arXiv Machine Learning

Sparse Functional Singular Value Decomposition for Biclustering and Triclustering Longitudinal Data

arXiv:2606. 05488v1 Announce Type: cross Abstract: Identifying subtypes of complex conditions, such as Inflammatory Bowel Disease (IBD), often requires capturing latent patterns in longitudinal omics data.

arXiv Machine Learning

Aug 18

Group ICA 2.0: Closing the Gap Between Subjects and Group Latent Decomposition with Copula-Linked Group ICA (CoLiG-ICA)

arXiv:2608. 16029v1 Announce Type: new Abstract: Group Independent Component Analysis (gICA) is widely used to decompose high-dimensional functional MRI data into interpretable brain networks.

By Oktay Agcaoglu

safety

arXiv Machine Learning

Jun 3

A Fast Screening Approach for High-dimensional Outcomes and High-dimensional Predictors

arXiv:2606. 03018v1 Announce Type: cross Abstract: Modeling interactions among multimodal, high-dimensional data is intrinsically challenging due to ultra-high dimensionality and complex dependence structure with high level noise.

By Hongju Park, Zhenyao Ye, Shuo Chen

multimodal safety

arXiv Machine Learning

Aug 26

A Structural FHMM for Interpretable Disease Trajectories in T2DM

arXiv:2608.24328v1 Announce Type: new Abstract: In this work, we propose a structural variant of the Factorial Hidden Markov Model (FHMM) for the analysis of disease trajectories in patients with Typ...

By Alessandro Mari, Ekaterina Krymova, Guillaume Obozinski, Maria Luisa Marques de Sa Faquetti, Adrian Martinez de la Torre, Andrea Burden

arXiv AI

Jun 10

LongMoE: Longitudinal Multimodal Learning via Trajectory-Aware Mixture-of-Experts

arXiv:2606. 09907v1 Announce Type: cross Abstract: Multimodal clinical learning is increasingly important for integrating diverse patient data, including imaging, text, and personalised health records.

By Maxx Richard Rahman, Prakhar Kumar, Wolfgang Maass

nlp multimodal

arXiv Machine Learning

Jul 27

Longitudinal Random Forests for Sparse and Irregular Response Trajectories

arXiv:2607. 21817v1 Announce Type: cross Abstract: Longitudinal studies often collect data at sparse, irregular, and unequally spaced time points.

By Yangsheng Wang, Xiaotian Dai, Haoda Fu, Guifang Fu

efficiency

arXiv Machine Learning

Jun 5

PAMF: Prior-Aware Multimodal Fusion for Incomplete Time Series Data

arXiv:2606. 06328v1 Announce Type: new Abstract: In healthcare, multimodal time series tasks often operate on incomplete observations in practice, for example when ECG segments are lost because electrodes detach or an entire respiratory channel is unavailable during overnight monitoring.

By Ziwen Kan, Wugeng Zheng, Tianlong Chen, Song Wang

rag diffusion multimodal benchmarks

arXiv Machine Learning

Aug 4

EHR2Path: Comprehensive Pathway-Level Modeling of Longitudinal Patient Trajectories from Multimodal Electronic Health Records

arXiv:2506. 04831v3 Announce Type: replace Abstract: Forecasting how a patient's condition is likely to evolve, including possible deterioration, recovery, treatment needs, and care transitions, could support more proactive and personalized care, but requires modeling heterogeneous and longitudinal electronic health record (EHR) data.

By Chantal Pellegrini, Ege \"Ozsoy, David Bani-Harouni, Matthias Keicher, Nassir Navab

nlp multimodal

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.

By Nikkie Hooman, Zhongjie Wu, Eric C. Larson, Mehak Gupta

multimodal safety

arXiv Machine Learning

Jul 27

Interpretable EEG biomarkers with bag-of-waves: Spatial and temporal waveform dictionaries for low-data regimes

arXiv:2607. 22508v1 Announce Type: new Abstract: Electroencephalography (EEG) is widely used to diagnose neurological conditions, but its analysis usually relies on either predefined spectral features or deep neural networks.

By Athanasios Papastathopoulos-Katsaros, Steven T. Lee, Lin Yao, Ajay Thomas, Junseok Park, Matthew J. McGinley, Zhandong Liu

benchmarks safety

arXiv Computer Vision

Aug 21

4DLoG: Generative Modeling of Neurodegenerative Brain Anatomy with 4D Longitudinal Diffusion Model

arXiv:2604. 22700v2 Announce Type: replace Abstract: Modeling and predicting neurodegenerative disease progression from medical images remains a major challenge in medical AI, with significant implications for early diagnosis, disease monitoring, and treatment planning.

By Nivetha Jayakumar, Swakshar Deb, Bahram Jafrasteh, Qingyu Zhao, Miaomiao Zhang

diffusion computer-vision benchmarks safety

arXiv Machine Learning

Aug 4

Beyond Random Partitioning: Unsupervised Spatio-Temporal Stratification for Cohort Balancing in Longitudinal Medical Imaging

arXiv:2608. 00073v1 Announce Type: cross Abstract: Rigorous dataset partitioning is a foundational, yet frequently overlooked, prerequisite for reliable deep learning in longitudinal medical imaging.

By Qinghui Liu, Jon Andr\'e Ottesen, Atle Bj{\o}rnerud, Kyrre Eeg Emblem

safety

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...

By Razan Albouq, Asra Aslam

safety

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