21 citations · 39 across the 24 of their papers we have counts for
12 papers · 1 filter
Intervention Granularity Matters: Coherent Treatment Bundles in Counterfactual Simulation with Clinical World Models
Fangzhou Wang, Yixuan Yang, Camilla Balzarotti +1
Counterfactual simulation with a clinical world model means fixing a patient's history, changing the treatment, and reading off the predicted response. Doing so requires deciding w…
Clin-JEPA: A Multi-Phase Co-Training Framework for Joint-Embedding Predictive Pretraining on EHR Patient Trajectories
Yixuan Yang, Mehak Arora, Ryan Zhang +10
Joint-embedding predictive architectures (JEPA) learn representations by predicting in latent space, as in computer vision; retaining the action-conditioned predictor at inference…
NeuroSep-CP-LCB: A Deep Learning-based Contextual Multi-armed Bandit Algorithm with Uncertainty Quantification for Early Sepsis Prediction
Anni Zhou, Raheem Beyah, Rishikesan Kamaleswaran
In critical care settings, timely and accurate predictions can significantly impact patient outcomes, especially for conditions like sepsis, where early intervention is crucial. We…
Sepsyn-OLCP: An Online Learning-based Framework for Early Sepsis Prediction with Uncertainty Quantification using Conformal Prediction
Anni Zhou, Beyah Raheem, Rishikesan Kamaleswaran +1
Sepsis is a life-threatening syndrome with high morbidity and mortality in hospitals. Early prediction of sepsis plays a crucial role in facilitating early interventions for septic…
Robust Meta-Model for Predicting the Need for Blood Transfusion in Non-traumatic ICU Patients
Alireza Rafiei, Ronald Moore, Tilendra Choudhary +6
Objective: Blood transfusions, crucial in managing anemia and coagulopathy in ICU settings, require accurate prediction for effective resource allocation and patient risk assessmen…
Improving Clinical Decision Support through Interpretable Machine Learning and Error Handling in Electronic Health Records
Mehak Arora, Hassan Mortagy, Nathan Dwarshuis +5
The objective of this work is to develop an Electronic Medical Record (EMR) data processing tool that confers clinical context to Machine Learning (ML) algorithms for error handlin…
Meta-learning in healthcare: A survey
Alireza Rafiei, Ronald Moore, Sina Jahromi +2
As a subset of machine learning, meta-learning, or learning to learn, aims at improving the model's capabilities by employing prior knowledge and experience. A meta-learning paradi…
Transfer Learning for Causal Effect Estimation
Song Wei, Hanyu Zhang, Ronald Moore +2
We present a Transfer Causal Learning (TCL) framework when target and source domains share the same covariate/feature spaces, aiming to improve causal effect estimation accuracy in…
Causal Graph Discovery from Self and Mutually Exciting Time Series
Song Wei, Yao Xie, Christopher S. Josef +1
We present a generalized linear structural causal model, coupled with a novel data-adaptive linear regularization, to recover causal directed acyclic graphs (DAGs) from time series…
ALRt: An Active Learning Framework for Irregularly Sampled Temporal Data
Ronald Moore, Rishikesan Kamaleswaran
Sepsis is a deadly condition affecting many patients in the hospital. Recent studies have shown that patients diagnosed with sepsis have significant mortality and morbidity, result…
UnfoldML: Cost-Aware and Uncertainty-Based Dynamic 2D Prediction for Multi-Stage Classification
Yanbo Xu, Alind Khare, Glenn Matlin +4
Machine Learning (ML) research has focused on maximizing the accuracy of predictive tasks. ML models, however, are increasingly more complex, resource intensive, and costlier to de…
Causal Graph Discovery from Self and Mutually Exciting Time Series
Song Wei, Yao Xie, Christopher S. Josef +1
We present a generalized linear structural causal model, coupled with a novel data-adaptive linear regularization, to recover causal directed acyclic graphs (DAGs) from time series…