AIMC Topic: Case-Control Studies

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Construction of a diagnostic model for tuberculosis based on long non-coding RNA.

Annals of medicine
BACKGROUND: The World Health Organization encourages the development of novel diagnostic tools based on 'non-sputum' samples to meet global goals for tuberculosis (TB) control. We aimed to develop a machine learning-driven model for TB diagnosis, usi...

Irritable bowel syndrome-specific volatile organic compounds in faecal headspace do not associate with classical symptom-based subtypes.

Journal of breath research
Irritable bowel syndrome (IBS), a disorder of gut-brain interaction, is diagnosed using symptom-based Rome criteria. These criteria classify IBS patients into four subtypes in accordance to their stool patterns. However, whether this subtyping approa...

Integrative multi-omics and network-based machine learning for early diagnosis of Parkinson's disease.

PloS one
BACKGROUND: Accurate diagnosis of Parkinson's Disease (PD) remains challenging due to its biological complexity. Integrating machine learning with multi-omics and network topological analyses may enhance diagnostic precision.

Artificial Intelligence-Enhanced Multi-Algorithm R Shiny Application for Predictive Modeling and Analytics: Case Study of Alzheimer Disease Diagnostics.

JMIR aging
BACKGROUND: Artificial intelligence (AI) has demonstrated superior diagnostic accuracy compared with medical practitioners, highlighting its growing importance in health care. SMART-Pred (Shiny Multi-Algorithm R Tool for Predictive Modeling) is an in...

Diagnosis of chronic fatigue syndrome using beat-to-beat autonomic measurements.

Journal of translational medicine
BACKGROUND: An artificial intelligence (AI) pipeline was used to differentiate patients suffering from Chronic Fatigue Syndrome (CFS) from healthy controls (HC) based on high-frequency, large-scale data obtained using beat-to-beat measurement of the ...

Abnormal brain network reconfiguration in neuropsychiatric disorders across cognitive decline, Depression, and Schizophrenia.

PloS one
OBJECTIVE: Neuropsychiatric disorders are characterized by high complexity and comorbidity, imposing a substantial burden on both patients and society. However, their elusive pathogenic mechanisms impede accurate clinical diagnosis and effective inte...

ceRNA regulatory network and immune-neurodegenerative mechanisms of peripheral CD4+ T cells in parkinson's disease.

PloS one
Parkinson's disease (PD) is a neurodegenerative disorder characterized by dopaminergic neuron loss and neuroinflammation, with emerging roles of peripheral immune dysregulation in disease progression. This study aimed to investigate the regulatory ne...

Development of explainable machine learning models to predict side effects in patients with rheumatoid arthritis taking methotrexate treatment: a nationwide multicentre cohort study.

BMJ open
OBJECTIVES: Methotrexate (MTX) effectively controls rheumatoid arthritis (RA) but often leads to side effects (SE) such as gastrointestinal (GI) issues, liver toxicity and bone marrow suppression. To develop clinically interpretable machine learning ...

Enhanced hybrid deep neural network for EEG-based schizophrenia diagnosis using functional and temporal features.

Scientific reports
Schizophrenia is a complex psychiatric disorder that disrupts cognition, emotions, and social behavior. Timely and accurate diagnosis is essential for effective treatment. Traditional diagnostic methods relying on clinical assessments have limitation...

A noninvasive machine learning model using a complete blood count for screening of primary vitreoretinal lymphoma.

Nature communications
Primary vitreoretinal lymphoma (PVRL) is a rare and aggressive intraocular malignancy that is frequently misdiagnosed because of its nonspecific early manifestations and the lack of effective screening tools. We conduct a multicentre case-control stu...