Molecular machine learning (ML) underpins critical workflows in drug discovery, material science, and catalyst optimization ...

A novel generative AI framework can predict the need for red blood cell transfusion and mortality risk in ICU patients with ...

Researchers used data from the Diabetes Prevention Program (DPP) randomized controlled trial to develop a risk prediction model for developing incident type 2 diabetes. The model was later ...

Advt Artificial Intelligence (AI) and machine learning ... this model processes glucose data automatically, identifies key patterns, and delivers precise predictions. Speaking about the uniqueness ...

Both models demonstrated good discrimination, with AUCs above 0.8. The machine learning model outperformed the nomogram in terms of precision and specificity, highlighting its potential superiority in ...

The current work proposes a new approach for handling risks based on predictive analytics and machine ... on the machine learning algorithm to drive the risk prediction model in real-time mode. The ...

After data normalization, the machine learning model ... for the predictions. Poverty, use of Illegal drugs, and race are found to be the major predictors of hepatitis in people with diabetes.

the risk prediction model can be revised for any region of Europe that has epidemiological data specific for different ages and sexes. Additionally, while the recalibrated SCORE2-Diabetes model ...

Abstract: A large number of machine ... of data. In this post, a system that uses the Flask API to forecast numerous diseases is proposed. Tensor flow, Flask API, and machine learning techniques were ...

The existing model only looked at diabetes in conjunction with heart disease, and its prediction accuracy is low. Based on this problem, the proposed method employs the machine learning ... data is ...