AIMC Topic: Machine Learning

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Diffusion-/perfusion-weighted imaging fusion to automatically identify stroke within 4.5 h.

European radiology
OBJECTIVES: We aimed to develop machine learning (ML) models based on diffusion- and perfusion-weighted imaging fusion (DP fusion) for identifying stroke within 4.5 h, to compare them with DWI- and/or PWI-based ML models, and to construct an automati...

Machine Learning Radiomics-Based Prediction of Non-sentinel Lymph Node Metastasis in Chinese Breast Cancer Patients with 1-2 Positive Sentinel Lymph Nodes: A Multicenter Study.

Academic radiology
RATIONALE AND OBJECTIVES: This study aimed to construct a machine learning radiomics-based model using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) images to evaluate non-sentinel lymph node (NSLN) metastasis in Chinese breast cance...

Accuracy of machine learning in the preoperative identification of ovarian borderline tumors: a meta-analysis.

Clinical radiology
AIM: The objective of this study is to explore the diagnostic value of machine learning (ML) in borderline ovarian tumors through meta-analysis.

Understanding COVID-19 infection among people with intellectual and developmental disabilities using machine learning.

Disability and health journal
BACKGROUND: People with intellectual and developmental disabilities (IDD) were disproportionately affected by the COVID-19 pandemic. Predicting COVID-19 infection has been difficult.

Models for predicting coffee yield from chemical characteristics of soil and leaves using machine learning.

Journal of the science of food and agriculture
BACKGROUND: Coffee farming constitutes a substantial economic resource, representing a source of income for several countries due to the high consumption of coffee worldwide. Precise management of coffee crops involves collecting crop attributes (cha...

Modeling failures in smart grids by a bilinear logistic regression approach.

Neural networks : the official journal of the International Neural Network Society
Modeling and recognizing events in complex systems through machine learning techniques is a challenging task. Especially if the model is constrained to be explainable and interpretable, while ensuring high levels of accuracy. In this paper, we adopt ...

Multimodal active subspace analysis for computing assessment oriented subspaces from neuroimaging data.

Journal of neuroscience methods
BACKGROUND: For successful biomarker discovery, it is essential to develop computational frameworks that summarize high-dimensional neuroimaging data in terms of involved sub-systems of the brain, while also revealing underlying heterogeneous functio...

Exploring Chemical Reaction Space with Machine Learning Models: Representation and Feature Perspective.

Journal of chemical information and modeling
Chemical reactions serve as foundational building blocks for organic chemistry and drug design. In the era of large AI models, data-driven approaches have emerged to innovate the design of novel reactions, optimize existing ones for higher yields, an...