Machine learning model improves accuracy of liquid biopsy results

A machine learning model developed by researchers at the Johns Hopkins Kimmel Cancer Center filters out the biological noise ...
News-Medical.Net

Revolutionizing calibration: How Lumo uses machine learning to streamline E&L screening and toxicological risk assessment

Lumo leverages advanced machine learning to reduce calibration time, and flag low-confidence response factor predictions.
The Lancet

Deep learning for H&E-based meningioma molecular classification and outcome prediction: a retrospective cohort study

To our knowledge, this study is the first to apply deep learning models that can, beyond diagnosis, identify molecular subtypes and predict outcomes in a single brain tumour entity (meningioma) using ...
Neurology Advisor

Multimodal Deep Learning Model Improves CT-Based Alzheimer Disease Classification

A multimodal deep learning framework trained on paired CT and MRI data demonstrated improved diagnostic accuracy when classifying patients with Alzheimer disease, mild cognitive impairment, or normal ...
Devdiscourse

Banks could strengthen credit card fraud screening with ensemble machine learning model

Read more about Banks could strengthen credit card fraud screening with ensemble machine learning model on Devdiscourse ...
News Medical

AI-driven model predicts type 1 diabetes risk with greater accuracy

Study: Genetic association and machine learning improve the prediction of type 1 diabetes risk. Image credit: sasirin pamai/Shutterstock.com Researchers performed genetic association analysis and ...
IEEE

Power Quality Disturbance Classification Assessment Using Logistic Regression Algorithm

Abstract: Voltage sag, swell, harmonics, and transients are power quality (PQ) disturbances that damage modern electrical apparatus. This paper presents an effective classification approach using ...
IEEE

Fuzzy LASSO Logistic Regression: A Regularized Framework for Interpretable Binary Classification Under Uncertainty

Abstract: Fuzzy classification models are important for handling uncertainty and heterogeneity in high-dimensional data. Although recent fuzzy logistic regression approaches have demonstrated ...
Scientific Research Publishing

Shipe, M.E., Deppen, S.A., Farjah, F. and Grogan, E.L. (2019) Developing Prediction Models for Clinical Use Using Logistic Regression: An Overview. Journal of Thoracic Disease ...

ABSTRACT: Bipolar disorder is a multifaceted psychiatric illness characterized by unpredictable mood episodes and highly variable treatment responses across individuals. Predicting response to ...