Intelligence-based medicine最新文献_第4页

Using big data to predict young adult ischemic vs. non-ischemic heart disease risk factors: An artificial intelligence based model 利用大数据预测年轻人缺血性与非缺血性心脏病危险因素：基于人工智能的模型

Intelligence-based medicine

Pub Date : 2025-01-01 DOI: 10.1016/j.ibmed.2025.100207

Salam Bani Hani , Muayyad Ahmad

引用次数: 0

Open-source small language models for personal medical assistant chatbots 个人医疗助理聊天机器人的开源小语言模型

Intelligence-based medicine

Pub Date : 2025-01-01 DOI: 10.1016/j.ibmed.2024.100197

Matteo Magnini , Gianluca Aguzzi , Sara Montagna

Medical chatbots are becoming essential components of telemedicine applications as tools to assist patients in the self-management of their conditions. This trend is particularly driven by advancements in natural language processing techniques with pre-trained language models (LMs). However, the integration of LMs into clinical environments faces challenges related to reliability and privacy concerns.

This study seeks to address these issues by exploiting a privacy by design architectural solution that utilises the fully local deployment of open-source LMs. Specifically, to mitigate any risk of information leakage, we focus on evaluating the performance of open-source language models (SLMs) that can be deployed on personal devices, such as smartphones or laptops, without stringent hardware requirements.

We assess the effectiveness of this solution adopting hypertension management as a case study. Models are evaluated across various tasks, including intent recognition and empathetic conversation, using Gemini Pro 1.5 as a benchmark. The results indicate that, for certain tasks such as intent recognition, Gemini outperforms other models. However, by employing the “large language model (LLM) as a judge” approach for semantic evaluation of response correctness, we found several models that demonstrate a close alignment with the ground truth. In conclusion, this study highlights the potential of locally deployed SLMs as components of medical chatbots, while addressing critical concerns related to privacy and reliability.

医疗聊天机器人正在成为远程医疗应用的重要组成部分，作为帮助患者自我管理病情的工具。这一趋势尤其受到自然语言处理技术与预训练语言模型（LMs）的进步的推动。然而，将LMs集成到临床环境中面临着与可靠性和隐私问题相关的挑战。本研究试图通过利用开源LMs的完全本地部署来利用隐私设计架构解决方案来解决这些问题。具体来说，为了减少信息泄露的风险，我们着重于评估可以部署在个人设备（如智能手机或笔记本电脑）上的开源语言模型（slm）的性能，而不需要严格的硬件要求。我们以高血压管理为例来评估这种解决方案的有效性。模型在各种任务中进行评估，包括意图识别和移情对话，使用Gemini Pro 1.5作为基准。结果表明，对于某些任务，如意图识别，Gemini优于其他模型。然而，通过采用“大型语言模型（LLM）作为判断”的方法来对响应正确性进行语义评估，我们发现了几个与基本事实密切一致的模型。总之，本研究强调了本地部署的slm作为医疗聊天机器人组件的潜力，同时解决了与隐私和可靠性相关的关键问题。

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引用次数: 0

Development and validation of a moderate aortic stenosis disease progression model 中度主动脉瓣狭窄疾病进展模型的建立和验证

Intelligence-based medicine

Pub Date : 2025-01-01 DOI: 10.1016/j.ibmed.2025.100201

Miguel R. Sotelo , Paul Nona , Loren Wagner , Chris Rogers , Julian Booker , Efstathia Andrikopoulou

Background

Understanding the multifactorial determinants of rapid progression in patients with aortic stenosis (AS) remains limited. We aimed to develop and validate a machine learning model (ML) for predicting rapid progression from moderate to severe AS within one year.

Methods

8746 patients were identified with moderate AS across seven healthcare organizations. Three ML models were trained using demographic, and echocardiographic variables, namely Random Forest, XGBoost and causal discovery-logistic regression. An ensemble model was developed integrating the aforementioned three. A total of 3355 patients formed the training and internal validation cohort. External validation was performed on 171 patients from one institution.

Results

An ensemble model was selected due to its superior F1 score and precision in internal validation (0.382 and 0.301, respectively). Its performance on the external validation cohort was modest (F1 score = 0.626, precision = 0.532).

Conclusion

An ensemble model comprising only demographic and echocardiographic variables was shown to have modest performance in predicting one-year progression from moderate to severe AS. Further validation in larger populations, along with integration of comprehensive clinical data, is crucial for broader applicability.

背景：对主动脉瓣狭窄（AS）患者快速进展的多因素决定因素的了解仍然有限。我们的目标是开发和验证一种机器学习模型（ML），用于预测一年内从中度到重度AS的快速进展。方法7家医疗机构共8746例中度AS患者。使用人口统计学和超声心动图变量，即随机森林、XGBoost和因果发现-逻辑回归，训练了三个ML模型。开发了一个集成模型，将上述三者集成在一起。共有3355名患者组成了培训和内部验证队列。对来自同一机构的171例患者进行了外部验证。结果集成模型在内部验证中F1得分和精密度均较优（分别为0.382和0.301）。其在外部验证队列中的表现一般（F1评分= 0.626，精密度= 0.532）。结论仅包含人口统计学和超声心动图变量的集成模型在预测中度到重度AS的一年进展方面表现不佳。在更大的人群中进一步验证，以及综合临床数据的整合，对于更广泛的适用性至关重要。

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引用次数: 0

ESC-UNET: A hybrid CNN and Swin Transformers for skin lesion segmentation ESC-UNET：一种混合CNN和Swin变压器的皮肤损伤分割方法

Intelligence-based medicine

Pub Date : 2025-01-01 DOI: 10.1016/j.ibmed.2025.100257

Anwar Jimi , Nabila Zrira , Oumaima Guendoul , Ibtissam Benmiloud , Haris Ahmad Khan , Shah Nawaz

One of the most important tasks in computer-aided diagnostics is the automatic segmentation of skin lesions, which plays an essential role in the early diagnosis and treatment of skin cancer. In recent years, the Convolutional Neural Network (CNN) has largely replaced other traditional methods for segmenting skin lesions. However, due to insufficient information and unclear lesion region segmentation, skin lesion image segmentation still has challenges. In this paper, we propose a novel deep medical image segmentation approach named “ESC-UNET” which combines the advantages of CNN and Transformer to effectively leverage local information and long-range dependencies to enhance medical image segmentation. In terms of the local information, we use a CNN-based encoder and decoder framework. The CNN branch mines local information from medical images using the locality of convolution processes and the pre-trained EfficientNetB5 network. As for the long-range dependencies, we build a Transformer branch that emphasizes the global context. In addition, we employ Atrous Spatial Pyramid Pooling (ASPP) to gather network-wide relevant information. The Convolution Block Attention Module (CBAM) is added to the model to promote effective features and suppress ineffective features in segmentation. We have evaluated our network using the ISIC 2016, ISIC 2017, and ISIC 2018 datasets. The results demonstrate the efficiency of the proposed model in segmenting skin lesions.

计算机辅助诊断中最重要的任务之一是皮肤病变的自动分割，这对皮肤癌的早期诊断和治疗起着至关重要的作用。近年来，卷积神经网络（CNN）在很大程度上取代了其他传统的皮肤损伤分割方法。然而，由于信息不足和病灶区域分割不清，皮肤病灶图像分割仍然存在挑战。本文提出了一种新的医学图像深度分割方法“ESC-UNET”，该方法结合了CNN和Transformer的优点，有效地利用了局部信息和远程依赖关系来增强医学图像分割。在局部信息方面，我们使用了基于cnn的编码器和解码器框架。CNN分支使用卷积过程的局部性和预训练的effentnetb5网络从医学图像中挖掘局部信息。至于远程依赖，我们构建一个强调全局上下文的Transformer分支。此外，我们采用亚特劳斯空间金字塔池（ASPP）来收集全网络的相关信息。在该模型中加入了卷积块注意模块（CBAM），在分割中提升有效特征，抑制无效特征。我们使用ISIC 2016、ISIC 2017和ISIC 2018数据集评估了我们的网络。实验结果证明了该模型在皮肤损伤分割方面的有效性。

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引用次数: 0

In-silo federated learning vs. centralized learning for segmenting acute and chronic ischemic brain lesions 竖井联合学习与集中式学习对急性和慢性缺血性脑损伤的分割

Intelligence-based medicine

Pub Date : 2025-01-01 DOI: 10.1016/j.ibmed.2025.100283

Joon Kim , Hoyeon Lee , Jonghyeok Park , Sang Hyun Park , Myungjae Lee , Leonard Sunwoo , Chi Kyung Kim , Beom Joon Kim , Dong-Eog Kim , Wi-Sun Ryu

Objectives

To investigate the efficacy of federated learning (FL) compared to industry-level centralized learning (CL) for segmenting acute infarct and white matter hyperintensity.

Materials and methods

This retrospective study included 13,546 diffusion-weighted images (DWI) from 10 hospitals and 8421 fluid-attenuated inversion recovery (FLAIR) images from 9 hospitals for acute (Task I) and chronic (Task II) lesion segmentation. We trained with datasets originated from 9 and 3 institutions for Task I and Task II, respectively, and externally tested them in datasets originated from 1 and 6 institutions each. For FL, the central server aggregated training results every four rounds with FedYogi (Task I) and FedAvg (Task II). A batch clipping strategy was tested for the FL models. Performances were evaluated with the Dice similarity coefficient (DSC).

Results

The mean ages (SD) for the training datasets were 68.1 (12.8) for Task I and 67.4 (13.0) for Task II. The frequency of male participants was 51.5 % and 60.4 %, respectively. In Task I, the FL model employing batch clipping trained for 360 epochs achieved a DSC of 0.754 ± 0.183, surpassing an equivalently trained CL model (DSC 0.691 ± 0.229; p < 0.001) and comparable to the best-performing CL model at 940 epochs (DSC 0.755 ± 0.207; p = 0.701). In Task II, no significant differences were observed amongst FL model with clipping, without clipping, and CL model after 48 epochs (DSCs of 0.761 ± 0.299, 0.751 ± 0.304, 0.744 ± 0.304). Few-shot FL showed significantly lower performance. Task II reduced training times with batch clipping (3.5–1.75 h).

Conclusions

Comparisons between CL and FL in identical settings suggest the feasibility of FL for medical image segmentation.

目的比较联邦学习（FL）与集中式学习（CL）在急性梗死和脑白质高信号分割中的疗效。材料和方法本回顾性研究包括来自10家医院的13546张弥散加权图像（DWI）和来自9家医院的8421张液体衰减反转恢复（FLAIR）图像，用于急性（任务I）和慢性（任务II）病变分割。我们在Task I和Task II中分别使用来自9个和3个机构的数据集进行训练，并在来自1个和6个机构的数据集中对它们进行外部测试。对于FL，中央服务器使用FedYogi （Task I）和FedAvg （Task II）每四轮汇总训练结果。对FL模型进行了批量裁剪策略测试。用Dice相似系数（DSC）对性能进行评价。结果任务1的平均年龄（SD）为68.1(12.8)，任务2的平均年龄（SD）为67.4（13.0）。男性参与者的频率分别为51.5%和60.4%。在Task I中，采用360次批次裁剪训练的FL模型的DSC为0.754±0.183，超过了同等训练的CL模型(DSC为0.691±0.229；p & lt;0.001)，与940个epoch的最佳CL模型相当(DSC 0.755±0.207；p = 0.701)。在Task II中，经过48个epoch后，经剪裁的FL模型、未经过剪裁的FL模型和CL模型的dsc均无显著差异（dsc分别为0.761±0.299、0.751±0.304、0.744±0.304）。少射FL表现出明显较低的性能。任务II通过批量裁剪减少了训练时间（3.5-1.75小时）。结论在相同的条件下，对CL和FL的比较表明FL用于医学图像分割是可行的。

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引用次数: 0

Swarm intelligence in biomedical engineering 生物医学工程中的群体智能

Intelligence-based medicine

Pub Date : 2025-01-01 DOI: 10.1016/j.ibmed.2025.100308

Seyyed Ali Zendehbad , Elias Mazrooei Rad , Shahryar Salmani Bajestani

Swarm Intelligence (SI), a specialized branch of Artificial Intelligence (AI), is founded on the collective behaviors observed in biological systems, such as those of ants, bees, and bird flocks. This bio-inspired approach enables SI to develop computational algorithms that tackle complex problems, which traditional methods often struggle to address. Over the past few decades, SI has gained substantial traction in biomedical engineering due to its capacity to address multifaceted issues with higher adaptability and efficiency. This review presents key advancements in SI applications across three primary areas: neurorehabilitation, Alzheimer's Disease Diagnosis (ADD), and medical image processing. In neurorehabilitation, SI has played a pivotal role in improving the precision and adaptability of devices such as exoskeletons and neuroprostheses, enhancing motor function recovery for patients. Similarly, in ADD, SI algorithms have shown significant promise in analyzing neuroimaging and neurophysiological data, increasing diagnostic accuracy and enabling earlier intervention. Furthermore, in medical image processing, SI techniques have been effectively applied to tasks such as image segmentation, tumor detection, feature extraction, and artifact reduction, particularly in modalities like Magnetic Resonance Imaging (MRI), Computed Tomography (CT), and ultrasound imaging (such as Sonography). Based on the literature analyzed, SI methods have demonstrated consistent strengths in global optimization, adaptability to noisy data, and robustness in feature selection tasks when compared to traditional machine learning techniques. However, SI still faces challenges, especially regarding computational complexity, model interpretability, and limited clinical translation. Overcoming these hurdles is crucial for the full-scale adoption of this technology in clinical settings. This review not only highlights the progress in these areas but also synthesizes current limitations and future directions to guide the effective integration of SI into real-world biomedical applications.

群体智能（SI）是人工智能（AI）的一个专门分支，它建立在生物系统中观察到的集体行为基础上，例如蚂蚁、蜜蜂和鸟群。这种受生物启发的方法使SI能够开发出解决复杂问题的计算算法，而传统方法往往难以解决这些问题。在过去的几十年里，由于其具有更高的适应性和效率，能够解决多方面的问题，因此在生物医学工程中获得了巨大的吸引力。本文综述了SI在三个主要领域的应用进展：神经康复、阿尔茨海默病诊断（ADD）和医学图像处理。在神经康复中，SI在提高外骨骼和神经假体等设备的精度和适应性，促进患者运动功能恢复方面发挥了关键作用。同样，在ADD中，SI算法在分析神经影像学和神经生理学数据、提高诊断准确性和实现早期干预方面显示出巨大的前景。此外，在医学图像处理中，SI技术已有效地应用于图像分割、肿瘤检测、特征提取和伪影减少等任务，特别是在磁共振成像（MRI）、计算机断层扫描（CT）和超声成像（如超声）等模式中。基于文献分析，与传统机器学习技术相比，SI方法在全局优化、对噪声数据的适应性和特征选择任务的鲁棒性方面表现出一致的优势。然而，SI仍然面临挑战，特别是在计算复杂性、模型可解释性和有限的临床翻译方面。克服这些障碍对于在临床环境中全面采用这项技术至关重要。这篇综述不仅强调了这些领域的进展，而且综合了当前的局限性和未来的方向，以指导科学技术有效地融入现实世界的生物医学应用。

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引用次数: 0

Regulating intelligence: a systematic analysis of safety, ethics, and equity in artificial intelligence driven healthcare 规范智能：对人工智能驱动的医疗保健中的安全、伦理和公平进行系统分析

Intelligence-based medicine

Pub Date : 2025-01-01 DOI: 10.1016/j.ibmed.2025.100320

Muayyad Ahmad

Objective

The growing use of artificial intelligence (AI) in healthcare demands robust regulatory frameworks to ensure safety, ethics, and legal compliance, particularly regarding algorithmic transparency, data privacy, and bias. This systematic review analyzes AI regulations and grey literature (2019–2024) from the Food and Drug Administration (FDA), the World Health Organization (WHO), the Organization for Economic Co-operation and Development (OECD), and the International Organization for Standardization/International Electrotechnical Commission (ISO/IEC).

Methods

This systematic review, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, analysed 26 studies (Peer-reviewed studies: n = 17 & Grey literature: n = 9) on AI regulatory frameworks in healthcare between 2019 and 2024. A systematic literature search and rigorous inclusion criteria ensured relevance, with data consolidated into safety, effectiveness, and ethical themes. The analysis integrates a low-middle income countries (LMIC) perspective via WHO policy and a handful of studies, but the main academic body is drawn primarily from high-income contexts.

Bias risk was assessed systematically.

Results

The reviewed studies highlight the critical need for AI regulatory frameworks in healthcare, focusing on patient safety, ethics, and trust. Key findings stress the necessity for transparent, equitable integration and clear guidelines addressing bias, legal issues, and validation. Grey literature consistently emphasizes risk-based safety models and principles like transparency and human oversight. However, a significant gap remains in translating equity commitments into enforceable standards for bias mitigation, underscoring a critical need for future regulatory action.

Conclusion

This review identifies critical gaps in AI regulatory frameworks, particularly in equity, real-world validation, and liability, and proposes actionable, interdisciplinary strategies to ensure AI's safe, ethical, and equitable integration into healthcare.

人工智能（AI）在医疗保健领域的日益广泛应用需要强有力的监管框架，以确保安全、道德和法律合规性，特别是在算法透明度、数据隐私和偏见方面。本系统综述分析了美国食品药品监督管理局（FDA）、世界卫生组织（WHO）、经济合作与发展组织（OECD）和国际标准化组织/国际电工委员会（ISO/IEC）的人工智能法规和灰色文献（2019-2024）。方法：本系统评价遵循系统评价和荟萃分析（PRISMA）指南的首选报告项目，分析了26项关于2019年至2024年医疗保健领域人工智能监管框架的研究（同行评审研究：n = 17；灰色文献：n = 9）。系统的文献检索和严格的纳入标准确保了相关性，并将数据整合到安全性、有效性和伦理主题中。该分析通过世卫组织政策和少数研究整合了中低收入国家的视角，但主要学术机构主要来自高收入背景。系统评估偏倚风险。结果：回顾的研究强调了在医疗保健领域建立人工智能监管框架的迫切需要，重点是患者安全、道德和信任。主要结论强调了透明、公平整合的必要性，以及解决偏见、法律问题和有效性的明确指导方针。灰色文献一贯强调基于风险的安全模型和原则，如透明度和人为监督。然而，在将股权承诺转化为可执行的减轻偏见标准方面仍存在重大差距，这突出表明迫切需要今后采取监管行动。本综述确定了人工智能监管框架的关键差距，特别是在公平、现实验证和责任方面，并提出了可操作的跨学科战略，以确保人工智能安全、道德和公平地融入医疗保健。

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引用次数: 0

3D brain tumor segmentation in MRI images using hierarchical adaptive pruning of non-tumor regions 基于非肿瘤区域分层自适应剪枝的MRI图像三维脑肿瘤分割

Intelligence-based medicine

Pub Date : 2025-01-01 DOI: 10.1016/j.ibmed.2025.100303

Ali Mehrabi, Nasser Mehrshad

Background

The detection of brain tumors in MRI images has significantly improved with the advent of deep learning methods. However, these approaches often suffer from high complexity, computational cost, and the need for extensive annotated training data, making them less practical for real-time and patient-centered diagnostic systems. To address these challenges, this study introduces a perceptually inspired, algorithmic method that mimics the diagnostic behavior of physicians, offering a lightweight and interpretable alternative for brain tumor segmentation.

Method

We propose a novel adaptive hierarchical pruning algorithm for 3D MRI brain images that iteratively removes low-intensity, non-tumor voxels based on the statistical distribution of intensities. The tumor region is identified through the comparison of the remaining pixel intensity values statistics. The pruning automatically stops when the mean and median of the remaining voxels converge, leaving the candidate tumor region.

Results

The proposed algorithm was evaluated on all patients of the BraTS2019 and BraTS2023 datasets, achieving segmentation accuracies of 99.1 % and 99.13 %, respectively. It demonstrated high sensitivity and specificity compared to several deep learning methods, showing robust performance across diverse patient scans.

Conclusions

This study demonstrates that a simple, perceptually driven segmentation algorithm can match or outperform complex deep learning models, particularly in clinical settings where lightweight, transparent, and efficient tools are essential.

随着深度学习方法的出现，MRI图像中脑肿瘤的检测有了显著的提高。然而，这些方法通常存在复杂性高、计算成本高、需要大量带注释的训练数据等问题，这使得它们在实时和以患者为中心的诊断系统中不太实用。为了解决这些挑战，本研究引入了一种感知启发的算法方法，该方法模仿医生的诊断行为，为脑肿瘤分割提供了一种轻量级且可解释的替代方法。方法提出了一种新的自适应分层剪枝算法，该算法基于强度的统计分布，迭代地去除低强度的非肿瘤体素。通过比较剩余的像素强度值统计来识别肿瘤区域。当剩余体素的均值和中值收敛时，剪枝自动停止，留下候选肿瘤区域。结果该算法在BraTS2019和BraTS2023数据集的所有患者上进行了评估，分割准确率分别达到99.1%和99.13%。与几种深度学习方法相比，它表现出高灵敏度和特异性，在不同的患者扫描中表现出强大的性能。本研究表明，一个简单的、感知驱动的分割算法可以匹配或优于复杂的深度学习模型，特别是在临床环境中，轻量级、透明和高效的工具是必不可少的。

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引用次数: 0

Segmentation of low-resolution MII human oocyte images using data-efficient meta-learning 使用数据高效元学习的低分辨率MII人类卵母细胞图像分割

Intelligence-based medicine

Pub Date : 2025-01-01 DOI: 10.1016/j.ibmed.2025.100327

Mahshid Alizadeh Kiashi, Ashkan Mousazadeh Soustani, Seyed Abolghasem Mirroshandel

Although many infertility problems in humans are treatable today, some of these methods have problems that need to be addressed. One of the ways to solve infertility problems is to use in vitro fertilization. In this method, if a suitable oocyte with a high chance of fertility is not selected, it may lead to multiple births or even infertility. Identifying the most suitable cell with the highest chance of fertility is a very difficult task even for embryologists. Hence, this research focuses on designing a deep learning framework to take on the challenging task of segmenting low-resolution oocyte microscopic images and accurately delineating and distinguishing the boundaries of the three important regions of the oocyte, i.e., zona pellucida (ZP), perivitelline space (PVS), and ooplasm. Then it is calculated with the key indicators in the health of each cell and compared with the true values to evaluate the level of abnormalities and select the most suitable cell for in vitro fertilization. Finally, after testing on 253 images of the test set, in binary segmentation, the average accuracy is 99.17 in ooplasm, 97.14 in PVS, and 94.78 in ZP, and in multi-class segmentation, the average accuracy is 99.30 in ooplasm, 97.36 in PVS, and 94.95 in ZP. These values have been obtained by training on 300 microscopic images of human oocytes, which have been reduced to less than half compared to previous studies.

虽然今天人类的许多不孕症是可以治疗的，但其中一些方法存在需要解决的问题。解决不孕问题的方法之一是使用体外受精。在这种方法中，如果没有选择生育机会高的合适卵母细胞，可能会导致多胎甚至不孕。即使对胚胎学家来说，确定最合适的细胞和最高的生育机会也是一项非常困难的任务。因此，本研究的重点是设计一个深度学习框架，以承担低分辨率卵母细胞显微图像的分割任务，并准确描绘和区分卵母细胞的三个重要区域，即透明带（ZP）、卵泡周围空间（PVS）和卵浆的边界。然后与各细胞健康状况的关键指标进行计算，并与真实值进行比较，评估异常程度，选择最适合体外受精的细胞。最后，对测试集的253张图像进行测试，在二值分割中，卵浆的平均准确率为99.17，PVS的平均准确率为97.14，ZP的平均准确率为94.78；在多类分割中，卵浆的平均准确率为99.30，PVS的平均准确率为97.36，ZP的平均准确率为94.95。这些值是通过对300个人类卵母细胞的显微图像进行训练获得的，与以前的研究相比，这些图像减少到不到一半。

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引用次数: 0

Predicting COPD admissions using machine learning and SHAP: An exploratory multi-hospital study in Riyadh, Saudi Arabia 使用机器学习和SHAP预测COPD入院：沙特阿拉伯利雅得的一项探索性多医院研究

Intelligence-based medicine

Pub Date : 2025-01-01 DOI: 10.1016/j.ibmed.2025.100312

Anas Ali Alhur , Jamilu Sani , Mohamed Mustaf Ahmed

Background

Chronic obstructive pulmonary disease (COPD) is a leading cause of hospitalization and mortality globally, placing a substantial burden on healthcare systems. In Saudi Arabia, COPD admissions are rising due to demographic shifts and environmental exposures. Accurate prediction of COPD-related hospitalizations is essential for timely intervention and resource planning. This study applied machine learning (ML) techniques to predict COPD admissions using routine hospital data from major healthcare facilities in Riyadh.

Methods

A cross-sectional analysis was conducted using 41,544 patient admission records from eight major hospitals in Saudi Arabia between 2022 and 2024. The dataset included demographic, clinical, and healthcare utilization variables. Several ML classifiers: Logistic Regression, Support Vector Machine, K-Nearest Neighbors, Decision Tree, Random Forest, Gradient Boosting, and XGBoost, were developed and evaluated. The primary outcome was inpatient admission for COPD. Model performance was assessed using accuracy, precision, recall, F1-score, AUROC, and confusion matrices. SHapley Additive exPlanations (SHAP) were used to interpret model outputs and rank feature importance.

Results

The Random Forest model outperformed other classifiers with an accuracy of 0.73, precision of 0.70, recall of 0.79, F1-score of 0.74, and AUROC of 0.79. Key predictors identified by SHAP analysis included hospital name, admission count, comorbid conditions, and disease severity. Features such as gender and seasonal variation showed minimal influence on prediction outcomes. SHAP visualizations provided interpretable insights into individual-level risk contributions.

Conclusion

Machine learning models, particularly Random Forest, demonstrated moderate but promising capacity for predicting COPD admissions using routine hospital data. Model interpretability through SHAP enhances clinical relevance and supports early identification of high-risk patients. Integration of these tools into hospital systems may facilitate proactive care and improve resource allocation for respiratory conditions.

慢性阻塞性肺疾病（COPD）是全球住院和死亡的主要原因，给卫生保健系统带来了沉重负担。在沙特阿拉伯，由于人口变化和环境暴露，慢性阻塞性肺病入院人数正在上升。准确预测copd相关住院对及时干预和资源规划至关重要。本研究利用利雅得主要医疗机构的常规医院数据，应用机器学习（ML）技术预测COPD入院情况。方法对沙特阿拉伯8家主要医院2022 - 2024年间41544例住院患者进行横断面分析。数据集包括人口统计、临床和医疗保健利用变量。开发并评估了几个ML分类器：逻辑回归、支持向量机、k近邻、决策树、随机森林、梯度增强和XGBoost。主要终点为慢性阻塞性肺病住院。使用准确性、精密度、召回率、f1评分、AUROC和混淆矩阵评估模型性能。SHapley加性解释（SHAP）用于解释模型输出并对特征重要性进行排序。结果随机森林模型的准确率为0.73，精密度为0.70，召回率为0.79，f1得分为0.74，AUROC为0.79，优于其他分类器。SHAP分析确定的关键预测因素包括医院名称、入院人数、合并症和疾病严重程度。性别和季节变化等特征对预测结果的影响最小。SHAP可视化为个人层面的风险贡献提供了可解释的见解。机器学习模型，特别是随机森林模型，在使用常规医院数据预测COPD入院情况方面表现出中等但有希望的能力。通过SHAP模型的可解释性提高了临床相关性，并支持早期识别高危患者。将这些工具整合到医院系统中可以促进主动护理并改善呼吸系统疾病的资源分配。

{"title":"Predicting COPD admissions using machine learning and SHAP: An exploratory multi-hospital study in Riyadh, Saudi Arabia","authors":"Anas Ali Alhur , Jamilu Sani , Mohamed Mustaf Ahmed","doi":"10.1016/j.ibmed.2025.100312","DOIUrl":"10.1016/j.ibmed.2025.100312","url":null,"abstract":"<div><h3>Background</h3><div>Chronic obstructive pulmonary disease (COPD) is a leading cause of hospitalization and mortality globally, placing a substantial burden on healthcare systems. In Saudi Arabia, COPD admissions are rising due to demographic shifts and environmental exposures. Accurate prediction of COPD-related hospitalizations is essential for timely intervention and resource planning. This study applied machine learning (ML) techniques to predict COPD admissions using routine hospital data from major healthcare facilities in Riyadh.</div></div><div><h3>Methods</h3><div>A cross-sectional analysis was conducted using 41,544 patient admission records from eight major hospitals in Saudi Arabia between 2022 and 2024. The dataset included demographic, clinical, and healthcare utilization variables. Several ML classifiers: Logistic Regression, Support Vector Machine, K-Nearest Neighbors, Decision Tree, Random Forest, Gradient Boosting, and XGBoost, were developed and evaluated. The primary outcome was inpatient admission for COPD. Model performance was assessed using accuracy, precision, recall, F1-score, AUROC, and confusion matrices. SHapley Additive exPlanations (SHAP) were used to interpret model outputs and rank feature importance.</div></div><div><h3>Results</h3><div>The Random Forest model outperformed other classifiers with an accuracy of 0.73, precision of 0.70, recall of 0.79, F1-score of 0.74, and AUROC of 0.79. Key predictors identified by SHAP analysis included hospital name, admission count, comorbid conditions, and disease severity. Features such as gender and seasonal variation showed minimal influence on prediction outcomes. SHAP visualizations provided interpretable insights into individual-level risk contributions.</div></div><div><h3>Conclusion</h3><div>Machine learning models, particularly Random Forest, demonstrated moderate but promising capacity for predicting COPD admissions using routine hospital data. Model interpretability through SHAP enhances clinical relevance and supports early identification of high-risk patients. Integration of these tools into hospital systems may facilitate proactive care and improve resource allocation for respiratory conditions.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"12 ","pages":"Article 100312"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145519505","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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