9 citations · 30 across the 5 of their papers we have counts for
5 papers
Reconstructing Missing EHRs Using Time-Aware Within- and Cross-Visit Information for Septic Shock Early Prediction
Ge Gao, Farzaneh Khoshnevisan, Min Chi
Real-world Electronic Health Records (EHRs) are often plagued by a high rate of missing data. In our EHRs, for example, the missing rates can be as high as 90% for some features, w…
An Adversarial Domain Separation Framework for Septic Shock Early Prediction Across EHR Systems
Farzaneh Khoshnevisan, Min Chi
Modeling patient disease progression using Electronic Health Records (EHRs) is critical to assist clinical decision making. While most of prior work has mainly focused on developin…
Improving Robustness on Seasonality-Heavy Multivariate Time Series Anomaly Detection
Farzaneh Khoshnevisan, Zhewen Fan, Vitor R. Carvalho
Robust Anomaly Detection (AD) on time series data is a key component for monitoring many complex modern systems. These systems typically generate high-dimensional time series that…
RSM-GAN: A Convolutional Recurrent GAN for Anomaly Detection in Contaminated Seasonal Multivariate Time Series
Farzaneh Khoshnevisan, Zhewen Fan
Robust anomaly detection is a requirement for monitoring complex modern systems with applications such as cyber-security, fraud prevention, and maintenance. These systems generate…
Your Actions or Your Associates? Predicting Certification and Dropout in MOOCs with Behavioral and Social Features
Niki Gitinabard, Farzaneh Khoshnevisan, Collin F. Lynch +1
The high level of attrition and low rate of certification in Massive Open Online Courses (MOOCs) has prompted a great deal of research. Prior researchers have focused on predicting…