53 citations · 53 across the 15 of their papers we have counts for
16 papers
GRFBrain: Graph-Structured Rectified Flows for EEG Dynamic Modeling
Haohui Jia, Zheng Chen, Jathurshan Pradeepkumar +4
Forecasting time-varying functional connectivity from electroencephalography (EEG) requires modeling both history-dependent trends and structured variability across channels. Condi…
EpiGraph: Building Generalists for Evidence-Intensive Epilepsy Reasoning in the Wild
Yuyang Dai, Zheng Chen, Jathurshan Pradeepkumar +4
Epilepsy diagnosis and treatment require evidence-intensive reasoning across heterogeneous clinical knowledge, including biosignal patterns, genetic mechanisms, pharmacogenomics, t…
Test-Time Adaptation for EEG Foundation Models: A Systematic Study under Real-World Distribution Shifts
Gabriel Jason Lee, Jathurshan Pradeepkumar, Jimeng Sun
Electroencephalography (EEG) foundation models have shown strong potential for learning generalizable representations from large-scale neural data, yet their clinical deployment is…
How Well Do Multimodal Models Reason on ECG Signals?
Maxwell A. Xu, Harish Haresamudram, Catherine W. Liu +11
While multimodal large language models offer a promising solution to the "black box" nature of health AI by generating interpretable reasoning traces, verifying the validity of the…
ODEBrain: Continuous-Time EEG Graph for Modeling Dynamic Brain Networks
Haohui Jia, Zheng Chen, Lingwei Zhu +6
Modeling neural population dynamics is crucial for foundational neuroscientific research and various clinical applications. Conventional latent variable methods typically model con…
Making Conformal Predictors Robust in Healthcare Settings: a Case Study on EEG Classification
Arjun Chatterjee, Sayeed Sajjad Razin, John Wu +3
Quantifying uncertainty in clinical predictions is critical for high-stakes diagnosis tasks. Conformal prediction offers a principled approach by providing prediction sets with the…
Neural Signals Generate Clinical Notes in the Wild
Jathurshan Pradeepkumar, Zheng Chen, Jimeng Sun
Generating clinical reports that summarize abnormal patterns, diagnostic findings, and clinical interpretations from long-term EEG recordings remains labor-intensive. We present CE…
PyHealth 2.0: A Comprehensive Open-Source Toolkit for Accessible and Reproducible Clinical Deep Learning
John Wu, Yongda Fan, Zhenbang Wu +14
Difficulty replicating baselines, high computational costs, and required domain expertise create persistent barriers to clinical AI research. To address these challenges, we introd…
Prostate-VarBench: A Benchmark with Interpretable TabNet Framework for Prostate Cancer Variant Classification
Abraham Francisco Arellano Tavara, Umesh Kumar, Jathurshan Pradeepkumar +1
Variants of Uncertain Significance (VUS) limit the clinical utility of prostate cancer genomics by delaying diagnosis and therapy when evidence for pathogenicity or benignity is in…
Developing Large Language Models for Clinical Research Using One Million Clinical Trials
Zifeng Wang, Jiacheng Lin, Qiao Jin +5
Developing artificial intelligence (AI) for clinical research requires a comprehensive data foundation that supports model training and rigorous evaluation. Here, we introduce Tria…
Tokenizing Single-Channel EEG with Time-Frequency Motif Learning
Jathurshan Pradeepkumar, Xihao Piao, Zheng Chen +1
Foundation models are reshaping EEG analysis, yet an important problem of EEG tokenization remains a challenge. This paper presents TFM-Tokenizer, a novel tokenization framework th…
Automatically Labeling Clinical Trial Outcomes: A Large-Scale Benchmark for Drug Development
Chufan Gao, Jathurshan Pradeepkumar, Trisha Das +2
Background The cost of drug discovery and development is substantial, with clinical trial outcomes playing a critical role in regulatory approval and patient care. However, access…
Contrastive Deep Encoding Enables Uncertainty-aware Machine-learning-assisted Histopathology
Nirhoshan Sivaroopan, Chamuditha Jayanga, Chalani Ekanayake +6
Deep neural network models can learn clinically relevant features from millions of histopathology images. However generating high-quality annotations to train such models for each…
A Knowledge Distillation Framework For Enhancing Ear-EEG Based Sleep Staging With Scalp-EEG Data
Mithunjha Anandakumar, Jathurshan Pradeepkumar, Simon L. Kappel +2
Sleep plays a crucial role in the well-being of human lives. Traditional sleep studies using Polysomnography are associated with discomfort and often lower sleep quality caused by…
Toward Interpretable Sleep Stage Classification Using Cross-Modal Transformers
Jathurshan Pradeepkumar, Mithunjha Anandakumar, Vinith Kugathasan +4
Accurate sleep stage classification is significant for sleep health assessment. In recent years, several machine-learning based sleep staging algorithms have been developed , and i…
Towards Accurate Cross-Domain In-Bed Human Pose Estimation
Mohamed Afham, Udith Haputhanthri, Jathurshan Pradeepkumar +3
Human behavioral monitoring during sleep is essential for various medical applications. Majority of the contactless human pose estimation algorithms are based on RGB modality, caus…