AIMC Topic: Machine Learning

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Generalizability of clinical prediction models in mental health.

Molecular psychiatry
Concerns about the generalizability of machine learning models in mental health arise, partly due to sampling effects and data disparities between research cohorts and real-world populations. We aimed to investigate whether a machine learning model t...

Emerging Applications of Digital Technologies for Periodontal Screening, Diagnosis and Prognosis in the Dental Setting.

Journal of clinical periodontology
AIM: To comprehensively review digital technologies (including artificial intelligence, AI) for periodontal screening, diagnosis and prognosis in the dental setting, focusing on accuracy metrics.

Integrated AI and machine learning pipeline identifies novel WEE1 kinase inhibitors for targeted cancer therapy.

Molecular diversity
The dysregulation of the cell cycle in cancer underscores the therapeutic potential of targeting WEE1 kinase, a key regulator of the G2/M checkpoint. This study harnessed artificial intelligence (AI)-driven methodologies, particularly the MORLD platf...

Perioperative risk assessment for emergency general surgery in those with multimorbidity or frailty.

Current opinion in critical care
PURPOSE OF REVIEW: This review explores advances in risk stratification tools and their applicability in identifying and managing high-risk emergency general surgery (EGS) patients.

The Role of AI in Modern Hernia Surgery: A Review and Practical Insights.

Surgical innovation
BackgroundArtificial intelligence (AI) is revolutionizing various aspects of health care, particularly in the surgical field, where it offers significant potential for improving surgical risk assessment, predictive analytics, and research advancement...

Machine learning models for enhanced diagnosis and risk assessment of prostate cancer with Ga-PSMA-617 PET/CT.

European journal of radiology
OBJECTIVE: Prostate cancer (PCa) is highly heterogeneous, making early detection of adverse pathological features crucial for improving patient outcomes. This study aims to predict PCa aggressiveness and identify radiomic and protein biomarkers assoc...

Uncovering hidden subtypes in dementia: An unsupervised machine learning approach to dementia diagnosis and personalization of care.

Journal of biomedical informatics
OBJECTIVE: Dementia represents a growing public health challenge, affecting an increasing number of individuals. It encompasses a broad spectrum of cognitive impairments, ranging from mild to severe stages, each of which demands varying levels of car...

Sandwich Miura-Ori Enabled Large Area, Super Resolution Tactile Skin for Human-Machine Interactions.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
With substantial advances in materials science and electronics, flexible tactile sensors have emerged as a promising sector with extensive applications, notably in human-machine interactions. However, achieving large-area sensing with few sensing uni...

Compressed Representation of Extreme Learning Machine with Self-Diffusion Graph Denoising Applied for Dissecting Molecular Heterogeneity.

Journal of computational biology : a journal of computational molecular cell biology
Molecular heterogeneity exists in many biological systems, such as major malignancies or diverse cell populations. Clustering of gene expression profiles has been widely used to dissect molecular heterogeneity. One drawback common to most clustering ...

Unsupervised Machine Learning-Based Image Recognition of Raw Infrared Spectra: Toward Chemist-like Chemical Structural Classification and Beyond Numerical Data.

Journal of chemical information and modeling
Recent advances in artificial intelligence have significantly improved spectral data analysis. In this study, we used unsupervised machine learning to classify chemical compounds based on infrared (IR) spectral images, without relying on prior chemic...