AIMC Topic: Cohort Studies

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Machine Learning-based Prediction of Active Tuberculosis in People With HIV Using Clinical Data.

Clinical infectious diseases : an official publication of the Infectious Diseases Society of America
BACKGROUND: Coinfections of Mycobacterium tuberculosis (MTB) and human immunodeficiency virus (HIV) impose a substantial global health burden. Patients with MTB infection face a heightened risk of progression to incident active TB, which preventive t...

Comparison of CT referral justification using clinical decision support and large language models in a large European cohort.

European radiology
BACKGROUND: Ensuring appropriate use of CT scans is critical for patient safety and resource optimization. Decision support tools and artificial intelligence (AI), such as large language models (LLMs), have the potential to improve CT referral justif...

Machine learning-driven programmed cell death signature for prognosis and drug candidate discovery in diffuse large B-cell lymphoma: Multi-cohort study and experimental validation.

International immunopharmacology
BACKGROUND: Relapse and drug resistance are major contributor to chemotherapy failure in diffuse large B-cell lymphoma (DLBCL). Programmed cell death (PCD), a key mechanism in tumor progression and resistance, has emerged as a promising biomarker for...

Associations between Calcium Intake and T-cell Infiltration in Colorectal Tumors.

Cancer prevention research (Philadelphia, Pa.)
UNLABELLED: Higher T-cell infiltration in colorectal tumors has been associated with better prognosis. Evidence indicates that calcium signaling is essential for T-cell functioning. However, as it is unknown whether calcium intake influences T-cell i...

SPACE: Subregion Perfusion Analysis for Comprehensive Evaluation of Breast Tumor Using Contrast-Enhanced Ultrasound-A Retrospective and Prospective Multicenter Cohort Study.

Ultrasound in medicine & biology
OBJECTIVE: To develop a dynamic contrast-enhanced ultrasound (CEUS)-based method for segmenting tumor perfusion subregions, quantifying tumor heterogeneity, and constructing models for distinguishing benign from malignant breast tumors.

Dental caries detection in children using intraoral scans and deep learning.

Journal of dentistry
OBJECTIVE: This study aimed to demonstrate the use of deep learning for automating caries detection using intraoral scan data from children and to evaluate diagnostic agreement between the models' predictions and dental practitioner assessments on 3D...

Heterogeneity in the association between internet use and dementia among older adults: A machine-learning analysis.

Archives of gerontology and geriatrics
BACKGROUND & AIMS: Internet use among older adults may reduce the risk of dementia, but it remains unknown how the effects vary across individuals. The aim of this study was to rigorously examine heterogeneity in the association between internet use ...

The Best of All Worlds: A Hybrid Approach to Cohort Identification with Rules, Small and Large Language Models.

Studies in health technology and informatics
Balancing operational feasibility with the performance of natural language processing (NLP) systems is a significant challenge. This study presents a hybrid strategy to integrate manually curated rules, small language model (SLM), and large language ...

Machine learning diagnosis of cognitive impairment and dementia in harmonized older adult cohorts.

Alzheimer's & dementia : the journal of the Alzheimer's Association
INTRODUCTION: Clinical diagnosis (normal cognition, mild cognitive impairment [MCI], dementia) is critical for understanding cognitive impairment and dementia but can be resource intensive and subject to inconsistencies due to complex clinical judgme...

Glucagon-like Peptide-1 Receptor Agonists in Asthma Exacerbations: An Application of High-Dimensional Iterative Causal Forest to Identify Subgroups.

Pharmacoepidemiology and drug safety
BACKGROUND: Glucagon-like Peptide-1 Receptor Agonists (GLP1RA) may reduce asthma exacerbation (AE) risk, but it is unclear which populations benefit most. Recent pharmacoepidemiologic studies have employed iterative causal forest (iCF), a machine lea...