AIMC Topic: Machine Learning

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Trustworthy assertion classification through prompting.

Journal of biomedical informatics
Accurate identification of the presence, absence or possibility of relevant entities in clinical notes is important for healthcare professionals to quickly understand crucial clinical information. This introduces the task of assertion classification ...

Image Classification Method Based on Multi-Agent Reinforcement Learning for Defects Detection for Casting.

Sensors (Basel, Switzerland)
A casting image classification method based on multi-agent reinforcement learning is proposed in this paper to solve the problem of casting defects detection. To reduce the detection time, each agent observes only a small part of the image and can mo...

Gene function prediction in five model eukaryotes exclusively based on gene relative location through machine learning.

Scientific reports
The function of most genes is unknown. The best results in automated function prediction are obtained with machine learning-based methods that combine multiple data sources, typically sequence derived features, protein structure and interaction data....

Temporal quality degradation in AI models.

Scientific reports
As AI models continue to advance into many real-life applications, their ability to maintain reliable quality over time becomes increasingly important. The principal challenge in this task stems from the very nature of current machine learning models...

Applications of artificial intelligence multiomics in precision oncology.

Journal of cancer research and clinical oncology
Cancer is the second leading worldwide disease that depends on oncogenic mutations and non-mutated genes for survival. Recent advancements in next-generation sequencing (NGS) have transformed the health care sector with big data and machine learning ...

The Prediction of Sinter Drums Strength Using Hybrid Machine Learning Algorithms.

Computational intelligence and neuroscience
The prediction model with the sinter drum strength as the evaluation index was established based on the index data and historical sintering data generated during the sintering process. The regression prediction model in the algorithm of machine learn...

Predicting poor glycemic control during Ramadan among non-fasting patients with diabetes using artificial intelligence based machine learning models.

Diabetes research and clinical practice
AIMS: This study aims to predict poor glycemic control during Ramadan among non-fasting patients with diabetes using machine learning models.

Visualization of a Machine Learning Framework toward Highly Sensitive Qualitative Analysis by SERS.

Analytical chemistry
Surface-enhanced Raman spectroscopy (SERS), providing near-single-molecule-level fingerprint information, is a powerful tool for the trace analysis of a target in a complicated matrix and is especially facilitated by the development of modern machine...

Machine Learning-Enabled Prediction of 3D-Printed Microneedle Features.

Biosensors
Microneedles (MNs) introduced a novel injection alternative to conventional needles, offering a decreased administration pain and phobia along with more efficient transdermal and intradermal drug delivery/sample collecting. 3D printing methods have e...

Use of machine learning to identify patients at risk of sub-optimal adherence: study based on real-world data from 10,929 children using a connected auto-injector device.

BMC medical informatics and decision making
BACKGROUND: Our aim was to develop a machine learning model, using real-world data captured from a connected auto-injector device and from early indicators from the first 3 months of treatment, to predict sub-optimal adherence to recombinant human gr...