arXiv:2606. 07606v1 Announce Type: new Abstract: Very low birth weight infants (VLBWI) are at high risk of mortality and severe neurodevelopmental impairment, including cerebral palsy, yet reliable discharge-time prognostic stratification remains challenging in high-dimensional and data-limited clinical settings.
arXiv:2606. 16337v1 Announce Type: new Abstract: Predictive modeling for clinical tabular data is central to clinical decision support and therefore requires not only strong predictive performance but also transparent decision logic.
The paper introduces the first federated learning framework for infant movement analysis, specifically targeting General Movement Assessment using skeletal motion data. It employs Monte Carlo Dropout to estimate predictive uncertainty and proposes an Uncertainty-Aware Federated Averaging (UA‑FedAvg) strategy that weights client updates by this uncertainty. Experiments with three clients show that federated learning outperforms local models and approaches centralized training performance, with UA‑FedAvg generally surpassing standard FedAvg.
The study presents a real‑time musculoskeletal surrogate for children with cerebral palsy, built from OpenSim parameters, joint kinematics, and muscle capacities. Using leave‑one‑subject‑out validation on nine pediatric gait recordings, the surrogate reproduces musculotendon lengths with high accuracy (R² ≈ 0.92–0.95, nRMSE < 8%) and achieves sub‑millisecond inference times, well below the 100 ms interactive‑rehabilitation target. A Monte Carlo credibility pilot reveals that small variations in anthropometry and muscle capacity lead to overconfident 90 % prediction intervals, highlighting the need for improved force modeling and uncertainty quantification.
arXiv:2605. 00647v2 Announce Type: replace Abstract: Automated pediatric electrocardiogram (ECG) interpretation remains challenging because developmental differences in heart rate, intervals, and waveforms limit the transferability of models trained mainly on adult data, while expert-labeled pediatric ECG cohorts are scarce.
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:2609.09189v1 Announce Type: cross Abstract: High classification accuracy alone is insufficient for clinical image analysis, where calibrated confidence and reliable uncertainty estimates are es...
arXiv:2606. 19183v1 Announce Type: cross Abstract: Large language models (LLMs) can make clinical decision support more accessible by interpreting free-text documentation, but their direct use as diagnostic engines is limited by sensitivity to prompts, information order, and plausible but incorrect outputs.
The paper introduces REMIND, a clustering‑based keypoint‑selection method that uses training dynamics to detect and filter noisy annotations in 2D pose estimation for preterm infants. Applied to the NeoPose dataset of 46 clinical videos, REMIND achieves up to 93% AUC across three pose‑estimation architectures, demonstrating robust learning without prior noise assumptions. This work is the first to explicitly tackle label noise in neonatal pose estimation, enabling more reliable monitoring of infant motor development in real clinical settings.
arXiv:2607. 02553v1 Announce Type: cross Abstract: Introduction: Objective neuroimaging biomarkers may improve Parkinson's disease motor assessment by capturing brain variation not directly observable from clinical examination.
The paper introduces a four-step pipeline that mines decision rules in the latent space of an FT-Transformer and then translates those rules back into measurable clinical features. By treating embedding dimensions that separate patient groups as latent biomarkers, small decision trees are used to extract rules, which are then mapped to raw features using gradient-input saliency and CLS attention attribution. Across six public clinical datasets, the translated rules generally outperformed raw-feature rules, achieving significant AUROC gains, though some high-performing latent rules could not be fully captured by simple raw-feature conditions.
arXiv:2508.04899v3 Announce Type: replace Abstract: Reliable evaluation of machine learning models for neonatal seizure detection is critical for clinical adoption. Current practices often rely on in...
arXiv:2606. 25434v1 Announce Type: new Abstract: Early and scalable detection of mild cognitive impairment (MCI) remains an unresolved clinical challenge.