AIMC Topic: Machine Learning

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Challenges for Predictive Modeling With Neural Network Techniques Using Error-Prone Dietary Intake Data.

Statistics in medicine
Dietary intake data are routinely drawn upon to explore diet-health relationships, and inform clinical practice and public health. However, these data are almost always subject to measurement error, distorting true diet-health relationships. Beyond m...

[Classification of Alzheimer's disease based on multi-example learning and multi-scale feature fusion].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
Alzheimer's disease (AD) classification models usually segment the entire brain image into voxel blocks and assign them labels consistent with the entire image, but not every voxel block is closely related to the disease. To this end, an AD auxiliary...

[Prediction method of paroxysmal atrial fibrillation based on multimodal feature fusion].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
The risk prediction of paroxysmal atrial fibrillation (PAF) is a challenge in the field of biomedical engineering. This study integrated the advantages of machine learning feature engineering and end-to-end modeling of deep learning to propose a PAF ...

[The joint analysis of heart health and mental health based on continual learning].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
Cardiovascular diseases and psychological disorders represent two major threats to human physical and mental health. Research on electrocardiogram (ECG) signals offers valuable opportunities to address these issues. However, existing methods are cons...

AI-driven health analysis for emerging respiratory diseases: A case study of Yemen patients using COVID-19 data.

Mathematical biosciences and engineering : MBE
In low-income and resource-limited countries, distinguishing COVID-19 from other respiratory diseases is challenging due to similar symptoms and the prevalence of comorbidities. In Yemen, acute comorbidities further complicate the differentiation bet...

Predicting psoriasis severity using machine learning: a systematic review.

Clinical and experimental dermatology
BACKGROUND: In dermatology, the applications of machine learning (ML), an artificial intelligence (AI) subset that enables machines to learn from experience, have progressed past the diagnosis and classification of skin lesions. A lack of systematic ...

[Construction and preliminary validation of machine learning predictive models for cervical cancer screening based on human DNA methylation].

Zhonghua zhong liu za zhi [Chinese journal of oncology]
Using methylation characteristics of human genes to construct machine learning predictive models for screening cervical cancer and precancerous lesions. Human DNA methylation detection was performed on 224 cervical exfoliated cell specimens from th...

[Exploration of the Predictive Value of Peripheral Blood-related Indicators for EGFR 
Mutations and Prognosis in Non-small Cell Lung Cancer Using Machine Learning].

Zhongguo fei ai za zhi = Chinese journal of lung cancer
BACKGROUND: Epidermal growth factor receptor (EGFR) sensitive mutation is one of the effective targets of targeted therapy for non-small cell lung cancer (NSCLC). However, due to the difficulty of obtaining some primary tissues and the economic facto...

Stimulus Selection Influences Prediction of Individual Phenotypes in Naturalistic Conditions.

Human brain mapping
While the use of naturalistic stimuli such as movie clips for understanding individual differences and brain-behaviour relationships attracts increasing interest, the influence of stimulus selection remains largely unclear. By using machine learning ...

Recurrent Neural Network/Machine Learning Predictions of Reactive Channels in H + CH at E = 30 eV: A Prototype of Ion Cancer Therapy Reactions.

Journal of computational chemistry
We present a simplest-level electron nuclear dynamics/machine learning (SLEND/ML) approach to predict chemical properties in ion cancer therapy (ICT) reactions. SLEND is a time-dependent, variational, on-the-fly, and nonadiabatic method. In SLEND, nu...