21 citations · 39 across the 24 of their papers we have counts for
27 papers
ClinGuard: a governance framework for reliable, transparent, and auditable clinical coordination
Kaiyuan Wu, Aditya Nagori, Nicoleta J. Economou +2
ClinGuard is a model-agnostic governance framework that treats clinical coordination as patient-scoped, policy-bound transactions rather than free text. An independent sentinel lay…
A doctrine-grounded visual question answering dataset for Tactical Combat Casualty Care
Junseob Kim, Jade Chng, Ayman Ali +5
Tactical Combat Casualty Care (TC3) requires responders to connect visual observations of injuries and interventions with established clinical guidance. Developing vision-language…
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…
Learning a Continuous Sepsis Severity Score Without Hour-by-Hour Supervision: A Two-Site Retrospective Study
Kevin Zhu, Ryan Zhang, Baraa Abed +18
Currently used sepsis severity indices rely on fixed variables and weights established decades ago, which are coarsely discretized and calibrated to a cohort that no longer reflect…
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…
Planner-Auditor Twin: Agentic Discharge Planning with FHIR-Based LLM Planning, Guideline Recall, Optional Caching and Self-Improvement
Kaiyuan Wu, Aditya Nagori, Rishikesan Kamaleswaran
Objective: Large language models (LLMs) show promise for clinical discharge planning, but their use is constrained by hallucination, omissions, and miscalibrated confidence. We int…
SpikGPT: A High-Accuracy and Interpretable Spiking Attention Framework for Single-Cell Annotation
Min Huang, Rishikesan Kamaleswaran
Accurate and scalable cell type annotation remains a challenge in single-cell transcriptomics, especially when datasets exhibit strong batch effects or contain previously unseen ce…
Performance of Large Language Models in Answering Critical Care Medicine Questions
Mahmoud Alwakeel, Aditya Nagori, An-Kwok Ian Wong +3
Large Language Models have been tested on medical student-level questions, but their performance in specialized fields like Critical Care Medicine (CCM) is less explored. This stud…
Open-Source Agentic Hybrid RAG Framework for Scientific Literature Review
Aditya Nagori, Ricardo Accorsi Casonatto, Ayush Gautam +2
The surge in scientific publications challenges traditional review methods, demanding tools that integrate structured metadata with full-text analysis. Hybrid Retrieval Augmented G…
Evaluating LLMs in Medicine: A Call for Rigor, Transparency
Mahmoud Alwakeel, Aditya Nagori, Vijay Krishnamoorthy +1
Objectives: To evaluate the current limitations of large language models (LLMs) in medical question answering, focusing on the quality of datasets used for their evaluation. Materi…
Contextual Phenotyping of Pediatric Sepsis Cohort Using Large Language Models
Aditya Nagori, Ayush Gautam, Matthew O. Wiens +6
Clustering patient subgroups is essential for personalized care and efficient resource use. Traditional clustering methods struggle with high-dimensional, heterogeneous healthcare…
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…
Deep Representation Learning-Based Dynamic Trajectory Phenotyping for Acute Respiratory Failure in Medical Intensive Care Units
Alan Wu, Tilendra Choudhary, Pulakesh Upadhyaya +3
Sepsis-induced acute respiratory failure (ARF) is a serious complication with a poor prognosis. This paper presents a deep representation learningbased phenotyping method to identi…
Social Media as a Sensor: Analyzing Twitter Data for Breast Cancer Medication Effects Using Natural Language Processing
Seibi Kobara, Alireza Rafiei, Masoud Nateghi +3
Breast cancer is a significant public health concern and is the leading cause of cancer-related deaths among women. Despite advances in breast cancer treatments, medication non-adh…
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…
Detecting algorithmic bias in medical-AI models using trees
Jeffrey Smith, Andre Holder, Rishikesan Kamaleswaran +1
With the growing prevalence of machine learning and artificial intelligence-based medical decision support systems, it is equally important to ensure that these systems provide pat…
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…
Online Critical-State Detection of Sepsis Among ICU Patients using Jensen-Shannon Divergence
Jeffrey R. Smith, Yao Xie, Christopher S. Josef +1
Sepsis is a severe medical condition caused by a dysregulated host response to infection that has a high incidence and mortality rate. Even with such a high-level occurrence rate,…