AIMC Topic: Machine Learning

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Machine learning driven biomarker selection for medical diagnosis.

PloS one
Recent advances in experimental methods have enabled researchers to collect data on thousands of analytes simultaneously. This has led to correlational studies that associated molecular measurements with diseases such as Alzheimer's, Liver, and Gastr...

Evaluating large language models for information extraction from gastroscopy and colonoscopy reports through multi-strategy prompting.

Journal of biomedical informatics
OBJECTIVE: To systematically evaluate large language models (LLMs) for automated information extraction from gastroscopy and colonoscopy reports through prompt engineering, addressing their ability to extract structured information, recognize complex...

Unveiling Hidden Health Risks: Machine Learning Enhanced Modeling of Plastic Additive Release Kinetics in Fresh Produce Packaging.

Environmental science & technology
Fresh produce packaging (FPP) plays a critical role in protecting fruits and vegetables from various environmental factors. However, the presence, migration, and human health risks of additives in FPP have received limited attention. This study inves...

High-Throughput Analysis of Protein Adsorption to a Large Library of Polymers Using Liquid Extraction Surface Analysis-Tandem Mass Spectrometry (LESA-MS/MS).

Analytical chemistry
Biomaterials play an important role in medicine from contact lenses to joint replacements. High-throughput screening coupled with machine learning has identified synthetic polymers that prevent bacterial biofilm formation, prevent fungal cell attachm...

Automated classification of seizure onset pattern using intracranial electroencephalogram signal of non-human primates.

Physiological measurement
To develop and validate a machine learning framework for the classification of distinct seizure onset patterns using intracranial EEG (iEEG) recordings in a non-human primate (NHP) model of penicillin-induced seizures.iEEG data were collected from si...

What does evolution make? Learning in living lineages and machines.

Trends in genetics : TIG
How does genomic information unfold, to give rise to self-constructing living organisms with problem-solving capacities at all levels of organization? We review recent progress that unifies work in developmental genetics and machine learning (ML) to ...

Explainable one-class feature extraction by adaptive resonance for anomaly detection in quality assurance.

PloS one
In this study, we address the inherent challenges in radiotherapy (RT) plan quality assessment (QA). RT, a prevalent cancer treatment, utilizes high-energy beams to target tumors while sparing adjacent healthy tissues. Typically, an RT plan is refine...

Decoding PM oxidative potential in Ningbo, China: Key chemicals, sources, and health risks via dual-assay and machine learning.

Journal of hazardous materials
PM oxidative potential (OP), a key driver of health risks, was investigated in Ningbo, China, using dual dithiothreitol (DTT) and ascorbic acid (AA) assays combined with machine learning (ML). This approach accounts for the complexity of interactions...

Machine learning and the labor market: A portrait of occupational and worker inequities in Canada.

Social science & medicine (1982)
INTRODUCTION: Machine learning (ML), an artificial intelligence (AI) subfield, is increasingly used by Canadian workplaces. Concerningly, the impact of ML may be inequitable and contribute to social and health inequities in the working population. Th...

The performance of machine learning models in predicting postpartum depression: a meta-analysis and systematic review.

Journal of reproductive and infant psychology
AIM: To evaluate the effectiveness of machine learning (ML) approaches in predicting individuals with postpartum depression (PPD), this study systematically reviewed and meta-analysed existing evidence.