AIMC Topic: Machine Learning

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Development and validation of an imageless machine-learning algorithm for the initial screening of prostate cancer.

The Prostate
PURPOSE: Prostate specific antigen (PSA) testing is a low-cost screening method for prostate cancer (PCa). However, its accuracy is limited. While progress is being made using medical imaging for PCa screening, PSA testing can still be improved as an...

Identification of KRAS mutation-associated gut microbiota in colorectal cancer and construction of predictive machine learning model.

Microbiology spectrum
Gut microbiota has demonstrated an increasingly important role in the onset and development of colorectal cancer (CRC). Nonetheless, the association between gut microbiota and KRAS mutation in CRC remains enigmatic. We conducted 16S rRNA sequencing o...

Application of machine learning in affordable and accessible insulin management for type 1 and 2 diabetes: A comprehensive review.

Artificial intelligence in medicine
Proper insulin management is vital for maintaining stable blood sugar levels and preventing complications associated with diabetes. However, the soaring costs of insulin present significant challenges to ensuring affordable management. This paper con...

Harnessing machine learning for EEG signal analysis: Innovations in depth of anaesthesia assessment.

Artificial intelligence in medicine
Anaesthesia, crucial to surgical practice, is undergoing renewed scrutiny due to the integration of artificial intelligence in its medical use. The precise control over the temporary loss of consciousness is vital to ensure safe, pain-free procedures...

Machine learning for high solid anaerobic digestion: Performance prediction and optimization.

Bioresource technology
Biogas production through anaerobic digestion (AD) is one of the complex non-linear biological processes, wherein understanding its dynamics plays a crucial role towards process control and optimization. In this work, a machine learning based biogas ...

Integrated assessment of potentially toxic elements in soil of the Kangdian metallogenic province: A two-point machine learning approach.

Ecotoxicology and environmental safety
The accumulation of potentially toxic elements in soil poses significant risks to ecosystems and human well-being due to their inherent toxicity, widespread presence, and persistence. The Kangdian metallogenic province, famous for its iron-copper dep...

Bio-QSARs 2.0: Unlocking a new level of predictive power for machine learning-based ecotoxicity predictions by exploiting chemical and biological information.

Environment international
Practical, legal, and ethical reasons necessitate the development of methods to replace animal experiments. Computational techniques to acquire information that traditionally relied on animal testing are considered a crucial pillar among these so-cal...

Manifold Learning-Based Common Spatial Pattern for EEG Signal Classification.

IEEE journal of biomedical and health informatics
EEG signal classification using Riemannian manifolds has shown great potential. However, the huge computational cost associated with Riemannian metrics poses challenges for applying Riemannian methods, particularly in high-dimensional feature data. T...

Association between match-related physical activity profiles and playing positions in different tasks: A data driven approach.

Journal of sports sciences
Assessing the intensity characteristics of specific soccer drills (matches, small-side game, and match-based exercises) could help practitioners to plan training sessions by providing the optimal stimulus for every player. In this paper, we propose a...