Rochester Institute of Technology

RIT researchers use machine learning to better understand the pathways of disease

RIT computing students and Professor Rui Li are working on a National Institutes of Health-funded project to use AI in ...
San Diego Reader

Rethinking Machine Learning: Stuart Frost Makes the Case for Causal AI in Manufacturing

Causal AI transforms key areas of manufacturing by revealing what happens and why. In predictive maintenance, it shifts focus ...
Law

Training Alone Is Not Enough: Lessons From 'Recentive' and USPTO AI Examples on Patent Eligible Machine-Learning Claims

The Recentive decision exemplifies the Federal Circuit’s skepticism toward claims that dress up longstanding business problems in machine-learning garb, while the USPTO’s examples confirm that ...
eLife

Comparing the outputs of intramural and extramural grants funded by National Institutes of Health

This important study used five metrics to compare the cost-effectiveness of intramural and extramural research funded by the National Institutes of Health in the United States between 2009 and 2019.
IEEE

Optimizing Logistic Regression Model for Heart Disease Prediction with Machine Learning

Abstract: Nowadays, heart disease is a serious medical issue, and diagnosis is crucial to providing the proper treatment to the patient. Hence, machine learning (ML)-based early diagnosis of heart ...
IEEE

Predicting Medical Claim Denial Using Logistic Regression and Decision Tree Algorithm

Abstract: This study utilizes decision tree and logistic regression models to explore the factors contributing to medical claim denials and identify areas for improvement. We adapt undersampling ...
RMD Open

Systemic lupus erythematosus damage risk index (SLE-DRI): a simple machine learning-based tool for identifying patients at risk for early organ damage

Objective Organ damage is a key determinant of poor prognosis and increased mortality in systemic lupus erythematosus (SLE).