AIMC Topic: Machine Learning

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Precise classification of traditional Chinese medicine sources using intelligent fusion of hyperspectral imaging-mass spectrometry data combined with machine learning: A case study of American ginseng.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
The application of artificial intelligence in traditional Chinese medicine (TCM) has become a hot topic in the scientific community. American ginseng (AG), a perennial herb with a rich history, is widely utilized in clinical settings due to its diver...

Detection of Brain Cancer Using Genome-wide Cell-free DNA Fragmentomes.

Cancer discovery
UNLABELLED: Diagnostic delays in patients with brain cancer are common and can impact patient outcome. Development of a blood-based assay for detection of brain cancers could accelerate brain cancer diagnosis. In this study, we analyzed genome-wide c...

Bioactive films with essential oils and machine learning for controlling Aspergillus niger growth and fumonisin B production in vitro.

International journal of food microbiology
Aspergillus niger is an important species in the fungal community of many foods and is one of the most significant microorganisms used in biotechnology. Some A. niger strains are capable of producing fumonisin B (FB) under certain conditions, but lit...

Automatic and precise identification of volatile organic compounds from gas chromatography in prolonged atmospheric monitoring.

Journal of chromatography. A
Long-term continuous monitoring of volatile organic compounds (VOCs) is pivotal for climate change research, air quality assessment, pollution source identification, and public health early warning systems. Prolonged VOC monitoring is routinely imple...

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...

Predicting p53 Status in IDH-Mutant Gliomas Using MRI-Based Radiomic Model.

Cancer medicine
OBJECTIVES: Accurate and noninvasive detection of p53 status in isocitrate dehydrogenase mutant (IDH-mt) glioma is clinically meaningful for molecular stratification of glioma, yet it remains challenging. We aimed to investigate the diagnostic effica...

Machine Learning Model for Predicting Pathological Invasiveness of Pulmonary Ground-Glass Nodules Based on AI-Extracted Radiomic Features.

Thoracic cancer
BACKGROUND: With the widespread adoption of low-dose CT screening, the detection of pulmonary ground-glass nodules (GGNs) has risen markedly, presenting diagnostic challenges in distinguishing preinvasive lesions from invasive adenocarcinomas (IAC). ...

Diagnostic Machine Learning Models of Infectious Mononucleosis in Children Based on Clinical Data: A Retrospective Multicenter Study.

Journal of medical virology
The clinical manifestations of infectious mononucleosis (IM) and acute respiratory tract infections (ARTI) exhibit significant similarities. We aim to develop cost-efficient models for IM in children utilizing the Shapley Additive explanation (SHAP) ...

Developing an Explainable Prognostic Model for Acute Ischemic Stroke: Combining Clinical and Inflammatory Biomarkers With Machine Learning.

Brain and behavior
BACKGROUND: Predicting the prognosis of patients with acute cerebral infarction (ACI) is crucial for clinical decision-making and personalized treatment. However, existing models often lack the comprehensive integration of clinical and biological ind...

Afforestation Surpasses Abandonment in the Recovery of Post-Agricultural Soil Organic Carbon in China as Estimated by Machine Learning Models.

Global change biology
The surface soil organic carbon (SOC) dynamics typically follow a trend of initial loss followed by subsequent accumulation after cropland abandonment. However, the timing of SOC stock increase (referred to as the threshold in this study) remains ins...