医学 AI - arXivDaily 专题

2511.05221 2026-06-18 cs.LG q-bio.NC 版本更新 95%

ActiTect: A Generalizable Machine Learning Pipeline for REM Sleep Behavior Disorder Screening through Standardized Actigraphy

ActiTect：通过标准化体动记录进行REM睡眠行为障碍筛查的通用机器学习流程

David Bertram, Anja Ophey, Sinah Röttgen, Konstantin Kufer, Gereon R. Fink, Elke Kalbe, Clint Hansen, Walter Maetzler, Maximilian Kapsecker, Lara M. Reimer, Stephan Jonas, Andreas T. Damgaard, Natasha B. Bertelsen, Casper Skjaerbaek, Per Borghammer, Karolien Groenewald, Pietro-Luca Ratti, Michele T. Hu, Noémie Moreau, Michael Sommerauer, Katarzyna Bozek

发表机构 * Faculty of Mathematics and Natural Sciences, University of Cologne, Germany（科隆大学数学与自然科学学院，德国）； Institute for Biomedical Informatics, Faculty of Medicine and University Hospital Cologne, University of Cologne, Germany（科隆大学医学院与科隆大学医院生物医学信息学研究所，德国）； Center for Molecular Medicine Cologne (CMMC), Faculty of Medicine and University Hospital Cologne, University of Cologne, Germany（科隆分子医学中心（CMMC），科隆大学医学院与科隆大学医院，德国）； Medical Psychology | Neuropsychology and Gender Studies, Faculty of Medicine and University Hospital Cologne, University of Cologne, Germany（科隆大学医学院与科隆大学医院医学心理学 | 神经心理学与性别研究，德国）； Cognitive Neuroscience, Insitute for Neuroscience and Medicine, INM-3, Research Center Juelich, Germany（认知神经科学，神经科学与医学研究所，Juelich研究中心，德国）； Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Germany（科隆大学医学院与科隆大学医院神经科，德国）； Center of Neurology, Department of Parkinson, Sleep and Movement Disorders, University Hospital Bonn, University of Bonn, Germany（神经科中心，帕金森、睡眠与运动障碍部门，波恩大学医院，德国）； German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany（德国神经退行性疾病研究中心（DZNE），波恩，德国）； Cluster of Excellence for Aging and Aging-Associated Diseases (CECAD), University of Cologne, Germany（老龄化与相关疾病卓越中心（CECAD），科隆大学，德国）； Department of Neurology, University Medical Center Schleswig-Holstein, Campus Kiel and Kiel University, Germany（神经科，施普伦德-霍斯特大学医院，基尔校区和基尔大学，德国）； Department of Informatics, Technical University of Munich, Germany（信息学院，慕尼黑技术大学，德国）； Institute for Digital Medicine, University Hospital Bonn, Germany（数字医学研究所，波恩大学医院，德国）； Lundbeck Foundation Parkinson’s Disease Research Center (PACE), Aarhus University, Denmark（路德维希基金会帕金森病研究中心（PACE），奥胡斯大学，丹麦）； Department of Nuclear Medicine, Aarhus University Hospital, Denmark（核医学部，奥胡斯大学医院，丹麦）； Department of Electrical and Computer Engineering, Aarhus University, Denmark（电气与计算机工程系，奥胡斯大学，丹麦）； Oxford Parkinson’s Disease Centre and Division of Neurology, Nuffield Department of Clinical Neurosciences, University of Oxford, UK（牛津帕金森病中心与神经科，牛津大学临床神经科学系，英国）

专题命中诊断辅助：通过体动记录筛查REM睡眠行为障碍，属于诊断辅助。

AI总结提出ActiTect，一个全自动开源机器学习工具，通过标准化预处理和睡眠-觉醒检测，从体动记录中识别RBD，在多个独立队列中验证了泛化能力（AUROC 0.84-0.94）。

Comments 37 pages including Supplementary Information, 4 core figures, 1 supplementary figure. (v2: fixed a typo in Table 3 and made minor text edits; v3: post review)

Journal ref npj Digital Medicine (2026)

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DOI: 10.1038/s41746-026-02738-8

AI中文摘要

孤立性快速眼动睡眠行为障碍（iRBD）是α-突触核蛋白病的主要前驱标志，通常先于帕金森病、路易体痴呆或多系统萎缩的临床发作。虽然腕戴式体动记录仪通过捕捉异常夜间运动在大规模筛查中具有检测RBD的巨大潜力，但缺乏可靠高效的分析流程则无法使用。本研究提出了ActiTect，一个全自动开源机器学习工具，用于从体动记录中识别RBD。为确保跨异构采集设置的泛化能力，我们的流程包括稳健的预处理和自动睡眠-觉醒检测，以协调多设备数据并提取表征活动模式的生理可解释运动特征。模型开发基于78名个体的队列，在嵌套交叉验证下表现出强大的区分能力（AUROC = 0.95）。在盲法本地测试集（n = 31，AUROC = 0.86）和两个独立外部队列（n = 113，AUROC = 0.84；n = 57，AUROC = 0.94）上验证了泛化性。为评估现实世界鲁棒性，跨内部和外部队列的留一数据集交叉验证显示出一致的性能（AUROC范围 = 0.84-0.89）。补充稳定性分析表明，关键预测特征在数据集中保持可重复性，支持最终合并的多中心模型作为更广泛部署的稳健预训练资源。通过开源且易于使用，我们的工具促进了广泛采用，并促进了独立验证和协作改进，从而推动该领域向使用可穿戴设备的统一且可泛化的RBD检测模型发展。

英文摘要

Isolated rapid eye movement sleep behavior disorder (iRBD) is a major prodromal marker of $α$-synucleinopathies, often preceding the clinical onset of Parkinson's disease, dementia with Lewy bodies, or multiple system atrophy. While wrist-worn actimeters hold significant potential for detecting RBD in large-scale screening efforts by capturing abnormal nocturnal movements, they become inoperable without a reliable and efficient analysis pipeline. This study presents ActiTect, a fully automated, open-source machine learning tool to identify RBD from actigraphy recordings. To ensure generalizability across heterogeneous acquisition settings, our pipeline includes robust preprocessing and automated sleep-wake detection to harmonize multi-device data and extract physiologically interpretable motion features characterizing activity patterns. Model development was conducted on a cohort of 78 individuals, yielding strong discrimination under nested cross-validation (AUROC = 0.95). Generalization was confirmed on a blinded local test set (n = 31, AUROC = 0.86) and on two independent external cohorts (n = 113, AUROC = 0.84; n = 57, AUROC = 0.94). To assess real-world robustness, leave-one-dataset-out cross-validation across the internal and external cohorts demonstrated consistent performance (AUROC range = 0.84-0.89). A complementary stability analysis showed that key predictive features remained reproducible across datasets, supporting the final pooled multi-center model as a robust pre-trained resource for broader deployment. By being open-source and easy to use, our tool promotes widespread adoption and facilitates independent validation and collaborative improvements, thereby advancing the field toward a unified and generalizable RBD detection model using wearable devices.

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2605.21528 2026-06-18 cs.LG cs.AI 版本更新 85%

A Reproducible Log-Driven AutoML Framework for Interpretable Pipeline Optimization in Healthcare Risk Prediction

可重复的基于日志的自动机器学习框架用于医疗风险预测中的可解释流水线优化

Rui Huang, Lican Huang

发表机构 * School of Basic Medicine, Hangzhou Normal University（杭州师范大学基础医学院）； Research Department, Hangzhou Domain Zones Technology Co.Ltd.（杭州域区技术有限公司）

专题命中诊断辅助：AutoML框架用于医疗风险预测，属于诊断辅助。

AI总结本文提出了一种可重复的基于日志的自动机器学习框架，用于医疗风险预测中的可解释流水线优化，通过分析组件属性、交互和冗余性，提高了模型性能和稳定性。

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AI中文摘要

准确且可重复的疾病风险预测仍然具有挑战性，由于异质特征、有限样本和严重的类别不平衡。本研究引入了yvsoucom-iterkit，一种确定性和基于日志的自动化机器学习框架，将流水线优化完全可重复地建模为配置级系统。每个流水线被编码为可追溯的日志实体，使能够分析组件属性、交互、相似性和跨种子鲁棒性。在超过18,000个流水线配置上对Pima Indians糖尿病和中风数据集的实验揭示了一个结构化且部分冗余的搜索空间，其中性能由一小部分相互作用的组件决定。随机森林重要性分析显示，增强（0.454）、模型选择（0.198）和不平衡处理（0.101）是Pima数据集的关键驱动因素，而不平衡处理主导中风（0.406）。组件相似性分析显示强冗余性，特征选择变体（biMax-biMean）表现出低RMS距离（0.0252），混合匹配无增强（0.0279），TomekLinks与无不平衡处理对齐（0.0325），而高斯噪声与无增强的差异更大（0.10）。该框架使用集成模型（加权F1 0.89，宏F1 0.88在Pima；加权F1 0.94在中风）实现了强且稳定的性能，而宏F1在中风上较低（0.67）由于类别不平衡。跨种子分析揭示了性能-鲁棒性权衡，集成模型的变异性低于SVM。这些结果表明，有效的AutoML优化可以聚焦于一组高影响的组件。

英文摘要

Accurate disease risk prediction is challenged by heterogeneous features, limited data, and class imbalance. This study presents yvsoucom-iterkit, a deterministic AutoML framework that models pipeline optimization as a configuration-level system with full reproducibility and traceable execution logs, enabling systematic analysis of component attribution, interactions, similarity, and cross-seed robustness. Experiments on the Pima Indians Diabetes and Stroke datasets across more than 18,000 pipeline configurations reveal a structured yet partially redundant search space, where performance is dominated by a small subset of interacting components. Ensemble models achieve stable performance, reaching a Weighted-F1 of 0.89 on Pima and 0.94 on Stroke. Macro-F1 reaches approximately 0.88 on Pima but drops to 0.6560 on Stroke due to severe imbalance. Cross-seed experiments show that ensembles reduce variance compared to single models. Friedman testing ($p < 0.05$) confirms significant ranking differences across configurations. Based on analysis of component attribution, interaction, and similarity, optimal configuration design reveals dataset-dependent behavior. For the Pima dataset, computational efficiency benefits from simplified search spaces where redundant components can be removed, with split ratio playing a key role. In contrast, the Stroke dataset requires enhanced imbalance-aware strategies, where RandomOverSampler improves Macro-F1 from 0.6560 to 0.6766. These findings demonstrate that effective AutoML optimization is achieved through optimal configuration design, where carefully constraining the search space to high-impact components can improve performance, stability, and interpretability while reducing unnecessary search complexity.

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2603.15988 2026-06-18 eess.AS cs.AI cs.LG 版本更新 85%

Something from Nothing: Data Augmentation for Robust Severity Level Estimation of Dysarthric Speech

无中生有：面向构音障碍语音严重程度鲁棒估计的数据增强

Jaesung Bae, Xiuwen Zheng, Minje Kim, Chang D. Yoo, Mark Hasegawa-Johnson

发表机构 * 1 University of Illinois Urbana-Champaign, IL, USA 2 Korea Advanced Institute of Science \& Technology, KR

专题命中诊断辅助：构音障碍语音质量评估，用于临床诊断

AI总结提出三阶段框架，利用未标注构音障碍语音和典型语音数据集，通过教师模型生成伪标签、标签感知对比学习预训练和微调，在五个未见数据集上平均SRCC达0.761，显著优于现有方法。

Comments Accepted to Interspeech 2026 Long Paper Track

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AI中文摘要

构音障碍语音质量评估（DSQA）对于临床诊断和包容性语音技术至关重要。然而，主观评估成本高且难以规模化，而标注数据的稀缺限制了鲁棒的客观建模。为解决这一问题，我们提出了一个三阶段框架，利用未标注的构音障碍语音和大规模典型语音数据集来扩展训练。教师模型首先生成未标注样本的伪标签，然后使用标签感知对比学习策略进行弱监督预训练，使模型暴露于多样化的说话者和声学条件。预训练模型随后针对下游DSQA任务进行微调。在跨越多种病因和语言的五个未见数据集上的实验证明了我们方法的鲁棒性。我们的基于Whisper的基线显著优于SOTA DSQA预测器（如SpICE），完整框架在未见测试数据集上实现了平均SRCC为0.761。

英文摘要

Dysarthric speech quality assessment (DSQA) is critical for clinical diagnostics and inclusive speech technologies. However, subjective evaluation is costly and difficult to scale, and the scarcity of labeled data limits robust objective modeling. To address this, we propose a three-stage framework that leverages unlabeled dysarthric speech and large-scale typical speech datasets to scale training. A teacher model first generates pseudo-labels for unlabeled samples, followed by weakly supervised pretraining using a label-aware contrastive learning strategy that exposes the model to diverse speakers and acoustic conditions. The pretrained model is then fine-tuned for the downstream DSQA task. Experiments on five unseen datasets spanning multiple etiologies and languages demonstrate the robustness of our approach. Our Whisper-based baseline significantly outperforms SOTA DSQA predictors such as SpICE, and the full framework achieves an average SRCC of 0.761 across unseen test datasets.

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2509.14183 2026-06-18 stat.ME stat.AP 版本更新 70%

Index Date Imputation for Survival Analysis in Externally Controlled Trials with Delayed Treatment Initiation

延迟治疗启动的外部对照试验中生存分析的索引日期插补

Q. Le Coent, G. L. Rosner, M-C. Wang, C. Hu

专题命中诊断辅助：外部对照试验中索引日期插补方法

AI总结针对外部对照试验中因治疗启动延迟导致的索引日期错位问题，提出截断感知的索引日期插补（IDI）方法，结合倾向得分加权以校正混杂，模拟和真实数据验证其减少偏差的有效性。

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AI中文摘要

外部对照试验将单臂试验的结果与从历史试验、注册或观察性研究中抽取的外部对照进行比较。对于时间至事件终点，一个关键挑战是单臂试验中的随访以治疗启动为索引，而外部对照数据以更早的临床里程碑（如诊断或复发）为索引。这种错位可能引入永存时间偏倚，扭曲风险集，并复杂化生存比较的解释。我们提出索引日期插补（IDI），一种截断感知的方法，用于在延迟治疗启动的设置中为外部对照患者插补可比较的索引日期。IDI估计目标单臂人群中治疗启动时间的边际分布，同时考虑到启动时间仅在存活足够长以启动治疗的患者中观察到。然后使用插补的索引日期来对齐随访，并在外部对照队列中强制实施可比较的截断条件。由于仅时间对齐不能解决人群水平的混杂，IDI与倾向得分加权或匹配相结合，以改善队列之间的协变量可比性。我们通过蒙特卡洛模拟研究评估所提出方法的有限样本性能。使用来自一项随机肿瘤试验的数据，我们模拟了一个具有诱导索引日期错位的外部对照分析，并显示IDI减少了与随机试验基准的差异。IDI为涉及延迟治疗启动的生存分析中的索引日期对齐提供了一种实用策略，并且在有合适外部对照可用时，可以与标准的协变量调整方法集成。

英文摘要

Externally controlled trials compare outcomes from a single-arm trial with external controls drawn from historical trials, registries, or observational studies. For time-to-event endpoints, a key challenge arises when follow-up is indexed at treatment initiation in the single-arm trial, but the external-control data are indexed at an earlier clinical milestone, such as diagnosis or relapse. This misalignment can induce immortal time bias, distort risk sets, and complicate the interpretation of survival comparisons. We propose Index Date Imputation (IDI), a truncation-aware approach for imputing comparable index dates for external-control patients in settings with delayed treatment initiation. IDI estimates the marginal distribution of treatment-initiation times in the target single-arm population while accounting for the fact that initiation times are observed only among patients who survive long enough to initiate treatment. The imputed index dates are then used to align follow-up and enforce comparable truncation conditions in the external-control cohort. Because temporal alignment alone does not address population-level confounding, IDI is combined with propensity score weighting or matching to improve covariate comparability between cohorts. We evaluate the finite-sample performance of the proposed approach through Monte Carlo simulation studies. Using data from a randomized oncology trial, we emulate an externally controlled analysis with induced index-date misalignment and show that IDI reduces discrepancy from the randomized trial benchmark. IDI provides a practical strategy for index-date alignment in survival analyses involving delayed treatment initiation and can be integrated with standard covariate-adjustment methods when suitable external controls are available.

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