AIMC Topic: Machine Learning

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Differential impact of CD34+ cell dose for different age groups in allogeneic hematopoietic cell transplantation for acute leukemia: a machine learning-based discovery.

Experimental hematology
Allogeneic hematopoietic cell transplantation (allo-HCT) presents a potentially curative treatment for hematologic malignancies yet carries associated risks and complications. Continuous research focuses on predicting outcomes and identifying risk fa...

Effect-directed analysis of genotoxicants in food packaging based on HPTLC fractionation, bioassays, and toxicity prediction with machine learning.

Analytical and bioanalytical chemistry
Many chemicals in food packaging can leach as complex mixtures to food, potentially including substances hazardous to consumer health. Detecting and identifying all of the leachable chemicals are impractical with current analytical instrumentation an...

A comprehensive machine learning-based models for predicting mixture toxicity of azole fungicides toward algae (Auxenochlorella pyrenoidosa).

Environment international
Quantitative structure-activity relationships (QSARs) have been used to predict mixture toxicity. However, current research faces gaps in achieving accurate predictions of the mixture toxicity of azole fungicides. To address this gap, the application...

Detecting Honey Adulteration: Advanced Approach Using UF-GC Coupled with Machine Learning.

Sensors (Basel, Switzerland)
This article introduces a novel approach to detecting honey adulteration by combining ultra-fast gas chromatography (UF-GC) with advanced machine learning techniques. Machine learning models, particularly support vector regression (SVR) and least abs...

Deep graph clustering via aligning representation learning.

Neural networks : the official journal of the International Neural Network Society
Deep graph clustering is a fundamental yet challenging task for graph data analysis. Recent efforts have witnessed significant success in combining autoencoder and graph convolutional network to explore graph-structured data. However, we observe that...

Statistics of Generative Artificial Intelligence and Nongenerative Predictive Analytics Machine Learning in Medicine.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc
The rapidly evolving landscape of artificial intelligence (AI) and machine learning (ML) in medicine has prompted medical professionals to increasingly familiarize themselves with related topics. This also demands grasping the underlying statistical ...

Using machine learning to predict soil lead relative bioavailability.

Journal of hazardous materials
Although the relative bioavailability (RBA) can be applied to assess the effects of Pb on human health, there is no definition and no specific data of Pb-RBA to different soil sources and endpoints in vivo. In this study, we estimated the Pb-RBA from...

Predictive Factors Driving Positive Awake Test in Carotid Endarterectomy Using Machine Learning.

Annals of vascular surgery
BACKGROUND: Positive neurologic awake testing during the carotid cross-clamping may be present in around 8% of patients undergoing carotid endarterectomy (CEA). The present work aimed to assess the accuracy of an artificial intelligence (AI)-powered ...

Effect of a Machine Learning-Derived Early Warning Tool With Treatment Protocol on Hypotension During Cardiac Surgery and ICU Stay: The Hypotension Prediction 2 (HYPE-2) Randomized Clinical Trial.

Critical care medicine
OBJECTIVES: Cardiac surgery is associated with perioperative complications, some of which might be attributable to hypotension. The Hypotension Prediction Index (HPI), a machine-learning-derived early warning tool for hypotension, has only been evalu...