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miRNAs expression pattern and machine learning models elucidate risk for gastric GIST.

Cancer biomarkers : section A of Disease markers
BACKGROUND: Gatrointestinal stromal tumors (GISTs) are the main mesenchymal tumors found in the gastrointestinal system. GISTs clinical phenotypes differ significantly and their molecular basis is not yet completely known. microRNAs (miRNAs) have bee...

Relationship of MRI-measured pelvimetric dimensions and surgical positions with anaesthesia parameters in robotic perineal prostatectomy.

Archivos espanoles de urologia
Theaim of this study was to determine the effect formedby pelvic diameters preoperatively measured throughmultiparametric magnetic resonance imaging (mp-MRI) and different surgical positions on anesthesiaparameters used during perineal robot-assisted...

Minimally invasive lymphocele drainage using the Da VinciĀ® single port platform: step-by- step technique.

International braz j urol : official journal of the Brazilian Society of Urology
BACKGROUND: Reports in the literature describe lymphocele formation in up to half of patients following pelvic lymph node dissection (PLND) (1) in robotic-assisted radical prostatectomy (RARP), with 1-2% requiring intervention (2). The advantage of s...

Genome-wide association study-based prediction of atrial fibrillation using artificial intelligence.

Open heart
OBJECTIVE: We previously reported early-onset atrial fibrillation (AF) associated genetic loci among a Korean population. We explored whether the AF-associated single-nucleotide polymorphisms (SNPs) selected from the Genome-Wide Association Study (GW...

Demographic Reporting in Publicly Available Chest Radiograph Data Sets: Opportunities for Mitigating Sex and Racial Disparities in Deep Learning Models.

Journal of the American College of Radiology : JACR
OBJECTIVE: Data sets with demographic imbalances can introduce bias in deep learning models and potentially amplify existing health disparities. We evaluated the reporting of demographics and potential biases in publicly available chest radiograph (C...

A Machine Learning-Based Holistic Approach to Predict the Clinical Course of Patients within the Alzheimer's Disease Spectrum.

Journal of Alzheimer's disease : JAD
BACKGROUND: Alzheimer's disease (AD) is a neurodegenerative condition driven by multifactorial etiology. Mild cognitive impairment (MCI) is a transitional condition between healthy aging and dementia. No reliable biomarkers are available to predict t...

Development and validation of a radiopathomics model to predict pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a multicentre observational study.

The Lancet. Digital health
BACKGROUND: Accurate prediction of tumour response to neoadjuvant chemoradiotherapy enables personalised perioperative therapy for locally advanced rectal cancer. We aimed to develop and validate an artificial intelligence radiopathomics integrated m...

Development and validation of an artificial neural network algorithm to predict mortality and admission to hospital for heart failure after myocardial infarction: a nationwide population-based study.

The Lancet. Digital health
BACKGROUND: Patients have an estimated mortality of 15-20% within the first year following myocardial infarction and one in four patients who survive myocardial infarction will develop heart failure, severely reducing quality of life and increasing t...

Electronic health record machine learning model predicts trauma inpatient mortality in real time: A validation study.

The journal of trauma and acute care surgery
INTRODUCTION: Patient outcome prediction models are underused in clinical practice because of lack of integration with real-time patient data. The electronic health record (EHR) has the ability to use machine learning (ML) to develop predictive model...