AIMC Topic: Machine Learning

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Machine learning model to predict the adherence of tuberculosis patients experiencing increased levels of liver enzymes in Indonesia.

PloS one
Indonesia is still the second-highest tuberculosis burden country in the world. The antituberculosis adverse drug reaction and adherence may influence the success of treatment. The objective of this study is to define the model for predicting the adh...

A spatial interpolation method based on 3D-CNN for soil petroleum hydrocarbon pollution.

PloS one
Petroleum hydrocarbon pollution causes significant damage to soil, so accurate prediction and early intervention are crucial for sustainable soil management. However, traditional soil analysis methods often rely on statistical methods, which means th...

Low-cost and scalable machine learning model for identifying children and adolescents with poor oral health using survey data: An empirical study in Portugal.

PloS one
This empirical study assessed the potential of developing a machine-learning model to identify children and adolescents with poor oral health using only self-reported survey data. Such a model could enable scalable and cost-effective screening and ta...

Exploiting the similarity of dissimilarities for biomedical applications and enhanced machine learning.

PLoS computational biology
The "similarity of dissimilarities" is an emerging paradigm in biomedical science with significant implications for protein function prediction, machine learning (ML), and personalized medicine. In protein function prediction, recognizing dissimilari...

Unveiling diabetes onset: Optimized XGBoost with Bayesian optimization for enhanced prediction.

PloS one
Diabetes, a chronic condition affecting millions worldwide, necessitates early intervention to prevent severe complications. While accurately predicting diabetes onset or progression remains challenging due to complex and imbalanced datasets, recent ...

Deciphering the role of metal ion transport-related genes in T2D pathogenesis and immune cell infiltration via scRNA-seq and machine learning.

Frontiers in immunology
INTRODUCTION: Type 2 diabetes (T2D) is a complex metabolic disorder with significant global health implications. Understanding the molecular mechanisms underlying T2D is crucial for developing effective therapeutic strategies. This study employs sing...

Development and validation of a short form for the Raven's Coloured Progressive Matrices using a machine learning approach.

The British journal of developmental psychology
Raven's Coloured Progressive Matrices (CPM) is a widely used assessment tool for measuring general cognitive ability in developmental and educational research, particularly in studies involving young children. However, administering the full set of t...

Artificial intelligence and machine learning capabilities in the detection of acute scaphoid fracture: a critical review.

The Journal of hand surgery, European volume
This paper discusses the current literature surrounding the potential use of artificial intelligence and machine learning models in the diagnosis of acute obvious and occult scaphoid fractures. Current studies have notable methodological flaws and ar...

Knowledge-Guided Semantically Consistent Contrastive Learning for sequential recommendation.

Neural networks : the official journal of the International Neural Network Society
Contrastive learning has gained dominance in sequential recommendation due to its ability to derive self-supervised signals for addressing data sparsity problems. However, caused by random augmentations (e.g., crop, mask, and reorder), existing metho...

Attention-based deep learning models for predicting anomalous shock of wastewater treatment plants.

Water research
Quickly grasping the time-consuming water quality indicators (WQIs) such as total nitrogen (TN) and total phosphorus (TP) of influent is an essential prerequisite for wastewater treatment plants (WWTPs) to prompt respond to sudden shock loads. Soft d...