AIMC Topic: Middle Aged

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Detection and classification of glomerular lesions in kidney graft biopsies using 2-stage deep learning approach.

Medicine
Acute allograft rejection in patients undergoing renal transplantation is diagnosed through histopathological analysis of renal graft biopsies, which can be used to quantify elementary lesions. However, quantification of elementary lesions requires c...

Machine learning algorithm approach to complete blood count can be used as early predictor of COVID-19 outcome.

Journal of leukocyte biology
Although the SARS-CoV-2 infection has established risk groups, identifying biomarkers for disease outcomes is still crucial to stratify patient risk and enhance clinical management. Optimal efficacy of COVID-19 antiviral medications relies on early a...

Mortality risk assessment using deep learning-based frequency analysis of electroencephalography and electrooculography in sleep.

Sleep
STUDY OBJECTIVES: To assess whether the frequency content of electroencephalography (EEG) and electrooculography (EOG) during nocturnal polysomnography (PSG) can predict all-cause mortality.

Building a cancer risk and survival prediction model based on social determinants of health combined with machine learning: A NHANES 1999 to 2018 retrospective cohort study.

Medicine
The occurrence and progression of cancer is a significant focus of research worldwide, often accompanied by a prolonged disease course. Concurrently, researchers have identified that social determinants of health (SDOH) (employment status, family inc...

Machine learning model using immune indicators to predict outcomes in early liver cancer.

World journal of gastroenterology
BACKGROUND: Patients with early-stage hepatocellular carcinoma (HCC) generally have good survival rates following surgical resection. However, a subset of these patients experience recurrence within five years post-surgery.

Differentiating between renal medullary and clear cell renal carcinoma with a machine learning radiomics approach.

The oncologist
BACKGROUND: The objective of this study was to develop and validate a radiomics-based machine learning (ML) model to differentiate between renal medullary carcinoma (RMC) and clear cell renal carcinoma (ccRCC).

Can metformin prevent cancer relative to sulfonylureas? A target trial emulation accounting for competing risks and poor overlap via double/debiased machine learning estimators.

American journal of epidemiology
There is mounting interest in the possibility that metformin, indicated for glycemic control in type 2 diabetes, has a range of additional beneficial effects. Randomized trials have shown that metformin prevents adverse cardiovascular events, and met...

Prevalence, incidence, and mortality of inflammatory bowel disease in the Netherlands: development and external validation of machine learning models.

Journal of Crohn's & colitis
BACKGROUND AND AIMS: Large registries are promising tools to study the epidemiology of inflammatory bowel disease (IBD). We aimed to develop and validate machine learning models to identify IBD cases in administrative data, aiming to determine the pr...

Machine learning and metabolomics identify biomarkers associated with the disease extent of ulcerative colitis.

Journal of Crohn's & colitis
BACKGROUND AND AIMS: Ulcerative colitis (UC) is a metabolism-related chronic intestinal inflammatory disease. Disease extent is a key parameter of UC. Using serum metabolic profiling to identify noninvasive biomarkers of disease extent may inform the...

Automated Detection of Retinal Detachment Using Deep Learning-Based Segmentation on Ocular Ultrasonography Images.

Translational vision science & technology
PURPOSE: This study aims to develop an automated pipeline to detect retinal detachment from B-scan ocular ultrasonography (USG) images by using deep learning-based segmentation.