AIMC Topic: Machine Learning

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Assessment of Blood Glucose Measurement Using New Noninvasive Technology: Protocol and Methodology.

JMIR research protocols
BACKGROUND: Diabetes mellitus (DM) is a major noncommunicable disease with a significant increase in prevalence, especially in low- and middle-income countries. The latest International Diabetes Federation Diabetes Atlas (2025) reports that 11.1% of ...

Intervention in Health Misinformation Using Large Language Models for Automated Detection, Thematic Analysis, and Inoculation: Case Study on COVID-19.

Journal of medical Internet research
BACKGROUND: The rapid growth of social media as an information channel has enabled the swift spread of inaccurate or false health information, significantly impacting public health. This widespread dissemination of misinformation has caused confusion...

National Institutes of Health-Funded Artificial Intelligence and Machine Learning Research, 2019-2023: Cross-Sectional Study.

Journal of medical Internet research
Inflation-adjusted funding for artificial intelligence and machine learning research increased by 233% between fiscal year 2019 and 2023, outpacing the overall National Institutes of Health's budget increase of 12%.

Soil geochemistry and contamination zoning in Northeastern Ghana: insights from the Bongo and Talensi districts.

Environmental geochemistry and health
Reliable geochemical baselines are largely absent for northern Ghana, limiting efforts to distinguish natural element variability from human-induced contamination. This study addresses that gap by evaluating soil geochemical compositions in the Bongo...

Long-term benefit from high-dose ifosfamide in sarcoma depends on sustained prior control and timely intervention: a machine learning analysis.

Journal of cancer research and clinical oncology
PURPOSE: High-dose ifosfamide (HD-IFO) remains an effective regimen for advanced bone and soft tissue sarcomas, but predictors of long-term benefit are poorly defined. This study evaluated clinical outcomes and prognostic factors using machine learni...

An Introduction to Pathology Foundation Models.

Head and neck pathology
Foundation models are a recently described class of machine learning algorithms that use large amounts of data and training techniques that do not require content expert data labeling. They are trained to gain a representation of what patterns exist ...

Integrating Google Earth Engine and machine learning for urban land use and land cover dynamics analysis.

Environmental monitoring and assessment
The accurate land use and land cover (LULC) classification in the data-scarce urbanized region of Peshawar remains challenging due to computational limitations, accuracy assessment, and traditional techniques. This study, for the first time, addresse...

Evaluation of conditional treatment effect of salt stress on tomato sugar content using causal machine learning: A pilot study.

PloS one
Exposing tomatoes to salt stress has been reported to increase the fruit sugar content (°Brix); however, the causal impact of this treatment under varying environmental conditions remains unclear. In this pilot study, a causal inference analysis was ...

Hypoxemia prediction in pediatric patients under general anesthesia using machine learning: A retrospective observational study and external validation.

PloS one
BACKGROUND: Pediatric patients under general anesthesia are particularly vulnerable to hypoxemia, which can lead to rapid oxygen desaturation. This vulnerability necessitates heightened vigilance from anesthesiologists, making pediatric anesthesia ma...

Multi-objective QSAR prediction of ERα antagonists via SHAP-based interpretation.

PloS one
To achieve a comprehensive evaluation of candidate drugs in terms of both biological activity and ADMET properties, this study proposes a two-stage predictive framework based on Quantitative Structure-Activity Relationship (QSAR) modeling integrated ...