Random Hazard Forests (RHF) is a survival tree ensemble that models how a patient's hazard changes over continuous time as new measurements arrive. RHF directly estimates a nonparametric hazard likelihood for predictable covariate processes, using an efficient working model to guide tree construction and then estimating flexible time‑varying hazards at each terminal node. By routing each tree based on the covariate state immediately before each time point, RHF can handle irregular and asynchronous covariate updates, and averaging across trees yields a pathwise hazard estimate that accurately captures changing risk in simulations and an intensive‑care application.
By Hemant Ishwaran, Eileen M. Hsich, Udaya B. Kogalur, Donald K. K. Lee
DeMMO is an interpretable framework that models longitudinal digital mobility outcomes (DMOs) across multiple diseases and outcomes using multi-task learning. It introduces a cross-disease, cross-outcome relation-learning mechanism that learns signed relationships from longitudinal DMO coefficient matrices, allowing selective information sharing even when disease cohorts lack shared participants. Evaluated on the Mobilise‑D dataset, DeMMO outperforms nine strong baselines and identifies reliable longitudinal DMO patterns for clinical validation.
By Menghui Zhou, Zhipeng Yuan, Vitaveska Lanfranchi, Po Yang
The paper introduces a new algorithm that uses decision trees and random forests to estimate individual treatment effects while providing interpretability. It modifies the standard random forest splitting criterion by combining a heterogeneity-focused criterion with a bias-correction criterion, enabling the model to handle observational studies with varying treatment propensities without separately estimating propensity scores. The resulting tree structure directly reveals which features drive treatment effect differences, and simulation studies show the method matches or surpasses existing approaches in prediction accuracy while improving interpretability.
By Nicolas Alexander Ihlo, Merle Behr
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
The paper introduces Neural ODE-LMM, a hybrid model that integrates a Neural Ordinary Differential Equation into a linear mixed‑effects framework to flexibly learn time‑varying associations between exposures and outcomes. By encoding covariate trajectories into a continuous‑time latent state, the method preserves standard likelihood inference while capturing complex, potentially cumulative effects without pre‑specifying functional forms. Simulations demonstrate accurate recovery of instantaneous and cumulative effects, and application to the 3C cohort uncovers BMI and fasting glucose trajectories linked to cognitive decline.
By Zhe Aurore Li, Quentin Clairon, C\'ecilia Samieri, Rodolphe Thi\'ebaut, M\'elanie Prague, C\'ecile Proust-Lima
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.
By Yue Zhao, Thierry Chekouo, Sandra Safo
arXiv:2606. 00345v1 Announce Type: new Abstract: Wearable and mobile sensing technologies enable continuous monitoring of human behavior and health in real-world settings.
By Flavio Di Martino, Mattia G. Campana, Marcello Magno, Lorenza Pratali, Franca Delmastro
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
The paper introduces PAFIR, a Personalized and Adaptive Feature selection framework that treats feature selection as a reinforcement learning problem over longitudinal multimodal health data. PAFIR jointly models structural dependencies among assessment variables and temporal dynamics in wearable-derived physical activity data, learning adaptive selection policies across repeated study visits using reward signals from sparse fall incidence outcomes. Applied to the Physio Feedback Exercise Program (PEER) trial, PAFIR outperforms state‑of‑the‑art baselines by capturing longitudinal and structural patterns of feature relevance, enabling dynamic, subject‑specific feature selection for more timely fall prevention strategies.
By Chang Liu, Ladda Thiamwong, Yanjie Fu, Rui Xie
MultiSigBERT is a unified framework that performs multimodal sequential survival modeling in oncology by integrating narrative clinical reports, numerical measurements, and structured variables. The method converts free-text reports into sentence embeddings, compresses them with modality-specific PCA, and concatenates them with structured covariates to create joint temporal trajectories. These trajectories are encoded using the Signature transform from Rough Paths theory, and the resulting high-dimensional features are fed into a LASSO-regularized Cox model, achieving a concordance index of 0.743 on an independent test set of over 2,500 patients.
By Paul Minchella, St\'ephane Chr\'etien, Guillaume Metzler, Lo\"ic Verlingue, R\'emi Vaucher
arXiv:2608. 06430v1 Announce Type: new Abstract: Learning from Electronic Health Records (EHRs) has gained significant attention due to its potential to improve clinical prediction.
By Anirudh Rayas, Yuan Wang, Pavan Turaga
PGP-Clinical-TimeKAN is a trajectory-first framework for joint probabilistic forecasting of multivariate physiological data, combining missingness-aware temporal encoders, a soft organ-system prior, patient-specific relations, nonlinear Kolmogorov‑Arnold messages, and a low‑rank multivariate Student‑t head. Evaluated on a MIMIC‑IV cohort of 6,882 patients, it achieves the second‑lowest normalized MAE and the lowest RMSE among 13 models, while providing calibrated probabilistic forecasts with empirical coverage at 50%, 80%, and 95% intervals. Ablation studies show that relational structure is critical for performance, and increasing covariance rank improves likelihood but not point accuracy.
whyItMatters":"The model demonstrates that joint trajectory forecasting can yield highly accurate, calibrated predictions of physiological trajectories, offering a potentially inspectable intermediate task for clinical deterioration prediction."
By Weizhi Nie, Rihao Chang, Weijie Wang, Yuting Su