Author

Nicholas Konz

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Preprint Oct 2026

Sign-Constrained Intervention Effects for Domain-Generalizable ICU World Models

Predicting how a patient's vital signs respond to an intervention is a central question in intensive care. World models can do so by learning dynamics as a function of prior actions. However, such models tend to be brittle outside of training data. Clinicians choose drug dosages based on the patient's state, so the ass...

Zheng Xu, Nicholas Konz, Zhen Tan et al. · 0 citations

#machine learning Preprint Oct 2026

Aligning Multimodal Patient Evidence with Biomedical Knowledge Graphs for Clinical LLMs

Clinical questions often depend on linking a patient's multimodal evidence to external biomedical knowledge, yet existing predictive systems rarely represent such links explicitly, so they can neither be traced to their evidence sources nor removed to measure their contributions. We present MM-KG (Multimodal Knowledge...

Jia-Wen Du, Arshan Ali Khan, Chen-Hao Zhang et al. · 0 citations

Preprint Aug 2026

Represent, Then Generate: Multimodal-Conditioned Time-Series Generation under Irregular Missingness

Continuous physiological time series underpin modern clinical monitoring, yet many of the most informative signals are invasive, expensive, or simply unavailable for a given patient. Conditional generation offers a remedy: an absent signal can be synthesized from co-recorded signals and routine clinical variables. Exis...

H. Zhang, Jia-Heng Guo, Yu-Chao Huang et al. · 0 citations

#artificial intelligence Preprint Oct 2026

On the Divergence of Accuracy and Mechanism Consistency in Time Series World Models

A time series world model (TSWM) predicts a controlled system's state from its observed history and planned actions and exogenous inputs. Current approaches build forecasters with actions as covariates, trained and evaluated on prediction error under the executed plan. Yet world models compare unexecuted plans, but the...

H. Zhang, Jia-Heng Guo, Zheng Xu et al. · 0 citations

#artificial intelligence Preprint Sep 2026

No Scale Left Behind: Multi-Scale Autoencoder with Bi-directional Attention for Time Series Anomaly Detection

This work proposes Multi-Scale Autoencoder with Cross-Scale Attention for TSAD (MSCAD), a simple yet powerful semi-supervised TSAD framework founded on parallel autoencoder branches corresponding to different patch sizes.

Jia-Heng Guo, H. Zhang, Yu-Chao Huang et al. · 0 citations