AIMC Topic: Machine Learning

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Optimizing Strategy for Lung Cancer Screening: From Risk Prediction to Clinical Decision Support.

JCO clinical cancer informatics
PURPOSE: Low-dose computed tomography (LDCT) screening is effective in reducing lung cancer mortality by detecting the disease at earlier, more treatable stages. However, high false-positive rates and the associated risks of subsequent invasive diagn...

An innovative machine learning approach for slope stability prediction by combining shap interpretability and stacking ensemble learning.

Environmental science and pollution research international
Accurate slope stability prediction is crucial for mitigating slope failures, but conventional methods are challenging due to their complexity and high data requirements. To overcome these limitations, researchers have used machine learning (ML) tech...

Explainable Machine Learning Models for Colorectal Cancer Prediction Using Clinical Laboratory Data.

Cancer control : journal of the Moffitt Cancer Center
IntroductionEarly diagnosis of colorectal cancer (CRC) poses a significant clinical challenge. This study aims to develop machine learning (ML) models for CRC risk prediction using clinical laboratory data.MethodsThis retrospective, single-center stu...

Performance evaluation of machine learning techniques in surface morphology and corrosion prediction for A286 3D printed micro-lattice structures.

PloS one
The development of lightweight, corrosion-resistant metallic lattice structures has gained significant attention in aerospace, defense, and structural applications, where material durability and weight optimization are critical. This study investigat...

CLAAF: Multimodal fake information detection based on contrastive learning and adaptive Agg-modality fusion.

PloS one
The widespread disinformation on social media platforms has created significant challenges in verifying the authenticity of content, especially in multimodal contexts. However, simple modality fusion can introduce much noise due to the differences in...

Knowledge-guided adaptive spatial-temporal graph contrastive learning framework: Regional crop diseases prediction based on electronic medical records.

Neural networks : the official journal of the International Neural Network Society
The occurrence of crop diseases exhibits nonlinear and dynamic spatial-temporal correlations. How to realize real-time and accurate regional disease prediction is an emerging challenge in smart agriculture. Existing research is hindered by difficulti...

Machine Learning Models Can Predict Tinnitus and Noise-Induced Hearing Loss.

Ear and hearing
OBJECTIVES: Despite the extensive use of machine learning (ML) models in health sciences for outcome prediction and condition classification, their application in differentiating various types of auditory disorders remains limited. This study aimed t...

Synergistic effects of environmental factors on benthic diversity: Machine learning analysis.

Water research
This study examines the water environmental factors of the Cangshan stream and benthic animal communities by using random forest, gradient boosting decision tree, and support vector machine models to analyze the complex response mechanisms of benthic...

Uncovering key factors in weight loss effectiveness through machine learning.

International journal of obesity (2005)
BACKGROUND/OBJECTIVES: One of the main challenges in weight loss is the dramatic interindividual variability in response to treatment. We aim to systematically identify factors relevant to weight loss effectiveness using machine learning (ML).

Navigating the Fragrance Space Using Graph Generative Models and Predicting Odors.

Journal of chemical information and modeling
We explore a suite of generative modeling techniques to efficiently navigate and explore the complex landscapes of odor and the broader chemical space. Unlike traditional approaches, we not only generate molecules but also predict the odor likeliness...