Publications by authors named "Mingxuan Ju"
Article Synopsis
- The study aimed to create a predictive model to identify BPH patients likely to respond to medical treatment and those who won't.
- Researchers analyzed data from 2,172 BPH patients treated with three different therapies, employing machine learning techniques to select the most effective model.
- The final boosted support vector machine model achieved an overall accuracy (AUC) of 0.698, indicating a reasonable ability to distinguish between treatment responders and failures across different treatment groups.
α-Satellite: An AI-Driven System and Benchmark Datasets for Dynamic COVID-19 Risk Assessment in the United States.
IEEE J Biomed Health Inform
October 2020
The fast evolving and deadly outbreak of coronavirus disease (COVID-19) has posed grand challenges to human society. To slow the spread of virus infections and better respond for community mitigation, by advancing capabilities of artificial intelligence (AI) and leveraging the large-scale and up-to-date data generated from heterogeneous sources (e.g.
View Article and Find Full Text PDFObjective: To develop and externally validate a prediction model for anticholinergic response in patients with overactive bladder (OAB).
Methods: A machine learning model to predict the likelihood of anticholinergic treatment failure was constructed using a retrospective data set (n=559) of female patients with OAB who were treated with anticholinergic medications between January 2010 and December 2017. Treatment failure was defined as less than 50% improvement in frequency, urgency, incontinence episodes, and nocturia, and the patient's subjective impression of symptomatic relief.