arXiv AI
Jul 1
Can Physician Expertise Improve Machine Learning Identification of Delirium?
arXiv:2606. 30651v1 Announce Type: cross Abstract: Delirium is common in hospitalized patients and is often missed in routine care.
Risk Stratification for ICU Delirium using Pervasive Ambient Sensing Information
arXiv:2606. 19292v1 Announce Type: new Abstract: Delirium is a common and serious complication in the Intensive Care Unit (ICU), associated with increased morbidity, prolonged hospital stays, and higher healthcare costs.
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arXiv AI
Sep 1
Predicting Future Organ Dysfunction in ICU Patients Using Temporal Convolutional Networks on MIMIC-IV Data
arXiv:2608.29301v1 Announce Type: new Abstract: Predicting future organ dysfunction in Intensive Care Unit (ICU) patients is critical for early clinical intervention, yet existing machine learning ap...
arXiv Machine Learning
Jul 7
OC-Distill: Ontology-aware Contrastive Learning with Cross-Modal Distillation for ICU Risk Prediction
arXiv:2604. 16878v2 Announce Type: replace Abstract: Early prediction of severe clinical deterioration and remaining length of stay can enable timely intervention and better resource allocation in high-acuity settings such as the ICU.
arXiv Machine Learning
Jun 2
Longitudinal Multimodal Sensing of Physical Activity and Well-Being in Older Adults
arXiv:2606. 00345v1 Announce Type: new Abstract: Wearable and mobile sensing technologies enable continuous monitoring of human behavior and health in real-world settings.
arXiv Machine Learning
Aug 12
GARLIC: Graph Attention-based Relational Learning of Multivariate Time Series in Intensive Care
arXiv:2608. 10969v1 Announce Type: new Abstract: Healthcare data, such as Intensive Care Unit (ICU) records, comprise heterogeneous multivariate time series sampled at irregular intervals with pervasive missingness.
arXiv Machine Learning
2d ago
Data-Driven Priors for Uncertainty-Aware Risk Prediction of Clinical Deterioration using Multimodal Data
arXiv:2603.08459v2 Announce Type: replace Abstract: Safe predictions are a crucial requirement for integrating predictive models into clinical decision support systems. One approach to improving trus...
arXiv Machine Learning
Aug 11
A Domain-Structured Ensemble Framework for Perioperative Outcome Prediction Using Electronic Health Record Data
arXiv:2608. 08920v1 Announce Type: new Abstract: Perioperative risk prediction models are often limited by narrow surgical populations, incomplete intraoperative data, poor calibration, and limited interpretability.
arXiv AI
Sep 24
A Leakage-Aware Multimodal Evaluation Framework for Early Intraoperative Acute Kidney Injury Prediction
The study introduces SynerT, a waveform-only hybrid temporal model that uses a causal dilated TCN and dilated recurrent layers to predict early intraoperative acute kidney injury (AKI). Two extensions, SynerT-MM and SynerT-Stack, incorporate hemodynamic summaries, preoperative covariates, and a leakage-safe stacked ensemble to improve discrimination and calibration. Evaluated on the VitalDB database with a strict 60‑minute prediction window, SynerT-Stack achieved the best performance across AUROC, AUPRC, and F1‑max, and demonstrated the greatest net clinical benefit after recalibration.
arXiv Machine Learning
Jul 14
Multimodal Routing for Interpretable, Robust, and Auditable Clinical Prediction
arXiv:2607. 09982v1 Announce Type: new Abstract: Electronic health record (EHR) data are inherently multimodal, and leveraging multiple modalities can improve predictive performance.
arXiv AI
Jun 9
Next-Token Prediction Learns Generalisable Representations of Sleep Physiology
arXiv:2606. 09605v1 Announce Type: new Abstract: Foundation models offer a promising route to compress multi-modal physiological signals into compact representations of human health, with broad applications across sleep medicine, cardiology, neurology and other healthcare domains.
arXiv Machine Learning
Jul 27
Autoregressive EHR Foundation Models with Multimodal Inputs
arXiv:2607. 22264v1 Announce Type: new Abstract: Autoregressive foundation models trained on tokenized electronic health records (EHRs) can support zero-shot clinical prediction, yet most operate on structured event codes alone, and do not incorporate multiple modalities in a principled way.
arXiv Machine Learning
Sep 17
LightSleepX: A Lightweight, Inception-Based Dual-Modal Network for Sleep Staging
LightSleepX is a lightweight, inception‑based dual‑modal network designed for sleep staging in resource‑constrained environments. It uses depthwise separable convolutions, multi‑scale enhanced attention for efficient EEG/EOG feature extraction, and a Mamba encoder for long‑range temporal modeling. On public benchmarks, it achieves 85.9% accuracy on Sleep‑EDF‑20 and 81.8% on ISRUC‑S3 with only 0.049M parameters and 195.9 MFLOPs.