AIMC Topic: Machine Learning

Clear Filters Showing 30371 to 30380 of 34417 articles

A review on statistical and machine learning competing risks methods.

Biometrical journal. Biometrische Zeitschrift
When modeling competing risks (CR) survival data, several techniques have been proposed in both the statistical and machine learning literature. State-of-the-art methods have extended classical approaches with more flexible assumptions that can impro...

Understanding New Machine Learning Architectures: Practical Generative Artificial Intelligence for Anesthesiologists.

Anesthesiology
Recent advances in neural networks have given rise to generative artificial intelligence, systems able to produce fluent responses to natural questions or attractive and even photorealistic images from text prompts. These systems were developed throu...

Using Decomposed Error for Reproducing Implicit Understanding of Algorithms.

Evolutionary computation
Reproducibility is important for having confidence in evolutionary machine learning algorithms. Although the focus of reproducibility is usually to recreate an aggregate prediction error score using fixed random seeds, this is not sufficient. Firstly...

Screening of key immunerelated gene in Parkinsons disease based on WGCNA and machine learning.

Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences
OBJECTIVES: Abnormal immune system activation and inflammation are crucial in causing Parkinson's disease. However, we still don't fully understand how certain immune-related genes contribute to the disease's development and progression. This study a...

PXPermute reveals staining importance in multichannel imaging flow cytometry.

Cell reports methods
Imaging flow cytometry (IFC) allows rapid acquisition of numerous single-cell images per second, capturing information from multiple fluorescent channels. However, the traditional process of staining cells with fluorescently labeled conjugated antibo...

Data governance and Gensini score automatic calculation for coronary angiography with deep-learning-based natural language extraction.

Mathematical biosciences and engineering : MBE
With the widespread adoption of electronic health records, the amount of stored medical data has been increasing. Clinical data, often in the form of semi-structured or unstructured electronic medical records (EMRs), contains rich patient information...

[Predicting the risk of becoming eligible for the disability pension: Machine learning methods applied to French health data].

Sante publique (Vandoeuvre-les-Nancy, France)
INTRODUCTION: Benefiting from the disability pension implies morbid (physical and psychological) and social (fall in income) implications for the person. It also has economic consequences for society, with increasing expenses since 2011 (+4.9% on ave...

Automatically pre-screening patients for the rare disease aromatic l-amino acid decarboxylase deficiency using knowledge engineering, natural language processing, and machine learning on a large EHR population.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: Electronic health record (EHR) data may facilitate the identification of rare diseases in patients, such as aromatic l-amino acid decarboxylase deficiency (AADCd), an autosomal recessive disease caused by pathogenic variants in the dopa d...