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Machine Learning - AI Medical Compendium

AIMC Topic: Machine Learning

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A comparative analysis of heterogeneity in lung cancer screening effectiveness in two randomised controlled trials.

Nature communications
Clinical trials demonstrate that screening can reduce lung cancer mortality by over 20%. However, lung cancer screening effectiveness (reduction in lung cancer specific mortality) may vary by personal risk-factors. Here we evaluate heterogeneity in l...

Machine learning-based penetrance of genetic variants.

Science (New York, N.Y.)
Accurate variant penetrance estimation is crucial for precision medicine. We constructed machine learning (ML) models for 10 diseases using 1,347,298 participants with electronic health records, then applied them to an independent cohort with linked ...

Generalizing machine learning models from clinical free text.

Scientific reports
To assess strategies for enhancing the generalizability of healthcare artificial intelligence models, we analyzed the impact of preprocessing approaches applied to medical free text, compared single- versus multiple-institution data models, and evalu...

A Machine Learning Algorithm With an Oversampling Technique in Limited Data Scenarios for the Prediction of Present and Future Restorative Treatment Need: Development and Validation Study.

JMIR medical informatics
BACKGROUND: Untreated dental caries is the most common health condition worldwide. Therefore, new strategies need to be developed to reduce the manifestations of dental caries.

Machine learning driven optimization of compressive strength of 3D printed bio polymer composite material.

PloS one
3D printing has brought significant changes to manufacturing sectors, making it possible to produce intricate, multi-layered designs with greater ease. This study focuses on optimizing the compressive strength (CS) of functionally graded multi-materi...

The critical effects of self-management strategies on predicting cancer survivors' future quality of life and health status using machine learning techniques.

PloS one
Despite the significance of enhancing the quality of life (QoL) and overall health status (including physical, mental, social, and spiritual well-being) among individuals who have survived cancer, the existing prediction model for QoL and health stat...

Fault diagnosis model based on multi-strategy adaptive COA and improved weighted kernel ELM: A case study on wind turbine blade icing.

PloS one
The icing failures of wind turbine blades are critical factors that affect both power generation efficiency and safety. To improve the diagnostic accuracy and speed, an improved weighted kernel extreme learning machine (IWKELM) optimized by multi-str...

Optimizing ensemble machine learning models for accurate liver disease prediction in healthcare.

PloS one
Liver disease encompasses a range of conditions affecting the liver, including hepatitis, cirrhosis, fatty liver, and liver cancer. It can be caused by infections, alcohol abuse, obesity, or genetic factors, and it often progresses silently until adv...

VNC-Dist: A machine learning-based semi-automated pipeline for quantification of neuronal position in the C. elegans ventral nerve cord.

PloS one
The C. elegans ventral nerve cord (VNC) provides a genetically tractable model for investigating the developmental mechanisms involved in neuronal positioning and organization. The VNC of newly hatched larvae contains a set of 22 motoneurons organize...

Machine learning-based identification of diagnostic and prognostic mitotic cell cycle genes in hepatocellular carcinoma.

PloS one
Mitotic cell cycle (MCC) is a critical process in cell growth and division, and dysregulation of MCC genes may contribute to tumorigenesis. In this study, to identify diagnostic and prognostic value of MCC genes, differentially expressed MCC genes be...