AIMC Topic: Machine Learning

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Automated vertigo diagnosis using video-oculography and artificial intelligence for clinic-based triage: Preliminary results.

Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
BACKGROUND: Distinguishing dangerous from benign vertigo remains a diagnostic challenge. Our study aimed to develop and evaluate a machine learning model to differentiate between dangerous and benign vertigo in the outpatient setting across two medic...

Air pollution macro-regions identification using machine learning and spatio-temporal analysis.

PloS one
Air pollution caused by suspended particulate matter (PM) remains one of the key environmental challenges in Poland, particularly in the context of public health and spatial planning. This study presents a spatio-temporal analysis based on data from ...

Prodrug-ML: prodrug-likeness prediction via machine learning on sampled negative decoys.

Journal of computer-aided molecular design
A prodrug is a pharmacologically inactive (or attenuated) derivative that undergoes bioreversible transformation in vivo to release an active parent drug, enabling temporary optimization of properties such as solubility, permeability, and targeting. ...

Cell death-associated genes as novel diagnostic biomarkers for autism spectrum disorder.

Apoptosis : an international journal on programmed cell death
Autism spectrum disorder (ASD) involves neuroinflammation and dysregulated neuronal death but lacks objective diagnostic biomarkers. This study investigated whether cell death could serve as a molecular basis for ASD diagnosis. We identified cell dea...

Machine Learning Analysis of Retrospective Data From 503 Hospitalized Older Patients With Type 2 Diabetes to Identify Factors Associated With Cognitive Impairment.

Medical science monitor : international medical journal of experimental and clinical research
BACKGROUND Diabetes is increasingly prevalent among older adults; mild cognitive impairment (MCI) comorbidity in this group represents a major concern. Existing MCI prediction methods are often inaccurate, but machine learning (ML) offers improved po...

Machine learning-based integration of tumor deposit molecular signatures improves prognostic stratification in colon adenocarcinoma.

International journal of colorectal disease
BACKGROUND: Colon adenocarcinoma (COAD) remains a leading cause of cancer-related mortality worldwide. Although tumor deposits (TDs) are established prognostic indicators, their molecular characteristics and potential for improving risk stratificatio...

Implementing QbD for Nano-Pharmaceuticals and Complex Formulations to Achieve Predictable and High-Quality Outcomes.

AAPS PharmSciTech
Recent advances in artificial intelligence (AI) and machine learning (ML) are revolutionizing nanopharmaceutical development by enabling data-driven formulation design, process optimization, and prediction of biological performance. AI encompasses co...

An interpretable machine learning model based on MRI radiomics and GAME score for predicting early recurrence after thermal ablation in colorectal liver metastases.

International journal of colorectal disease
OBJECTIVE: To develop and validate machine learning models based on preoperative magnetic resonance imaging(MRI) and baseline clinical characteristics for predicting early recurrence(ER) in patients with colorectal liver metastases(CRLM) treated with...

Physical Activity Recommendations Tailored by a Predictive Model for Adults With High Blood Pressure: Observational Study.

Journal of medical Internet research
BACKGROUND: Whether the benefits of identical physical activity (PA) patterns for adults with high blood pressure (BP) vary according to an individual's characteristics has not been adequately studied.

Quantifying key drivers of atmospheric methane across Pakistan using a machine learning approach.

Environmental monitoring and assessment
Atmospheric methane (CH), a potent greenhouse gas, has shown a consistent rise since the Industrial Revolution, contributing significantly to global warming and climate change. Understanding the temporal and spatial variability of methane concentrati...