activity
20192025
most citedLearning to Identify Patients at Risk of Uncontrolled Hypertension Using Electronic Health Records Data

9 citations · 9 across the 2 of their papers we have counts for

collaborators

5 papers

cs.LG2025

Prediction of Clinical Complication Onset using Neural Point Processes

Sachini Weerasekara, Sagar Kamarthi, Jacqueline Isaacs

Predicting medical events in advance within critical care settings is paramount for patient outcomes and resource management. Utilizing predictive models, healthcare providers can…

stat.ME2024

A Novel Nonlinear Nonparametric Correlation Measurement With A Case Study on Surface Roughness in Finish Turning

Ming Luo, Srinivasan Radhakrishnan, Sagar Kamarthi

Estimating the correlation coefficient has been a daunting work with the increasing complexity of dataset's pattern. One of the problems in manufacturing applications consists of t…

cs.DL2024

pyKCN: A Python Tool for Bridging Scientific Knowledge

Zhenyuan Lu, Wei Li, Burcu Ozek +3

The study of research trends is pivotal for understanding scientific development on specific topics. Traditionally, this involves keyword analysis within scholarly literature, yet…

cs.LG2023

Uncertainty Quantification in Neural-Network Based Pain Intensity Estimation

Burcu Ozek, Zhenyuan Lu, Srinivasan Radhakrishnan +1

Improper pain management can lead to severe physical or mental consequences, including suffering, and an increased risk of opioid dependency. Assessing the presence and severity of…

cs.CY20199 cited

Learning to Identify Patients at Risk of Uncontrolled Hypertension Using Electronic Health Records Data

Ramin Mohammadi, Sarthak Jain, Stephen Agboola +3

Hypertension is a major risk factor for stroke, cardiovascular disease, and end-stage renal disease, and its prevalence is expected to rise dramatically. Effective hypertension man…