AIMC Topic: Machine Learning

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Reliable biomarkers for diabetic nephropathy using machine learning-assisted contrast-enhanced ultrasonography and clinical characteristics.

Clinical and experimental medicine
OBJECTIVE: To utilize machine learning techniques to screen contrast-enhanced ultrasound (CEUS) parameters and clinical characteristics, aiming to differentiate diabetic nephropathy (DN) from non-diabetic renal disease (NDRD) in patients with diabeti...

The ferroptosis-related gene MAFG screened by machine learning is associated with the diagnosis and prognosis of sepsis.

Clinical and experimental medicine
Ferroptosis is a novel form of cell death induced by ferrous ions and lipid peroxidation. However, the mechanisms of ferroptosis-related genes (FRGs) in sepsis have not been studied thoroughly. We performed differential analysis using GSE65682, and t...

Identification of potential biomarkers for Lyme disease using bioinformatics and machine learning.

Clinical and experimental medicine
Lyme disease (LD) presents significant diagnostic challenges due to the absence of a reliable screening method for initial detection. This study aimed to identify potential biomarkers using bioinformatics and machine learning algorithms, which may co...

DF-OSELM: a dynamic feedback feature learning model for air quality online prediction.

Environmental monitoring and assessment
Accurate and timely air quality forecasting is crucial for mitigating pollution risks and protecting public health. However, existing offline and online models face limitations in adaptability, computational efficiency, and interpretability. To addre...

PhysioFormer: Integrating multimodal physiological signals and symbolic regression for explainable affective state prediction.

PloS one
As affective computing becomes increasingly crucial in health monitoring and psychological intervention, accurately identifying affective states is a key challenge. While traditional machine learning models have achieved some success in affective com...

Machine learning algorithms for predicting and identifying the influencing predictors of antenatal care visits among women in Bangladesh: Evidence from BDHS 2022 data.

PloS one
BACKGROUND AND OBJECTIVE: Bangladesh, a South Asian country, continues to face significant challenges in maternal health, as reflected by its high maternal mortality ratio (MMR). According to the 2022 Bangladesh Demographic and Health Survey (BDHS), ...

An interpretable and balanced machine learning framework for Parkinson's disease prediction using feature engineering and explainable AI.

PloS one
Parkinson's disease (PD) is a progressive neurological disorder that affects millions globally, posing significant challenges in early and accurate diagnosis. Recent advancements in machine learning (ML) offer promising approaches for addressing thes...

Conditional VAE for personalized neurofeedback in cognitive training.

PloS one
Machine learning (ML) offers great potential in healthcare, especially in the analysis of complex physiological signals like electroencephalography (EEG). EEG recordings hold valuable insights into neurological function and can aid in diagnosing vari...

Dynamic forecasting and mechanisms of volatility synchronization in complex financial systems.

PloS one
Synchronization, which has been a common natural phenomenon, occurs frequently in complex financial systems and is an important contagion mechanism for systemic financial risks and even financial crises. In view of this, we construct a coupled stocha...

Causal predictive modeling of survival of lung and bronchus cancer patients diagnosed during 2010-2011 in Texas.

PloS one
BACKGROUND: Lung and Bronchus cancer is the most fatal type of cancer in the United States. According to the American Cancer Society, there were more than 127,000 deaths from lung cancer in 2023. Lung cancer care cost 23.8 billion dollars in 2020. In...