AIMC Topic: Machine Learning

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Decision Fusion Model for Predicting Microvascular Invasion in Hepatocellular Carcinoma Based on Multi-MR Habitat Imaging and Machine-Learning Classifiers.

Academic radiology
RATIONALE AND OBJECTIVES: Accurate prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) is crucial for guiding treatment. This study evaluates and compares the performance of clinicoradiologic, traditional radiomics, deep-lear...

Temporomandibular joint CBCT image segmentation via multi-view ensemble learning network.

Medical & biological engineering & computing
Accurate segmentation of the temporomandibular joint (TMJ) from cone beam CT (CBCT) images holds significant clinical value for diagnosing temporomandibular joint osteoarthrosis (TMJOA) and related conditions. Convolutional neural network-based medic...

Subcortical and insula functional connectivity aberrations and clinical implications in first-episode schizophrenia.

Asian journal of psychiatry
INTRODUCTION: Schizophrenia is a complex mental disorder whose pathophysiology remains elusive, particularly in the roles of subcortex. This study aims to explore the role of subcortex and insula and their relationship with symptom changes in first-e...

The state-of-the-art machine learning model for plasma protein binding prediction: Computational modeling with OCHEM and experimental validation.

European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences
Plasma protein binding (PPB) is closely related to pharmacokinetics, pharmacodynamics and drug toxicity. Existing models for predicting PPB often suffer from low prediction accuracy and poor interpretability, especially for high PPB compounds, and ar...

Post-Cardiac arrest outcome prediction using machine learning: A systematic review and meta-analysis.

International journal of medical informatics
BACKGROUND: Early and reliable prognostication in post-cardiac arrest patients remains challenging, with various factors linked to return of spontaneous circulation (ROSC), survival, and neurological results. Machine learning and deep learning models...

Large language models to facilitate pregnancy prediction after in vitro fertilization.

Acta obstetricia et gynecologica Scandinavica
We evaluated the efficacy of large language models (LLMs), specifically, generative pre-trained transformer-4 (GPT-4), in predicting pregnancy following in vitro fertilization (IVF) treatment and compared its accuracy with results from an original pu...

Roller compaction: Measuring ribbon porosity by terahertz spectroscopy and machine learning.

International journal of pharmaceutics
Roller compaction is a crucial unit operation in pharmaceutical manufacturing, with its ribbon porosity widely recognised as a critical quality attribute. Terahertz spectroscopy has emerged as a fast and non-destructive technique to measure porosity ...

Effective detection of indoor fungal contamination through the identification of volatile organic compounds using mass spectrometry and machine learning.

Environmental pollution (Barking, Essex : 1987)
Indoor fungal contamination poses significant challenges to human health and indoor air quality. This study addresses an effective approach using mass spectrometry and machine learning to identify microbial volatile organic compounds (MVOCs) originat...

Using the super-learner to predict the chemical acute toxicity on rats.

Journal of hazardous materials
With the rapid increase in the number of commercial chemicals, testing methods regarding on median lethal dose (LD) relying animal experiments face challenges such as high costs and ethical concerns. Classical quantitative structure-activity relation...

High-throughput prediction of oral acute toxicity in Rat and Mouse of over 100,000 polychlorinated persistent organic pollutants (PC-POPs) by interpretable data fusion-driven machine learning global models.

Journal of hazardous materials
This study utilized available oral acute toxicity data in Rat and Mouse for polychlorinated persistent organic pollutants (PC-POPs) to construct data fusion-driven machine learning (ML) global models. Based on atom-centered fragments (ACFs), the coll...