8 citations · 22 across the 16 of their papers we have counts for
17 papers
A Digital Twin for Individualized Treatment Effects of Non-Invasive Respiratory Support Strategies (DINIRS)
Md Fantacher Islam, Jarrod Mosier, Vignesh Subbian
Objective: Choosing between noninvasive respiratory support (NIRS) and invasive mechanical ventilation (IMV) for acute respiratory failure is a time-sensitive decision. Although gu…
Advancing Health Equity through Multi-Level Fairness in Health Informatics
Nick Souligne, Vignesh Subbian
The increasing integration of machine learning in healthcare has highlighted critical challenges related to fairness, transparency, and health equity. Specifically, the use of mult…
FairSelect: A Systematic Evaluation of Multi-Level and Intersectional Algorithmic Fairness
Nick Souligne, Isabella Mixton-Garcia, Vignesh Subbian
Algorithmic fairness methods are increasingly used to identify and mitigate bias in machine learning models, yet most approaches are evaluated in isolation and along single demogra…
FairLogue: A Toolkit for Intersectional Fairness Analysis in Clinical Machine Learning Models
Nick Souligne, Vignesh Subbian
Objective: Algorithmic fairness is essential for equitable and trustworthy machine learning in healthcare. Most fairness tools emphasize single-axis demographic comparisons and may…
Evaluating Intersectional Fairness across Clinical Machine Learning Use Cases using Fairlogue and the All of Us Research Program
Nick Souligne, Vignesh Subbian
Intersectional biases in healthcare data can produce compound disparities in clinical machine learning models, yet most fairness evaluations assess demographic attributes independe…
Characterizing Fungal Infections in the All of Us Research Program
Md Fantacher Islam, Emeral Norzagaray, Corneliu Antonescu +1
Fungal infections, such as coccidioidomycosis, aspergillosis, and histoplasmosis, represent a growing public health concern in the United States. However, the actual burden of thes…
Lightweight Language Models are Prone to Reasoning Errors for Complex Computational Phenotyping Tasks
Sarah Pungitore, Shashank Yadav, David Maughan +1
Although computational phenotyping is a central informatics activity with resulting cohorts supporting a wide variety of applications, it is time-intensive because of manual data r…
Development and Evaluation of an Ontology for Non-Invasive Respiratory Support in Acute Care
Md Fantacher Islam, Jarrod Mosier, Vignesh Subbian
Managing patients with respiratory failure increasingly involves noninvasive respiratory support (NIRS) strategies to support respiration, often preventing the need for invasive me…
Failure Modes of Time Series Interpretability Algorithms for Critical Care Applications and Potential Solutions
Shashank Yadav, Vignesh Subbian
Interpretability plays a vital role in aligning and deploying deep learning models in critical care, especially in constantly evolving conditions that influence patient survival. H…
SHREC: A Framework for Advancing Next-Generation Computational Phenotyping with Large Language Models
Sarah Pungitore, Shashank Yadav, Molly Douglas +2
Computational phenotyping is a central informatics activity with resulting cohorts supporting a wide variety of applications. However, it is time-intensive because of manual data r…
PHEONA: An Evaluation Framework for Large Language Model-based Approaches to Computational Phenotyping
Sarah Pungitore, Shashank Yadav, Vignesh Subbian
Computational phenotyping is essential for biomedical research but often requires significant time and resources, especially since traditional methods typically involve extensive m…
Discovery of Generalizable TBI Phenotypes Using Multivariate Time-Series Clustering
Hamid Ghaderi, Brandon Foreman, Chandan K. Reddy +1
Traumatic Brain Injury (TBI) presents a broad spectrum of clinical presentations and outcomes due to its inherent heterogeneity, leading to diverse recovery trajectories and varied…
Identifying TBI Physiological States by Clustering Multivariate Clinical Time-Series Data
Hamid Ghaderi, Brandon Foreman, Amin Nayebi +3
Determining clinically relevant physiological states from multivariate time series data with missing values is essential for providing appropriate treatment for acute conditions su…
Computable Phenotypes for Post-acute sequelae of SARS-CoV-2: A National COVID Cohort Collaborative Analysis
Sarah Pungitore, Toluwanimi Olorunnisola, Jarrod Mosier +1
Post-acute sequelae of SARS-CoV-2 (PASC) is an increasingly recognized yet incompletely understood public health concern. Several studies have examined various ways to phenotype PA…
A Self-Supervised Learning-based Approach to Clustering Multivariate Time-Series Data with Missing Values (SLAC-Time): An Application to TBI Phenotyping
Hamid Ghaderi, Brandon Foreman, Amin Nayebi +3
Self-supervised learning approaches provide a promising direction for clustering multivariate time-series data. However, real-world time-series data often include missing values, a…
WindowSHAP: An Efficient Framework for Explaining Time-series Classifiers based on Shapley Values
Amin Nayebi, Sindhu Tipirneni, Chandan K Reddy +2
Unpacking and comprehending how black-box machine learning algorithms make decisions has been a persistent challenge for researchers and end-users. Explaining time-series predictiv…
An Empirical Comparison of Explainable Artificial Intelligence Methods for Clinical Data: A Case Study on Traumatic Brain Injury
Amin Nayebi, Sindhu Tipirneni, Brandon Foreman +2
A longstanding challenge surrounding deep learning algorithms is unpacking and understanding how they make their decisions. Explainable Artificial Intelligence (XAI) offers methods…