AIMC Topic: Machine Learning

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Evaluation of biomarkers and immune microenvironment of gestational diabetes mellitus evidence from omics data and machine learning.

Scientific reports
This study aimed to identify core genes of Gestational diabetes mellitus (GDM) and explore its immune microenvironment. Using the limma package, we were able to identify differentially expressed genes (DEGs) between GDM and normal placental tissue. W...

Optimising hyperparameters with a tree structured Parzen estimator to improve diabetes prediction.

Scientific reports
Diabetes is a lifelong condition that occurs when the pancreas loses its ability to secrete insulin or experiences a significant reduction in insulin production. Early identification of high-risk patients is crucial for timely interventions and impro...

Identification and validation of cell senescence genes in recurrent spontaneous abortion via multiple bioinformatics algorithms.

Scientific reports
Recurrent spontaneous abortion (RSA) represents a significant challenge in reproductive obstetrics, affecting approximately 5% of couples globally. Despite various treatments, the effectiveness of these interventions remains highly contentious. Emerg...

Predictive modeling of adaptive behavior trajectories in autism: insights from a clinical cohort study.

Translational psychiatry
Research aimed at understanding how baseline clinical and demographic characteristics influence outcomes over time is critically important to inform individualized therapeutic programs for children with neurodevelopmental differences. This study char...

Novel electroencephalographic biomarkers for the prediction of responders to an experimental glutamatergic agent in patients with schizophrenia.

Translational psychiatry
All medications currently used to treat schizophrenia, which exert their therapeutic effects by inhibiting dopaminergic neurotransmission, have their greatest efficacy against the positive symptoms of schizophrenia but have limited impact on negative...

Development and validation of a model for predicting depression risk in primary palmar hyperhidrosis: a cross-sectional retrospective observational study.

BMJ open
OBJECTIVE: Primary palmar hyperhidrosis (PPH), characterised by excessive palm sweating, significantly impacts patients' physiology, psychology, self-esteem, work, life and social interactions. The incidence of depression is higher among PPH patients...

Machine learning model to classify chronic leg wounds and identify pyoderma gangrenosum.

BMJ health & care informatics
STUDY OBJECTIVES: Chronic wounds represent a significant economic and personal burden. For their successful treatment, the causes must be known and treated. Wounds caused by pyoderma gangrenosum (PG), a rare inflammatory skin disease, are often misdi...

Evaluation of Machine Learning Model Performance in Diabetic Foot Ulcer: Retrospective Cohort Study.

JMIR medical informatics
BACKGROUND: Machine learning (ML) has shown great potential in recognizing complex disease patterns and supporting clinical decision-making. Diabetic foot ulcers (DFUs) represent a significant multifactorial medical problem with high incidence and se...

High-resolution agricultural drought hazard mapping using the potential of geospatial data and machine learning approaches.

Environmental monitoring and assessment
Effective delineation of Agricultural Drought Hazard (ADH) zones is crucial for mitigating crop losses and ensuring water security in semi-arid regions. Conventional agricultural drought assessment methods, reliant on single-index approaches or stati...