AIMC Topic: Aged

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Machine learning approaches for predicting heart failure readmissions.

Postgraduate medical journal
PURPOSE: This study aims to develop and evaluate machine learning (ML) models to predict the likelihood of hospital readmission within 30 days after discharge for patients with heart failure (HF). The goal is to compare the predictive accuracy of ML ...

Stereotactic Radiation Therapy or Protons for Uveal Melanoma Patients? An Artificial Intelligence (AI)-Based Clinical Treatment Decision-Making Tool Predicting Doses To Radiation Therapy Constraints.

International journal of radiation oncology, biology, physics
PURPOSE: For ocular melanoma, selecting between stereotactic radiation therapy (SRT) and protons requires a lengthy plan comparison process. The purpose of this brief report is to describe an artificial intelligence (AI) decision-making tool to predi...

Association and prediction of serum lipid profiles with incident stroke in the CHARLS cohort: A machine learning analysis.

Medicine
Using the 2011 baseline data of the China health and retirement longitudinal study, we examined the associations between serum lipids and other risk factors and incident stroke, and developed and compared multiple machine learning models for stroke-r...

Orthopedic perioperative nursing under navigation nurse management: Machine learning-based risk prediction models for postoperative recovery quality and explainable artificial intelligence analysis.

Medicine
This study aimed to evaluate the effectiveness of navigation nurse management (NNM) in orthopedic perioperative care and develop machine learning (ML) models to predict postoperative recovery quality. We sought to identify key factors influencing rec...

The non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) as predictors of hypertensive patients: Analyses of NHANES data with machine learning.

Medicine
Elevated values of the non-HDL/HDL cholesterol ratio (NHHR) have been associated with increased hypertension risk, indicating its potential as a pathogenic factor, but its assessment remains challenging. We analyzed data from 22,562 hypertensive part...

Machine learning-based screening of characteristic factors for urinary tract infection following ureteral stone surgery and construction and validation of risk prediction models.

Medicine
Ureteroscopic lithotripsy has emerged as the cornerstone treatment modality for ureteral stones due to its exceptional success rates and minimal complication profiles. Nevertheless, postoperative urinary tract infection (UTI) remains a prevalent and ...

Multimodal Approach Predicts Relapse upon Cessation of Immune Checkpoint Inhibitors in Advanced Melanoma.

Clinical cancer research : an official journal of the American Association for Cancer Research
PURPOSE: Treatment with immune checkpoint inhibitors (ICI) in advanced melanoma can result in durable responses, yet an algorithm to decide which patients can safely discontinue ICI is still lacking.

Identifying azithromycin responders with an individual treatment effect model in COPD.

Thorax
OBJECTIVE: Long-term azithromycin treatment effectively prevents acute exacerbations of chronic obstructive pulmonary disease (COPD). However, patients would benefit from better identification of responders and non-responders to minimise unnecessary ...

Deep Learning Model for Breast Shear Wave Elastography to Improve Breast Cancer Diagnosis (INSPiRED 006): An International, Multicenter Analysis.

Journal of clinical oncology : official journal of the American Society of Clinical Oncology
PURPOSE: Shear wave elastography (SWE) has been investigated as a complement to B-mode ultrasound for breast cancer diagnosis. Although multicenter trials suggest benefits for patients with Breast Imaging Reporting and Data System (BI-RADS) 4(a) brea...

Detection of common bile duct dilatation on magnetic resonance cholangiopancreatography by deep learning.

Diagnostic and interventional radiology (Ankara, Turkey)
PURPOSE: This study aims to detect common bile duct (CBD) dilatation using deep learning methods from artificial intelligence algorithms.