11 papers
Agentic neural architecture search for clinical prediction from electronic health records: Cross-hospital knowledge transfer and computational efficiency
Deyi Li, Qi Xu, Lingyao Li +3
Transformer architectures for clinical prediction from electronic health records (EHRs) often require costly manual tuning and may vary across hospitals and tasks. We introduce ATH…
CASCADE Conformal Prediction: Uncertainty-Adaptive Prediction Intervals for Two-Stage Clinical Decision Support
Ricardo Diaz-Rincon, Muxuan Liang, Adolfo Ramirez-Zamora +1
Effective medication management in Parkinson's Disease (PD) is challenging due to heterogeneous disease progression, variable patient response, and medication side effects. While A…
A Unified Framework for Structure-Aware Clustering and Heterogeneous Causal Graph Learning
Honglin Du, Muxuan Liang, Xiang Zhong
In complex multivariate systems, interactions among variables are defined by dependency structures, often encoded as directed acyclic graphs (). However, dependency st…
Classification-Powered Conformal Inference for Zero-inflated Outcomes
Zhirui Li, Ricardo Diaz-Rincon, Benjamin Shickel +3
Zero-inflated outcomes, where responses are zero with positive probability and otherwise continuous, are common in biomedical, environmental, and social science studies. We propose…
DT-BEHRT: Disease Trajectory-aware Transformer for Interpretable Patient Representation Learning
Deyi Li, Zijun Yao, Qi Xu +4
The growing adoption of electronic health record (EHR) systems has provided unprecedented opportunities for predictive modeling to guide clinical decision making. Structured EHRs c…
Uncertainty-Aware Prediction of Parkinson's Disease Medication Needs: A Two-Stage Conformal Prediction Approach
Ricardo Diaz-Rincon, Muxuan Liang, Adolfo Ramirez-Zamora +1
Parkinson's Disease (PD) medication management presents unique challenges due to heterogeneous disease progression and treatment response. Neurologists must balance symptom control…
Late Fusion Multi-task Learning for Semiparametric Inference with Nuisance Parameters
Sohom Bhattacharya, Yongzhuo Chen, Muxuan Liang
In the age of large and heterogeneous datasets, the integration of information from diverse sources is essential to improve parameter estimation. Multi-task learning offers a power…
DeepJ: Graph Convolutional Transformers with Differentiable Pooling for Patient Trajectory Modeling
Deyi Li, Zijun Yao, Muxuan Liang +1
In recent years, graph learning has gained significant interest for modeling complex interactions among medical events in structured Electronic Health Record (EHR) data. However, e…
Penalized Linear Models for Highly Correlated High-Dimensional Immunophenotyping Data
Xiaoru Dong, Apoorva Goyal, Muxuan Liang +3
Accurate prediction and identification of variables associated with outcomes or disease states are critical for advancing diagnosis, prognosis, and precision medicine in biomedical…
Estimating optimal tailored active surveillance strategy under interval censoring
Muxuan Liang, Yingqi Zhao, Daniel W. Lin +2
Active surveillance (AS) using repeated biopsies to monitor disease progression has been a popular alternative to immediate surgical intervention in cancer care. However, a biopsy…
Inference with non-differentiable surrogate loss in a general high-dimensional classification framework
Muxuan Liang, Yang Ning, Maureen A Smith +1
Penalized empirical risk minimization with a surrogate loss function is often used to learn a high-dimensional linear decision rule in classification problems. Although much of the…