AIMC Topic: Machine Learning

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Utilizing patient data: A tutorial on predicting second cancer with machine learning models.

Cancer medicine
BACKGROUND: The article explores the potential risk of secondary cancer (SC) due to radiation therapy (RT) and highlights the necessity for new modeling techniques to mitigate this risk.

Identification of novel M2 macrophage-related molecule ATP6V1E1 and its biological role in hepatocellular carcinoma based on machine learning algorithms.

Journal of cellular and molecular medicine
Hepatocellular carcinoma (HCC) remains the most prevalent form of primary liver cancer, characterized by late detection and suboptimal response to current therapies. The tumour microenvironment, especially the role of M2 macrophages, is pivotal in th...

Vascular liver segmentation: a narrative review on methods and new insights brought by artificial intelligence.

The Journal of international medical research
Liver vessel segmentation from routinely performed medical imaging is a useful tool for diagnosis, treatment planning and delivery, and prognosis evaluation for many diseases, particularly liver cancer. A precise representation of liver anatomy is cr...

Identification of putative coral pathogens in endangered Caribbean staghorn coral using machine learning.

Environmental microbiology
Coral diseases contribute to the rapid decline in coral reefs worldwide, and yet coral bacterial pathogens have proved difficult to identify because 16S rRNA gene surveys typically identify tens to hundreds of disease-associate bacteria as putative p...

[APPLICATIONS OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN REHABILITATION].

Harefuah
The use of artificial intelligence applications in medicine has been common in recent decades. Machine learning, a subfield of artificial intelligence, is a methodology that allows computers to learn from examples and draw conclusions about new data....

Machines, psychology, and hypothesis generation: Commentary on Banker et al. (2024).

The American psychologist
Advances in machine learning and artificial intelligence are revolutionizing many aspects of human life, and as Banker et al. (2024) illustrate, generative artificial intelligence may also facilitate hypothesis generation in academic research. But wh...

Machine Learning-Driven Phenogrouping and Cardiorespiratory Fitness Response in Metastatic Breast Cancer.

JCO clinical cancer informatics
PURPOSE: The magnitude of cardiorespiratory fitness (CRF) impairment during anticancer treatment and CRF response to aerobic exercise training (AT) are highly variable. The aim of this ancillary analysis was to leverage machine learning approaches to...

Contribution of machine learning for subspecies identification from Mycobacterium abscessus with MALDI-TOF MS in solid and liquid media.

Microbial biotechnology
Mycobacterium abscessus (MABS) displays differential subspecies susceptibility to macrolides. Thus, identifying MABS's subspecies (M. abscessus, M. bolletii and M. massiliense) is a clinical necessity for guiding treatment decisions. We aimed to asse...

Comparing Cadence vs. Machine Learning Based Physical Activity Intensity Classifications: Variations in the Associations of Physical Activity With Mortality.

Scandinavian journal of medicine & science in sports
Step cadence-based and machine-learning (ML) methods have been used to classify physical activity (PA) intensity in health-related research. This study examined the association of intensity-specific PA duration with all-cause (ACM) and CVD mortality ...