11 papers
ICare-DT: a digital twin for continuous forecasting of intensive care patients
Yanke Li, Yahang Qi, Yumi Kim +1
Digital twins of intensive care patients should update forecasts from new data and predict treatment responses, but few studies test whether routine records support either. We prop…
Causal graph fusion across incomplete cohorts and causal steering of tabular foundation models
Yanke Li, Dhiyaa Al Jorf, Ayoube Ighissou +3
Clinical data are split across incomplete cohorts with partially overlapping variables. Causal discovery under missing data and multi-source integration have developed separately,…
Monocular markerless biomechanics for clinically interpretable gait assessment in spinal cord injury
Shreyasvi Natraj, Mathieu Ruepp, Yanke Li +3
Three-dimensional gait analysis guides rehabilitation after spinal cord injury but depends on marker-based motion capture and force plates, which few clinics have. Monocular marker…
GARLIC: Graph Attention-based Relational Learning of Multivariate Time Series in Intensive Care
Ruirui Wang, Yanke Li, Manuel Günther +1
Healthcare data, such as Intensive Care Unit (ICU) records, comprise heterogeneous multivariate time series sampled at irregular intervals with pervasive missingness. However, clin…
ECGLight: Compute-Light Framework For Paper ECG Digitization and Myocardial Infarction Screening
Shreyasvi Natraj, Cyrus Achtari, Felice Gragnano +3
Electrocardiography (ECG) is one of the most widely used tests for diagnosing cardiovascular disease. Yet several remote clinics still utilize paper ECG printouts for their analysi…
On the Role of Inductive Bias in Time-Series Pretraining: A Case Study in Learning Generalizable Representations for Clinical Time Series
Sharmita Dey, Diego Paez-Granados
Clinical time-series learning is routinely constrained by small, heterogeneous cohorts and protocol drift, while its downstream use spans both classification (e.g., pathology diagn…
A learning health system in Neurorehabilitation as a foundation for multimodal patient representation
Thomas Weikert, Eljas Roellin, Lukas Heumos +3
Neurological disorders represent a growing global health burden requiring long-term, interdisciplinary rehabilitation. Computational neurorehabilitation (compNR) - the use of data-…
FedSCS-XGB -- Federated Server-centric surrogate XGBoost for continual health monitoring
Felix Walger, Mehdi Ejtehadi, Anke Schmeink +1
Wearable sensors with local data processing can detect health threats early, enhance documentation, and support personalized therapy. In the context of spinal cord injury (SCI), wh…
KarmaTS: A Universal Simulation Platform for Multivariate Time Series with Functional Causal Dynamics
Haixin Li, Yanke Li, Diego Paez-Granados
We introduce KarmaTS, an interactive framework for constructing lag-indexed, executable spatiotemporal causal graphical models for multivariate time series (MTS) simulation. Motiva…
Detection of Autonomic Dysreflexia in Individuals With Spinal Cord Injury Using Multimodal Wearable Sensors
Bertram Fuchs, Mehdi Ejtehadi, Ana Cisnal +5
Autonomic Dysreflexia (AD) is a potentially life-threatening condition characterized by sudden, severe blood pressure (BP) spikes in individuals with spinal cord injury (SCI). Earl…
Embedding computational neurorehabilitation in clinical practice using a modular intelligent health system
Thomas Weikert, Eljas Roellin, Monica Pérez-Serrano +5
A significant and rising proportion of the global population suffer from non-communicable diseases, such as neurological disorders. Neurorehabilitation aims to restore function and…