) is a non-invasive respiratory support that is used for the treatment of Acute Respiratory Distress Syndrome (ARDS), a severe medical condition diagnosed when symptoms like ...
Noninvasive machine-learning models for the detection of lesion-specific ischemia in patients with stable angina with intermediate stenosis severity on coronary CT angiography.
Physical and engineering sciences in medicine
Dec 30, 2024
This study proposed noninvasive machine-learning models for the detection of lesion-specific ischemia (LSI) in patients with stable angina with intermediate stenosis severity based on coronary computed tomography (CT) angiography. This single-center ...
Machine learning and clinician predictions of antibiotic resistance in Enterobacterales bloodstream infections.
The Journal of infection
Dec 30, 2024
BACKGROUND: Patients with Gram-negative bloodstream infections are at risk of serious adverse outcomes without active treatment, but identifying who has antimicrobial resistance (AMR) to target empirical treatment is challenging.
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