Machine learning with applications最新文献_第3页

A new framework for input variable selection based on the gamma test machine learning performance in quantile prediction of flow duration curves 基于流量持续曲线分位数预测中伽马测试机器学习性能的输入变量选择新框架

IF 4.9

Machine learning with applications

Pub Date : 2026-01-09 DOI: 10.1016/j.mlwa.2026.100839

Arezoo Shafiei Bafti , Mehdi Vafakhah , Vahid Moosavi , Hadi Khosravi Farsani

Predicting streamflow in ungauged watersheds is a key hydrological challenge, commonly addressed through flow duration curve (FDC) regionalization. Although machine learning (ML) models are widely applied, their accuracy depends critically on both the algorithm and input variable selection. This research develops a systematic, quantile-aware ML framework to assess how input selection strategies affect FDC prediction. We evaluate three Gamma Test–based approaches: full variable set, classified variables, and expert opinion, combined with five ML techniques: Adaptive Neuro-Fuzzy Inference System (ANFIS), Support Vector Regression (SVR), Multivariate Adaptive Regression Splines (MARS), Random Forest (RF), and Boosted Regression Trees (BRT). The analysis uses data from 130 hydrometric stations across the Caspian Sea watershed. Results demonstrated that predictive performance varies not only by model but also significantly with flow quantile and input strategy. The ANFIS model enhanced with Fuzzy C-Means clustering (FCM) consistently delivered the highest accuracy. Specifically, low, medium and high flows were best predicted using the full variable set (Q90, R² = 0.94, improved by 623 %), the classified variable and expert opinion approaches (Q50, R² = 0.86, improved by 207.14 %; Q2, R² = 0.86, improved by 207.14 %), respectively. This confirms that no single ML configuration is optimal for all conditions, underscoring the necessity of flow-regime-specific variable selection for robust FDC regionalization in data-scarce areas. Accordingly, for similar watersheds, we recommend the following configurations of the ANFIS-FCM model: the full variable set for low-flow prediction, the classified variable approach for medium-flow prediction, and the expert opinion approach for high-flow prediction.

预测未测量流域的流量是一个关键的水文挑战，通常通过流量持续曲线（FDC）区划来解决。虽然机器学习（ML）模型被广泛应用，但其准确性主要取决于算法和输入变量的选择。本研究开发了一个系统的、分位数感知的机器学习框架来评估输入选择策略如何影响FDC预测。我们评估了三种基于Gamma测试的方法：全变量集、分类变量和专家意见，结合五种ML技术：自适应神经模糊推理系统（ANFIS）、支持向量回归（SVR）、多元自适应回归样条（MARS）、随机森林（RF）和增强回归树（BRT）。该分析使用了里海流域130个水文观测站的数据。结果表明，预测性能不仅受模型的影响，而且受流量分位数和输入策略的影响显著。经模糊c均值聚类（FCM）增强的ANFIS模型始终具有最高的准确率。具体而言，使用全变量集（Q90, R²= 0.94，提高了623%）、分类变量和专家意见方法（Q50, R²= 0.86，提高了207.14%;Q2, R²= 0.86，提高了207.14%）分别对低、中、高流量进行了最佳预测。这证实了没有单一的机器学习配置对所有条件都是最优的，强调了在数据稀缺地区为稳健的FDC区域化选择特定于流动状态的变量的必要性。因此，对于相似的流域，我们建议采用以下配置的anfiss - fcm模型：小流量预测采用全变量集，中流量预测采用分类变量法，大流量预测采用专家意见法。

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引用次数: 0

Conformalized classifiers with reject option 具有拒绝选项的符合化分类器

IF 4.9

Machine learning with applications

Pub Date : 2026-01-06 DOI: 10.1016/j.mlwa.2026.100838

Ulf Johansson, Cecilia Sönströd

In data-driven decision support, predictive models built using machine learning aid in making informed decisions. In this context, models with a reject option may refrain from making predictions for certain instances. Accurately assessing the trade-off between predictive performance and throughput requires the ability to estimate performance at different rejection levels in advance. In this paper, we demonstrate how conformal prediction can be used for this purpose. Under exchangeability, the proposed conformalized classifiers can perfectly estimate accuracy or precision for any rejection level. In an empirical investigation using 41 publicly available datasets, the conformalized classifiers with a reject option are shown to clearly outperform probabilistic predictors calibrated with state-of-the-art techniques.

在数据驱动的决策支持中，使用机器学习建立的预测模型有助于做出明智的决策。在这种情况下，带有拒绝选项的模型可能会避免对某些实例进行预测。准确评估预测性能和吞吐量之间的权衡需要能够提前估计不同拒绝级别下的性能。在本文中，我们演示了如何将保形预测用于此目的。在互换性下，所提出的符合化分类器可以很好地估计任何拒绝水平的准确度或精度。在使用41个公开可用数据集的实证调查中，具有拒绝选项的符合化分类器被证明明显优于使用最先进技术校准的概率预测器。

引用次数: 0

Oncology data extraction with large language models from real-world breast cancer electronic health records in Spanish 从西班牙语的真实乳腺癌电子健康记录中使用大型语言模型提取肿瘤数据

IF 4.9

Machine learning with applications

Pub Date : 2026-01-05 DOI: 10.1016/j.mlwa.2026.100837

Julio Montes-Torres , Francisco J. Moreno-Barea , Leonardo Franco , Nuria Ribelles , Emilio Alba , José M. Jerez

The integration of Artificial Intelligence (AI) in healthcare systems has the potential to significantly enhance patient care and streamline clinical processes. This research investigates the utilisation of generative AI and large language models (LLMs) for oncological information extraction (IE) from Spanish real electronic health records (EHRs) to enhance clinical decision-making and research. We conducted a comparative analysis of GPT-4.5 and 11 state-of-the-art, locally executable LLM-based chatbots, including Llama 3.2, Mistral-Small 3.2, and Phi-4, to extract specific clinical entities from real EHR narratives. Our evaluation workflow aimed to assess the performance of these models in contexts with computational constraints, specifically targeting the extraction of breast cancer prognostic factors. Initial findings indicate that while open-source LLM models are improving, they are not yet equivalent to human specialists in terms of Named Entity Recognition (NER) accuracy. The language of the clinical records notably influences performance, revealing that smaller models particularly struggle with Spanish text. However, with careful model selection and output post-processing, Mistral-Small 3.2 achieved a detection F1 score of over 74.7% for critical TNM information. This study highlights significant potential for generative AI in clinical IE but underscores the need for ongoing improvements, particularly in handling linguistic diversity. Locally managed open source models are still far from performing like a human specialist, but addressing common model shortcomings can facilitate the integration of AI-driven solutions into public healthcare systems, thereby improving patient outcomes and fostering efficient data utilisation.

人工智能（AI）在医疗保健系统中的集成有可能显著提高患者护理和简化临床流程。本研究探讨了利用生成式人工智能和大型语言模型（llm）从西班牙真实电子健康记录（EHRs）中提取肿瘤信息（IE），以增强临床决策和研究。我们对GPT-4.5和11个最先进的、本地可执行的基于llm的聊天机器人（包括Llama 3.2、Mistral-Small 3.2和Phi-4）进行了比较分析，以从真实的电子病历叙述中提取特定的临床实体。我们的评估工作流程旨在评估这些模型在计算限制情况下的性能，特别是针对乳腺癌预后因素的提取。最初的研究结果表明，虽然开源LLM模型正在改进，但在命名实体识别（NER）的准确性方面，它们还不能与人类专家相提并论。临床记录的语言明显影响了表现，表明较小的模型在西班牙语文本方面尤其困难。然而，经过仔细的模型选择和输出后处理，Mistral-Small 3.2对关键TNM信息的检测F1得分超过74.7%。这项研究强调了生成式人工智能在临床IE中的巨大潜力，但也强调了持续改进的必要性，特别是在处理语言多样性方面。本地管理的开源模型仍远未达到人类专家的水平，但解决常见的模型缺陷可以促进将人工智能驱动的解决方案集成到公共医疗保健系统中，从而改善患者的治疗效果并促进有效的数据利用。

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引用次数: 0

Helicopter turboshaft modeling via mixtures of experts 直升机涡轮轴建模通过混合专家

IF 4.9

Machine learning with applications

Pub Date : 2026-01-04 DOI: 10.1016/j.mlwa.2025.100835

Aurelio Raffa Ugolini , Francesco Aldo Tucci , Damiano Paniccia , Luigi Capone , Mara Tanelli

A reliable and robust engine model is critical for helicopter design, operation, and maintenance, given the centrality of this sub-system. Several sources of uncertainty can limit the reliability and fidelity of first-principles models, necessitating data-driven solutions. Due to the relevant safety and security issues inherent to aircraft operation, however, fully black-box models may be unsuited to the challenge, due to their lack of explainability. In this work, we propose a multi-model approach to combine multiple physics-based descriptions, achieving a learning architecture that incorporates, in a data-driven setting, the existing knowledge of the engine’s dynamics, maximizing interpretability and facilitating model validation and diagnostics. Enabled by recent advances in onboard data collection, we learn the model directly on realistic operating conditions by leveraging recorded flight information. The benefits include a high degree of local interpretability as well as minimal requirements in terms of input signals, as empirically demonstrated in a real-world use case. We compare our technique on a real helicopter dataset against the SINDy technique, showcasing the advantages of our approach against the well-known interpretable approach to Nonlinear System Identification.

考虑到该子系统的中心地位，可靠且稳健的发动机模型对于直升机的设计、操作和维护至关重要。不确定性的几个来源可能会限制第一原理模型的可靠性和保真度，因此需要数据驱动的解决方案。然而，由于飞机运行固有的相关安全和安保问题，由于缺乏可解释性，完全的黑匣子模型可能不适合这项挑战。在这项工作中，我们提出了一种多模型方法来结合多种基于物理的描述，实现一个学习架构，该架构在数据驱动的设置中结合了引擎动力学的现有知识，最大限度地提高了可解释性，并促进了模型验证和诊断。通过机载数据收集的最新进展，我们通过利用记录的飞行信息直接在实际操作条件下学习模型。其好处包括高度的本地可解释性以及输入信号方面的最低要求，正如在实际用例中经验证明的那样。我们将我们的技术与SINDy技术在真实直升机数据集上进行了比较，展示了我们的方法与众所周知的非线性系统识别可解释方法相比的优势。

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引用次数: 0

Multi-perspective machine learning MPML: A high-performance and interpretable ensemble method for heart disease prediction 多视角机器学习MPML：一种高性能和可解释的心脏病预测集成方法

IF 4.9

Machine learning with applications

Pub Date : 2026-01-03 DOI: 10.1016/j.mlwa.2026.100836

Sean T Miller , Keaton A Logan , Ricardo Anderson , Patricia E Cowell , Curtis Busby-Earle , Lisa-Dionne Morris

Machine Learning (ML) has demonstrated strong predictive capabilities in healthcare, often surpassing human performance in pattern recognition and decision-making. However, many high-performing models lack interpretability, which is critical in clinical settings where understanding and trusting predictions is essential. To achieve our objective, we proposed a Multi-Perspective machine learning framework (MPML) that combines established base classifiers with structured perspective-based design and interpretability pipeline. MPML organises features into meaningful subsets, or perspectives, enabling both global and instance-level interpretability. Unlike traditional ensemble methods such as Bagging, Boosting, and Random Forest, MPML delivers significantly higher-quality predictions across all evaluation metrics while maintaining a transparent structure. Applied to a heart disease dataset, MPML not only improves predictive accuracy but also provides detailed, accessible explanations for individual patient outcomes, advancing the potential for practical and ethical deployment of ML in healthcare.

机器学习（ML）在医疗保健领域显示出强大的预测能力，在模式识别和决策方面往往超过人类的表现。然而，许多高性能模型缺乏可解释性，这在临床环境中至关重要，因为理解和信任预测是必不可少的。为了实现我们的目标，我们提出了一个多视角机器学习框架（MPML），该框架将已建立的基本分类器与结构化的基于视角的设计和可解释性管道相结合。MPML将特性组织到有意义的子集或透视图中，从而实现全局和实例级的可解释性。与传统的集成方法（如Bagging、Boosting和Random Forest）不同，MPML在保持透明结构的同时，在所有评估指标中提供了更高质量的预测。应用于心脏病数据集，MPML不仅提高了预测的准确性，而且还为个体患者的结果提供了详细的、可访问的解释，提高了ML在医疗保健中的实际和道德部署的潜力。

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引用次数: 0

ExpressNet-MoE: A hybrid deep neural network for emotion recognition ExpressNet-MoE：一个用于情感识别的混合深度神经网络

IF 4.9

Machine learning with applications

Pub Date : 2026-01-02 DOI: 10.1016/j.mlwa.2025.100830

Deeptimaan Banerjee, Prateek Gothwal, Ashis Kumer Biswas

In many domains, including online education, healthcare, security, and human–computer interaction, facial emotion recognition (FER) is essential. Real-world FER is still difficult because of factors like head positions, occlusions, illumination shifts, and demographic diversity. Engagement detection system, which is essential in virtual learning platforms is severely challenged by these factors. In this article, we propose ExpressNet-MoE, a novel hybrid deep learning architecture that combines Convolutional Neural Networks (CNNs) with a Mixture of Experts (MoE) framework to address these challenges. The proposed model dynamically selects the most relevant expert networks for each input, thereby improving generalization and adaptability across diverse datasets. Our methodology involves training ExpressNet-MoE independently on several benchmark datasets after preprocessing facial pictures using BlazeFace for face detection and alignment. To maintain class distribution, stratified sampling is used to divide each dataset into training and testing groups. Our model improves on the accuracy of emotion recognition by utilizing multi-scale feature extraction to collect both global and local facial features. ExpressNet-MoE includes numerous CNN-based feature extractors, a MoE module for adaptive feature selection, and finally a residual network backbone for deep feature learning. To demonstrate efficacy of our proposed model we evaluated it on four widely used datasets:

{AffectNet}_{7}

,

{AffectNet}_{8}

, RAF-DB, and FER-2013; and compared with current state-of-the-art methods. Our model achieves accuracies of 74.40%

\pm

0.45 on

{AffectNet}_{7}

, 71.98%

\pm

0.66 on

{AffectNet}_{8}

, 83.41%

\pm

1.06 on RAF-DB, and 67.05%

\pm

2.08 on FER-2013. Overall, the findings indicate that the adaptive expert selection and multi-scale feature extraction significantly enhances the robustness of facial emotion recognition across diverse real-world conditions and how it may be used to develop end-to-end emotion recognition systems in practical settings. Reproducible codes and results are made publicly accessible at https://github.com/DeeptimaanB/ExpressNet-MoE.

在许多领域，包括在线教育、医疗保健、安全和人机交互，面部情感识别（FER）是必不可少的。由于头部位置、遮挡、光照变化和人口多样性等因素，现实世界的FER仍然很困难。这些因素对虚拟学习平台中必不可少的敬业度检测系统构成了严峻的挑战。在本文中，我们提出了ExpressNet-MoE，这是一种新型的混合深度学习架构，将卷积神经网络（cnn）与混合专家（MoE）框架相结合，以解决这些挑战。该模型为每个输入动态选择最相关的专家网络，从而提高了不同数据集的泛化和适应性。我们的方法包括在使用BlazeFace对人脸图像进行预处理后，在几个基准数据集上独立训练ExpressNet-MoE进行人脸检测和对齐。为了保持类分布，采用分层抽样的方法将每个数据集划分为训练组和测试组。我们的模型利用多尺度特征提取来收集全局和局部面部特征，从而提高了情绪识别的准确性。ExpressNet-MoE包括许多基于cnn的特征提取器，用于自适应特征选择的MoE模块，最后是用于深度特征学习的残差网络骨干。为了证明我们提出的模型的有效性，我们在四个广泛使用的数据集上进行了评估：AffectNet7、AffectNet8、RAF-DB和FER-2013；与目前最先进的方法相比。该模型在AffectNet7上的准确率为74.40%±0.45，在AffectNet8上的准确率为71.98%±0.66，在RAF-DB上的准确率为83.41%±1.06，在FER-2013上的准确率为67.05%±2.08。总体而言，研究结果表明，自适应专家选择和多尺度特征提取显著增强了面部情绪识别在不同现实世界条件下的鲁棒性，以及如何将其用于开发实际环境中的端到端情绪识别系统。可复制的代码和结果可在https://github.com/DeeptimaanB/ExpressNet-MoE上公开访问。

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引用次数: 0

AI-driven detection of tiny pests in foliage: Integrating image processing and deep learning 树叶中微小害虫的人工智能检测：融合图像处理和深度学习

IF 4.9

Machine learning with applications

Pub Date : 2025-12-31 DOI: 10.1016/j.mlwa.2025.100834

Lucía Baeza-Moreno , Pedro Blanco-Carmona , Eduardo Hidalgo-Fort , Rubén Martín-Clemente , Ramón González-Carvajal

We present a novel computer vision method for detecting insect pests on plant and tree leaves under real-world conditions, combining deep learning with classical image processing techniques. Detecting small, sparsely distributed, or camouflaged insects is challenging, as current state-of-the-art object detection methods, primarily designed for larger objects, often overlook them. Our approach to this problem is twofold. First, we employ a deep learning model to analyze suspicious leaves for anomalies (a task well suited to deep learning). However, since deep models struggle with tiny objects in complex backgrounds, we complement them with conventional image processing to pre-identify potentially infested foliage, guiding the model toward relevant areas and mitigating its limitations. This combined strategy proves effective and competitive with other methods across diverse datasets and real-world scenarios. Furthermore, we also conduct a detailed analysis to interpret the model’s predictions, strengthening confidence in its effectiveness.

我们提出了一种新的计算机视觉方法，将深度学习与经典图像处理技术相结合，在现实世界条件下检测植物和树木叶片上的害虫。检测小的、稀疏分布的或伪装的昆虫是具有挑战性的，因为目前最先进的目标检测方法主要是为较大的目标设计的，经常忽略它们。我们解决这个问题的方法是双重的。首先，我们采用深度学习模型来分析可疑叶子的异常（这是一项非常适合深度学习的任务）。然而，由于深度模型在复杂背景下与微小物体作斗争，我们用传统的图像处理来补充它们，以预先识别潜在的受感染的树叶，引导模型走向相关区域并减轻其局限性。事实证明，这种组合策略在不同的数据集和现实场景中与其他方法相比是有效的，并且具有竞争力。此外，我们还进行了详细的分析来解释模型的预测，加强了对其有效性的信心。

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引用次数: 0

Machine-interactive decision-assistance using a pre-trained natural language processing model for 4D printing technique selection 使用预训练的自然语言处理模型进行4D打印技术选择的机器交互决策辅助

IF 4.9

Machine learning with applications

Pub Date : 2025-12-30 DOI: 10.1016/j.mlwa.2025.100833

Chandramohan Abhishek , Nadimpalli Raghukiran

The present research showcases a machine-interactive approach for making decisions using a pre-trained natural language processing (NLP) model. The method is developed for 4D (4-dimensional) printing technique selection, as a plurality of variables is involved, such as process, material, design, and sequence selections. Due to the availability of numerous options, arriving at a preferred choice of technique requires expertise and time. The developed method aids in finding assistance from a single source. The approach incorporates bidirectional encoder representations from transformers (BERT), which accommodates parallel meanings of user requests, such as synonyms and adjectives, among others. The closed-loop system is programmed with a set of 7 prompts. It also introduces additional affirmation prompts to navigate both ambiguous phrasing and out-of-scope detection in order to receive a meaningful recommendation from the machine. The rule-governed technique (lightweight rule set) guides the selection of the conformable request during each prompt. The inference-based approach takes user requests, performs objective classification using BERT according to selected criteria, then dynamically filters the data, and recommends suggestions, with an inference time of 0.79 s. The modified model also establishes multi-level relationships among prompts for text classification. k-fold validation reached highest possible accuracy upon training with optimal hyperparameters. The fine-tuned method developed in Python environment can be generalized for other systems. The present research demonstrates the possibility of adapting an openly accessible model for developing a decision-assistance system with minimal personal computational resources.

本研究展示了使用预训练的自然语言处理（NLP）模型进行决策的机器交互方法。该方法是为4D（四维）打印技术选择而开发的，因为涉及多个变量，如工艺，材料，设计和顺序选择。由于可供选择的方法很多，要找到一种最佳的技术需要专业知识和时间。开发的方法有助于从单一来源寻求帮助。该方法结合了来自转换器（BERT）的双向编码器表示，它可以容纳用户请求的并行含义，例如同义词和形容词等。闭环系统由一组7个提示程序编程。它还引入了额外的确认提示，以导航模糊的短语和超出范围的检测，以便从机器接收有意义的推荐。规则控制的技术（轻量级规则集）指导在每个提示期间选择符合的请求。基于推理的方法接受用户请求，根据选择的标准使用BERT进行客观分类，然后动态过滤数据并推荐建议，推理时间为0.79 s。修改后的模型还建立了文本分类提示之间的多级关系。K-fold验证在最优超参数训练后达到最高可能的准确性。在Python环境中开发的微调方法可以推广到其他系统。目前的研究表明，采用开放可访问的模型来开发具有最小个人计算资源的决策辅助系统的可能性。

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引用次数: 0

LSEL: A lightweight deep learning model for social-emotional classification of classical music LSEL：用于古典音乐社会情感分类的轻量级深度学习模型

IF 4.9

Machine learning with applications

Pub Date : 2025-12-30 DOI: 10.1016/j.mlwa.2025.100832

Yuan-Jin Lin , Yu-Chi Chou , Shan-Ken Chien , Pen-Chiang Chao , Kuang-Kai Yeh , Yen-Chia Peng , Chen-Hao Tsao , Chih-Yun Chen , Shih-Lun Chen , Kuo-Chen Li , Wei-Chen Tu

Background/Objectives

Social-emotional learning (SEL) plays a crucial role in special education, yet current assessment approaches rely heavily on subjective teacher observation, which can be time-consuming and difficult to standardize. Music provides a meaningful medium for evaluating emotional competencies, creating an opportunity for artificial intelligence to support more objective and scalable SEL assessment.

Methods

We propose a lightweight social-emotional music classification model, termed LSEL, designed to identify three SEL-related competencies: Empathetic Perspective-Taking, Outlook, and Problem-Solving. LSEL utilizes 40×128 mel-frequency cepstral coefficient as input to capture core spectral–temporal characteristics relevant to SEL perception. Moreover, we provided an open-source SEM dataset for domain experts, utilizing 591 samples, which consisted of 194 Empathetic, 214 Outlook, and 183 Perspective-Taking samples, to analyze LSEL performance.

Results

LSEL reaching an average accuracy of 96.55 % and mAP of 99.29 % across experiments. With Cohen’s κ averaging 94.32 % and R² reaching 94.15 %, indicating high consistency with ground-truth. Per-category accuracies were similarly stable, including 96.95 % for Empathetic Perspective-Taking, 95.16 % for Outlook, and 95.36 % for Problem-Solving.

Conclusions

The lightweight LSEL framework offers an effective and robust solution for social-emotional music classification, supporting objective SEL assessment in educational contexts. The release of the SEM dataset further contributes to a valuable resource for advancing AI-assisted SEL research.

背景/目的社会情绪学习（SEL）在特殊教育中起着至关重要的作用，但目前的评估方法严重依赖于教师的主观观察，这既耗时又难以标准化。音乐为评估情感能力提供了一种有意义的媒介，为人工智能支持更客观、可扩展的情感能力评估创造了机会。方法我们提出了一个轻量级的社会情感音乐分类模型，称为LSEL，旨在识别三种与sel相关的能力：移情视角，展望和问题解决。LSEL利用40×128 mel-frequency倒谱系数作为输入，捕捉与SEL感知相关的核心频谱-时间特征。此外，我们为领域专家提供了一个开源的SEM数据集，利用591个样本，其中包括194个移情样本，214个展望样本和183个视角样本，来分析LSEL的表现。结果slsel的平均准确率为96.55%，mAP的平均准确率为99.29%。Cohen’s κ均值为94.32%，R²均值为94.15%，与ground-truth的一致性较高。每个类别的准确性同样稳定，包括共情换位思考的96.95%，展望的95.16%，问题解决的95.36%。结论轻量级LSEL框架为社会情感音乐分类提供了一种有效且稳健的解决方案，支持客观的教育背景下的SEL评估。SEM数据集的发布进一步为推进人工智能辅助SEL研究提供了宝贵的资源。

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引用次数: 0

PRCSL: A privacy-preserving continual split learning framework for decentralized medical diagnosis PRCSL：用于分散医疗诊断的隐私保护持续分裂学习框架

IF 4.9

Machine learning with applications

Pub Date : 2025-12-29 DOI: 10.1016/j.mlwa.2025.100828

Jungmin Eom , Minjun Kang , Myungkeun Yoon , Nikil Dutt , Jinkyu Kim , Jaekoo Lee

Deep learning-based medical AI systems are increasingly deployed for disease diagnosis in decentralized healthcare environments where data are siloed across hospitals and IoT devices and cannot be freely shared due to strict privacy and security regulations. However, most existing continual learning and distributed learning approaches either assume centrally aggregated data or overlook incremental clinical changes, leading to catastrophic forgetting when applied to real-world medical data streams.

This paper introduces a novel healthcare-specific framework that integrates continual learning and distributed learning methods to utilize medical AI models effectively by addressing the practical constraints of the healthcare and medical ecosystem, such as data privacy, security, and changing clinical environments. Through the proposed framework, medical clients, such as hospital devices and IoT-based smart devices, can collaboratively train deep learning-based models on distributed computing resources without sharing sensitive data. Additionally, by considering incremental characteristics in medical environments such as mutations, new diseases, and abnormalities, the proposed framework can improve the disease diagnosis of medical AI models in actual clinical scenarios.

We propose Privacy-preserving Rehearsal-based Continual Split Learning (PRCSL), a healthcare-specific continual split learning framework that combines differential-privacy-based exemplar sharing, a mutual information alignment (MIA) module to correct representation shifts induced by noisy exemplars, and a parameter-free nearest-mean-of-exemplars (NME) classifier to mitigate task-recency bias under non-IID data distributions. o=Across eight benchmark datasets, including four MedMNIST subsets, HAM10000, CCH5000, c=CIFAR,cp=, p=100, and SVHN, PRCSL achieves competitive performance compared with representative continual learning baselines in terms of average accuracy and average forgetting. In particular, PRCSL achieves up to 3.62%p higher average accuracy than the best baseline. These results indicate that PRCSL enables privacy-preserving, communication-efficient, and continually adaptable medical AI in realistic decentralized clinical and IoT-enabled ecosystems. Our code is publicly available at our repository.

基于深度学习的医疗人工智能系统越来越多地部署在分散的医疗环境中进行疾病诊断，这些环境中的数据分散在医院和物联网设备之间，由于严格的隐私和安全法规，无法自由共享。然而，大多数现有的持续学习和分布式学习方法要么假设集中汇总的数据，要么忽略增量临床变化，在应用于现实世界的医疗数据流时导致灾难性的遗忘。本文介绍了一种新的医疗保健特定框架，该框架集成了持续学习和分布式学习方法，通过解决医疗保健和医疗生态系统的实际限制，如数据隐私、安全性和不断变化的临床环境，有效地利用医疗人工智能模型。通过提出的框架，医疗客户端（如医院设备和基于物联网的智能设备）可以在不共享敏感数据的情况下，在分布式计算资源上协同训练基于深度学习的模型。此外，通过考虑突变、新疾病、异常等医疗环境中的增量特征，该框架可以提高医疗AI模型在实际临床场景中的疾病诊断能力。我们提出了一种基于隐私保护预演的持续分裂学习（PRCSL），这是一种医疗保健特定的持续分裂学习框架，它结合了基于差分隐私的范例共享，一个相互信息校准（MIA）模块来纠正由噪声范例引起的表示移位，以及一个无参数的最接近范例均值（NME）分类器来减轻非iid数据分布下的任务近因偏差。在八个基准数据集上，包括四个MedMNIST子集，HAM10000, CCH5000, c=CIFAR,cp=, p=100和SVHN， PRCSL在平均准确率和平均遗忘方面与代表性的持续学习基线相比具有竞争力。特别是，PRCSL的平均准确度比最佳基线高出3.62%p。这些结果表明，PRCSL能够在现实的分散临床和物联网生态系统中实现隐私保护、通信高效和持续适应性强的医疗人工智能。我们的代码在我们的存储库中是公开的。

{"title":"PRCSL: A privacy-preserving continual split learning framework for decentralized medical diagnosis","authors":"Jungmin Eom , Minjun Kang , Myungkeun Yoon , Nikil Dutt , Jinkyu Kim , Jaekoo Lee","doi":"10.1016/j.mlwa.2025.100828","DOIUrl":"10.1016/j.mlwa.2025.100828","url":null,"abstract":"<div><div>Deep learning-based medical AI systems are increasingly deployed for disease diagnosis in decentralized healthcare environments where data are siloed across hospitals and IoT devices and cannot be freely shared due to strict privacy and security regulations. However, most existing continual learning and distributed learning approaches either assume centrally aggregated data or overlook incremental clinical changes, leading to catastrophic forgetting when applied to real-world medical data streams.</div><div>This paper introduces a novel healthcare-specific framework that integrates continual learning and distributed learning methods to utilize medical AI models effectively by addressing the practical constraints of the healthcare and medical ecosystem, such as data privacy, security, and changing clinical environments. Through the proposed framework, medical clients, such as hospital devices and IoT-based smart devices, can collaboratively train deep learning-based models on distributed computing resources without sharing sensitive data. Additionally, by considering incremental characteristics in medical environments such as mutations, new diseases, and abnormalities, the proposed framework can improve the disease diagnosis of medical AI models in actual clinical scenarios.</div><div>We propose Privacy-preserving Rehearsal-based Continual Split Learning (PRCSL), a healthcare-specific continual split learning framework that combines differential-privacy-based exemplar sharing, a mutual information alignment (MIA) module to correct representation shifts induced by noisy exemplars, and a parameter-free nearest-mean-of-exemplars (NME) classifier to mitigate task-recency bias under non-IID data distributions. o=Across eight benchmark datasets, including four MedMNIST subsets, HAM10000, CCH5000, c=CIFAR,cp=, p=100, and SVHN, PRCSL achieves competitive performance compared with representative continual learning baselines in terms of average accuracy and average forgetting. In particular, PRCSL achieves up to 3.62%p higher average accuracy than the best baseline. These results indicate that PRCSL enables privacy-preserving, communication-efficient, and continually adaptable medical AI in realistic decentralized clinical and IoT-enabled ecosystems. Our code is publicly available at our repository.</div></div>","PeriodicalId":74093,"journal":{"name":"Machine learning with applications","volume":"23 ","pages":"Article 100828"},"PeriodicalIF":4.9,"publicationDate":"2025-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145976863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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