Intelligence-based medicine最新文献_第8页

Predicting patient no-shows using machine learning: A comprehensive review and future research agenda 利用机器学习预测患者未就诊情况：全面回顾与未来研究议程

Intelligence-based medicine

Pub Date : 2025-01-01 DOI: 10.1016/j.ibmed.2025.100229

Khaled M. Toffaha , Mecit Can Emre Simsekler , Mohammed Atif Omar , Imad ElKebbi

Patient no-shows for scheduled medical appointments pose significant challenges to healthcare systems, resulting in wasted resources, increased costs, and disrupted continuity of care. This comprehensive review examines state-of-the-art machine learning (ML) approaches for predicting patient no-shows in outpatient settings, analyzing 52 publications from 2010 to 2025.

The study reveals significant advancements in the field, with Logistic Regression (LR) as the most commonly used model in 68% of the studies. Tree-based models, ensemble methods, and deep learning techniques have gained traction in recent years, reflecting the field’s evolution. The best-performing models achieved Area Under the Curve (AUC) scores between 0.75 and 0.95, with accuracy ranging from 52% to 99.44%. Methodologically, researchers addressed common challenges such as class imbalance using various sampling techniques and employed a wide range of feature selection methods to improve model efficiency. The review also highlighted the importance of considering temporal factors and the context-dependent nature of no-show behavior across different healthcare settings.

Using the ITPOSMO framework (Information, Technology, Processes, Objectives, Staffing, Management, and Other Resources), the study identified several gaps in current ML approaches. Key challenges include data quality and completeness, model interpretability, and integration with existing healthcare systems. Future research directions include improving data collection methods, incorporating organizational factors, ensuring ethical implementation, and developing standardized approaches for handling data imbalance. The review also suggests exploring new data sources, utilizing ML algorithms to analyze patient behavior patterns, and using transfer learning techniques to adapt models across different healthcare facilities.

By addressing these challenges, healthcare providers can leverage ML to improve resource allocation, enhance patient care quality, and advance predictive analytics in healthcare. This comprehensive review underscores the potential of ML in predicting no-shows while acknowledging the complexities and challenges in its practical implementation.

病人爽约给医疗保健系统带来了巨大挑战，导致资源浪费、成本增加和护理连续性中断。本综述分析了 2010 年至 2025 年间发表的 52 篇论文，探讨了用于预测门诊患者爽约情况的最先进的机器学习（ML）方法。研究显示，该领域取得了重大进展，在 68% 的研究中，逻辑回归（LR）是最常用的模型。基于树的模型、集合方法和深度学习技术在近几年得到了广泛应用，反映了该领域的发展。表现最好的模型的曲线下面积（AUC）得分在 0.75 到 0.95 之间，准确率在 52% 到 99.44% 之间。在方法上，研究人员利用各种采样技术解决了类不平衡等常见难题，并采用了多种特征选择方法来提高模型效率。该综述还强调了考虑时间因素和不同医疗环境中缺席行为的环境依赖性的重要性。利用 ITPOSMO 框架（信息、技术、流程、目标、人员配备、管理和其他资源），该研究确定了当前 ML 方法中的几个差距。主要挑战包括数据质量和完整性、模型可解释性以及与现有医疗保健系统的集成。未来的研究方向包括改进数据收集方法、纳入组织因素、确保符合道德规范的实施，以及开发处理数据不平衡的标准化方法。该综述还建议探索新的数据源，利用 ML 算法分析患者行为模式，并使用迁移学习技术在不同的医疗机构间调整模型。通过应对这些挑战，医疗机构可以利用 ML 改善资源分配，提高患者护理质量，并推进医疗领域的预测分析。这篇全面的综述强调了人工智能在预测病例缺席方面的潜力，同时也承认了其实际应用中的复杂性和挑战。

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引用次数: 0

Hybrid vision transformer and Xception model for reliable CT-based ovarian neoplasms diagnosis 基于ct的卵巢肿瘤可靠诊断的混合视觉变换器和异常模型

Intelligence-based medicine

Pub Date : 2025-01-01 DOI: 10.1016/j.ibmed.2025.100227

Eman Hussein Alshdaifat , Hasan Gharaibeh , Amer Mahmoud Sindiani , Rola Madain , Asma'a Mohammad Al-Mnayyis , Hamad Yahia Abu Mhanna , Rawan Eimad Almahmoud , Hanan Fawaz Akhdar , Mohammad Amin , Ahmad Nasayreh , Raneem Hamad

Ovarian cancer is a major global health concern, characterized by high mortality rates and a lack of accurate diagnostic methods. Rapid and accurate detection of ovarian cancer is essential to improve patient outcomes and formulate appropriate treatment protocols. Medical imaging methods are essential for identifying ovarian cancer; however, achieving accurate diagnosis remains a challenge. This paper presents a robust methodology for ovarian cancer detection, including the identification and classification of benign and malignant tumors, using the Xception_ViT model. This hybrid approach was chosen because it combines the advantages of traditional CNN-based models (such as Xception) with the capabilities of modern Transformers-based models (such as ViT). This combination allows the model to take advantage of Xception, which extracts features from images. The Vision Transformer (ViT) model is then used to identify connections between diverse visual elements, enhancing the model's understanding of complex components. A Multi-Layer Perceptron (MLP) layer is finally integrated with the proposed model for image classification. The effectiveness of the model is evaluated using three computed tomography (CT) image datasets from King Abdullah University Hospital (KAUH) in Jordan. The first dataset consists of the ovarian cancer computed tomography dataset (KAUH-OCCTD), the second is the benign ovarian tumors dataset (KAUH-BOTD), and the third is the malignant ovarian tumors dataset (KAUH-MOTD). The three datasets collected from 500 women are characterized by their diversity in ovarian tumor classification and are the first of their kind collected in Jordan. The proposed model Xception_ViT achieved an accuracy of 98.09 % in identifying ovarian cancer on the KAUH-OCCTD dataset, and an accuracy of 96.05 % and 98.73 % on the KAUH-BOTD and KAUH-MOTD datasets, respectively, in distinguishing between benign and malignant ovarian tumors. The proposed model outperformed the pre-trained models on all three datasets. The results demonstrate that the proposed model can classify ovarian tumors. This method could also greatly enhance the efficiency of novice radiologists in evaluating ovarian malignancies and assist gynecologists in providing improved treatment alternatives for these individuals.

卵巢癌是一个主要的全球健康问题，其特点是死亡率高，缺乏准确的诊断方法。快速准确地检测卵巢癌对于改善患者预后和制定适当的治疗方案至关重要。医学影像学方法是鉴别卵巢癌的必要手段；然而，实现准确的诊断仍然是一个挑战。本文提出了一种强大的卵巢癌检测方法，包括使用Xception_ViT模型对良性和恶性肿瘤进行识别和分类。之所以选择这种混合方法，是因为它结合了传统的基于cnn的模型（如Xception）的优势和现代基于transformer的模型（如ViT）的能力。这种组合允许模型利用Xception，它从图像中提取特征。然后使用视觉转换器（Vision Transformer, ViT）模型来识别不同视觉元素之间的联系，增强模型对复杂组件的理解。最后将多层感知器（MLP）层与所提出的图像分类模型相结合。使用约旦阿卜杜拉国王大学医院（KAUH）的三个计算机断层扫描（CT）图像数据集评估该模型的有效性。第一个数据集包括卵巢癌计算机断层扫描数据集（KAUH-OCCTD），第二个数据集是良性卵巢肿瘤数据集（KAUH-BOTD），第三个数据集是恶性卵巢肿瘤数据集（KAUH-MOTD）。从500名妇女中收集的三个数据集以其卵巢肿瘤分类的多样性为特征，是约旦首次收集此类数据集。所提出的Xception_ViT模型在KAUH-OCCTD数据集上识别卵巢癌的准确率为98.09%，在KAUH-BOTD和KAUH-MOTD数据集上区分卵巢良恶性肿瘤的准确率分别为96.05%和98.73%。提出的模型在所有三个数据集上都优于预训练模型。结果表明，该模型能够对卵巢肿瘤进行分类。该方法还可以大大提高新手放射科医生评估卵巢恶性肿瘤的效率，并协助妇科医生为这些个体提供改进的治疗方案。

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引用次数: 0

Features and eigenspectral densities analyses for machine learning and classification of severities in chronic obstructive pulmonary diseases 慢性阻塞性肺疾病中机器学习和严重程度分类的特征和特征谱密度分析

Intelligence-based medicine

Pub Date : 2025-01-01 DOI: 10.1016/j.ibmed.2025.100217

Timothy Albiges, Zoheir Sabeur, Banafshe Arbab-Zavar

Chronic Obstructive Pulmonary Disease (COPD) has been presenting highly significant global health challenges for many decades. Equally, it is important to slow down this disease's ever-increasingly challenging impact on hospital patient loads. It has become necessary, if not critical, to capitalise on existing knowledge of advanced artificial intelligence to achieve the early detection of COPD and advance personalised care of COPD patients from their homes. The use of machine learning and reaching out on the classification of the multiple types of COPD severities effectively and at progressively acceptable levels of confidence is of paramount importance. Indeed, this capability will feed into highly effective personalised care of COPD patients from their homes while significantly improving their quality of life.

Auscultation lung sound analysis has emerged as a valuable, non-invasive, and cost-effective remote diagnostic tool of the future for respiratory conditions such as COPD. This research paper introduces a novel machine learning-based approach for classifying multiple COPD severities through the analysis of lung sound data streams. Leveraging two open datasets with diverse acoustic characteristics and clinical manifestations, the research study involves the transformation and decomposition of lung sound data matrices into their eigenspace representation in order to capture key features for machine learning and detection. Early eigenvalue spectra analyses were also performed to discover their distinct manifestations under the multiple established COPD severities. This has led us into projecting our experimental data matrices into their eigenspace with the use of the manifested data features prior to the machine learning process. This was followed by various methods of machine classification of COPD severities successfully. Support Vector Classifiers, Logistic Regression, Random Forests and Naive Bayes Classifiers were deployed. Systematic classifier performance metrics were also adopted; they showed early promising classification accuracies beyond 75 % for distinguishing COPD severities.

This research benchmark contributes to computer-aided medical diagnosis and supports the integration of auscultation lung sound analyses into COPD assessment protocols for individualised patient care and treatment. Future work involves the acquisition of larger volumes of lung sound data while also exploring multi-modal sensing of COPD patients for heterogeneous data fusion to advance COPD severity classification performance.

几十年来，慢性阻塞性肺疾病（COPD）一直是全球健康面临的重大挑战。同样，减缓这种疾病对医院病人负荷的日益严峻的影响也很重要。利用现有的先进人工智能知识来实现COPD的早期发现，并在家中推进COPD患者的个性化护理，即使不是至关重要，也是必要的。机器学习的使用和对多种类型COPD严重程度的有效分类以及逐步可接受的置信度水平的接触是至关重要的。事实上，这种能力将有助于在家中为COPD患者提供高效的个性化护理，同时显著改善他们的生活质量。听诊肺音分析已成为一种有价值的、无创的、具有成本效益的远程诊断工具，用于未来的呼吸系统疾病，如慢性阻塞性肺病。本文介绍了一种新的基于机器学习的方法，通过分析肺声数据流来分类多种COPD严重程度。利用两个具有不同声学特征和临床表现的开放数据集，该研究涉及将肺音数据矩阵转换和分解为其特征空间表示，以捕获用于机器学习和检测的关键特征。还进行了早期特征值谱分析，以发现其在多个已确定的COPD严重程度下的不同表现。这导致我们在机器学习过程之前使用已显示的数据特征将实验数据矩阵投影到它们的特征空间中。随后，各种COPD严重程度的机器分类方法都取得了成功。使用了支持向量分类器、逻辑回归、随机森林和朴素贝叶斯分类器。采用系统分类器性能指标；在区分COPD严重程度方面，他们显示出早期有希望的分类准确率超过75%。该研究基准有助于计算机辅助医疗诊断，并支持将听诊肺音分析整合到COPD评估方案中，以实现个体化患者护理和治疗。未来的工作包括获取更大量的肺声数据，同时探索COPD患者的多模式感知，以进行异构数据融合，以提高COPD严重程度分类性能。

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引用次数: 0

Predicting maternal health risk using PCA-enhanced XGBoost and SMOTE-ENN for improved healthcare outcomes 使用pca增强的XGBoost和SMOTE-ENN预测孕产妇健康风险，以改善医疗保健结果

Intelligence-based medicine

Pub Date : 2025-01-01 DOI: 10.1016/j.ibmed.2025.100300

Rahmatul Kabir Rasel Sarker , Sadman Hafij , Md Adib Yasir , Md Assaduzzaman , Md Monir Hossain Shimul , Md Kamrul Hossain

Background

Maternal health remains a global priority, especially in low-resource settings where timely risk identification is critical. Traditional machine learning models often suffer from poor generalizability, data imbalance, and computational inefficiencies. This study proposes an enhanced predictive model combining SMOTE-ENN data balancing and Principal Component Analysis (PCA) with XGBoost to improve maternal risk classification accuracy using minimal, easily collectible clinical features.

Methods

The dataset of 1014 maternal health records comprising seven physiological features was sourced from a public repository. Preprocessing involved standardization, label encoding, and class balancing using SMOTE-ENN. PCA was applied for dimensionality reduction to enhance computational performance and reduce overfitting. Several machine learning classifiers including Decision Tree, Random Forest, LightGBM, Gradient Boosting, and SVM were evaluated, with XGBoost selected as the final model. Performance metrics included accuracy, precision, recall, F1-score, ROC-AUC, and 10-fold cross-validation.

Results

The PCA-enhanced XGBoost model achieved the highest accuracy (97.73 %), precision (98 %), recall (98 %), and F1-score (98 %). It outperformed all other models, particularly in identifying high-risk cases with minimal false negatives. Cross-validation confirmed the model's robustness (mean accuracy: 98.39 %), and ROC-AUC scores exceeded 0.998 for all classes, indicating near-perfect classification performance.

Conclusion

This study validates a maternal health risk prediction model that is scalable for use in resource-constrained environments and interpretable within the limitations of the selected dimensionality-reduction approach. Its simplicity, high accuracy, and generalizability make it a promising tool for early clinical decision-making and intervention.

产妇保健仍然是全球优先事项，特别是在资源匮乏的环境中，及时识别风险至关重要。传统的机器学习模型通常存在泛化能力差、数据不平衡和计算效率低下的问题。本研究提出了一个增强的预测模型，结合SMOTE-ENN数据平衡和主成分分析（PCA）与XGBoost，利用最小的、易于收集的临床特征来提高孕产妇风险分类的准确性。方法从公共信息库中获取1014份孕产妇健康记录，包括7项生理特征。预处理包括使用SMOTE-ENN进行标准化、标签编码和类平衡。采用主成分分析法进行降维，提高计算性能，减少过拟合。对决策树、随机森林、LightGBM、梯度增强和支持向量机等几种机器学习分类器进行了评估，最终选择XGBoost作为最终模型。性能指标包括准确性、精密度、召回率、f1评分、ROC-AUC和10倍交叉验证。结果pca增强的XGBoost模型具有最高的准确率（97.73%）、精密度（98%）、召回率（98%）和f1评分（98%）。它优于所有其他模型，特别是在识别高风险病例时，以最小的假阴性。交叉验证证实了模型的稳健性（平均准确率为98.39%），所有类别的ROC-AUC得分均超过0.998，表明分类性能接近完美。结论：本研究验证了一种产妇健康风险预测模型，该模型可扩展用于资源受限环境，并可在所选降维方法的限制下解释。它的简单，高精度和可推广性使其成为早期临床决策和干预的有前途的工具。

{"title":"Predicting maternal health risk using PCA-enhanced XGBoost and SMOTE-ENN for improved healthcare outcomes","authors":"Rahmatul Kabir Rasel Sarker , Sadman Hafij , Md Adib Yasir , Md Assaduzzaman , Md Monir Hossain Shimul , Md Kamrul Hossain","doi":"10.1016/j.ibmed.2025.100300","DOIUrl":"10.1016/j.ibmed.2025.100300","url":null,"abstract":"<div><h3>Background</h3><div>Maternal health remains a global priority, especially in low-resource settings where timely risk identification is critical. Traditional machine learning models often suffer from poor generalizability, data imbalance, and computational inefficiencies. This study proposes an enhanced predictive model combining SMOTE-ENN data balancing and Principal Component Analysis (PCA) with XGBoost to improve maternal risk classification accuracy using minimal, easily collectible clinical features.</div></div><div><h3>Methods</h3><div>The dataset of 1014 maternal health records comprising seven physiological features was sourced from a public repository. Preprocessing involved standardization, label encoding, and class balancing using SMOTE-ENN. PCA was applied for dimensionality reduction to enhance computational performance and reduce overfitting. Several machine learning classifiers including Decision Tree, Random Forest, LightGBM, Gradient Boosting, and SVM were evaluated, with XGBoost selected as the final model. Performance metrics included accuracy, precision, recall, F1-score, ROC-AUC, and 10-fold cross-validation.</div></div><div><h3>Results</h3><div>The PCA-enhanced XGBoost model achieved the highest accuracy (97.73 %), precision (98 %), recall (98 %), and F1-score (98 %). It outperformed all other models, particularly in identifying high-risk cases with minimal false negatives. Cross-validation confirmed the model's robustness (mean accuracy: 98.39 %), and ROC-AUC scores exceeded 0.998 for all classes, indicating near-perfect classification performance.</div></div><div><h3>Conclusion</h3><div>This study validates a maternal health risk prediction model that is scalable for use in resource-constrained environments and interpretable within the limitations of the selected dimensionality-reduction approach. Its simplicity, high accuracy, and generalizability make it a promising tool for early clinical decision-making and intervention.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"12 ","pages":"Article 100300"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145219043","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}

引用次数: 0

Generative AI and scientific manuscript peer review 生成人工智能和科学手稿同行评审

Intelligence-based medicine

Pub Date : 2025-01-01 DOI: 10.1016/j.ibmed.2025.100246

Robert Hoyt MD (Associate Clinical Professor), Alfonso Limon PhD (Senior Data Scientist), Anthony Chang MD (Chief Intelligence and Innovation Officer)

引用次数: 0

Attention-driven graph-based machine learning for non-invasive diagnosis of NAFLD 基于注意力驱动图的机器学习在非侵入性NAFLD诊断中的应用

Intelligence-based medicine

Pub Date : 2025-01-01 DOI: 10.1016/j.ibmed.2025.100288

Ekta Srivastava , Sarath Mohan , Tapan Kumar Gandhi , Ashok Kumar Choudhury , Sandeep Kumar

An estimated 25%–30% of the global population is affected by non-alcoholic fatty liver disease (NAFLD), a silent yet progressive condition that can advance from simple steatosis to severe stages like non-alcoholic steatohepatitis (NASH), fibrosis, and cirrhosis, significantly heightening the risk of liver cancer. Currently, the gold-standard method for staging NAFLD is liver biopsy, an invasive procedure with risks such as bleeding, infection, and sampling error. Due to its high cost and impracticality for routine monitoring, there is a critical need for reliable, non-invasive diagnostic tools capable of effectively identifying NAFLD stages. We developed a graph-based framework in which each patient is represented as a node in a similarity network. Edges are formed via k-nearest neighbors (KNN) on standardized clinical and biochemical features, with missing values imputed by KNN to preserve biologically plausible variability. A two-layer Graph Attention Network (GAT) then learns edge-specific attention weights to focus on the most informative inter-patient relationships. Tested on a proprietary ILBS cohort (n = 622), our model achieved 75.2% accuracy (AUC = 0.768; F1 = 0.752), an 11% absolute improvement over Support Vector Machines and Random Forests, and demonstrated robustness in 10-fold cross-validation and adversarial noise tests. On a separate public dataset (n = 80) spanning lipidomic, glycomic, fatty acid, and hormone panels, it exceeded 99% accuracy (AUC

>

0.99). Attention-based explanations further highlighted key patient similarities driving each prediction. These findings suggest that attention-driven graph learning can clearly improve non-invasive NAFLD staging, enabling early detection and supporting personalized disease monitoring in diverse clinical settings.

据估计，全球25%-30%的人口受到非酒精性脂肪性肝病（NAFLD）的影响，这是一种沉默但进展的疾病，可从单纯的脂肪变性发展到严重阶段，如非酒精性脂肪性肝炎（NASH）、纤维化和肝硬化，显著增加了肝癌的风险。目前，NAFLD分期的金标准方法是肝活检，这是一种侵入性手术，存在出血、感染和抽样错误等风险。由于其高成本和常规监测的不实用性，迫切需要能够有效识别NAFLD分期的可靠、非侵入性诊断工具。我们开发了一个基于图形的框架，其中每个患者都表示为相似网络中的节点。边缘是通过标准化临床和生化特征的k近邻（KNN）形成的，缺失值由KNN输入以保持生物学上合理的可变性。然后，两层图注意网络（GAT）学习边缘特定注意权重，以关注最具信息量的患者间关系。在专有的ILBS队列（n = 622）上进行测试，我们的模型达到了75.2%的准确率（AUC = 0.768; F1 = 0.752），比支持向量机和随机森林提高了11%，并在10倍交叉验证和对抗噪声测试中显示出鲁棒性。在一个独立的公共数据集（n = 80）上，包括脂质组、糖糖组、脂肪酸组和激素组，准确率超过99% （AUC > 0.99）。基于注意力的解释进一步强调了驱动每种预测的关键患者相似性。这些发现表明，注意力驱动的图学习可以明显改善非侵入性NAFLD的分期，使早期发现成为可能，并在不同的临床环境中支持个性化的疾病监测。

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引用次数: 0

Privacy-aware and interpretable deep learning framework for dental caries classification 隐私感知和可解释的龋齿分类深度学习框架

Intelligence-based medicine

Pub Date : 2025-01-01 DOI: 10.1016/j.ibmed.2025.100294

Jashvant Kumar , Khaled Mohamad Almustafa , Rand Madanat , Akhilesh Kumar Sharma , Muhammed Sutcu , Juliano Katrib

Dental caries remains one of the most prevalent and persistent chronic diseases globally, affecting individuals across all age groups and posing a significant burden on public health systems. Early detection is critical to prevent the progression of tooth decay, reduce treatment complexity, and improve long-term oral health outcomes. In response to these clinical demands, this study presents a comprehensive, privacy-aware, and interpretable deep learning framework for the automated classification of dental caries from X-ray images. The approach addresses the issues of class imbalance, low Resolution image and privacy preserved patient's medical images.The framework is structured into three progressive phases that incorporate supervised learning through Convolutional Neural Networks (CNN), ResNet-18, and DenseNet; unsupervised clustering using Principal Component Analysis (PCA); and a decentralized federated learning strategy to ensure secure model training across distributed datasets. The experimental dataset consists of 957 labelled dental radiographs, including 174 healthy and 783 carious cases, emphasizing the issue of class imbalance. Initial baseline models achieved an accuracy of 84 %, which improved to 96 % following strategic data augmentation and class balancing interventions. PCA-based clustering visualizations revealed well-separated clusters (Silhouette Score: 0.6660), confirming the discriminative power of the selected features. Meanwhile, the federated learning implementation preserved data confidentiality without sacrificing performance, reinforcing the model's suitability for real-world clinical deployment. Collectively, these findings validate the framework's robustness, interpretability, and adaptability, offering a scalable and ethically aligned solution for AI-driven dental diagnostics in modern healthcare systems.

龋齿仍然是全球最普遍和最持久的慢性疾病之一，影响所有年龄组的个体，并对公共卫生系统构成重大负担。早期发现对于防止蛀牙恶化、减少治疗复杂性和改善长期口腔健康结果至关重要。为了响应这些临床需求，本研究提出了一个全面的、隐私意识的、可解释的深度学习框架，用于从x射线图像中自动分类龋齿。该方法解决了分类不平衡、图像分辨率低和患者医学图像隐私保护等问题。该框架分为三个渐进阶段，包括通过卷积神经网络（CNN）、ResNet-18和DenseNet进行监督学习；基于主成分分析（PCA）的无监督聚类；以及分散的联邦学习策略，以确保跨分布式数据集的安全模型训练。实验数据集由957张标记的牙科x光片组成，其中包括174张健康病例和783张龋齿病例，强调了类别不平衡的问题。初始基线模型的准确率为84%，在策略数据增强和班级平衡干预后提高到96%。基于pca的聚类可视化显示了分离良好的聚类（剪影得分：0.6660），证实了所选特征的判别能力。同时，联邦学习实现在不牺牲性能的情况下保护了数据机密性，增强了模型对现实世界临床部署的适用性。总的来说，这些发现验证了框架的稳健性、可解释性和适应性，为现代医疗保健系统中人工智能驱动的牙科诊断提供了可扩展和符合道德的解决方案。

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引用次数: 0

Exploring the intersection of cochlear implants and artificial intelligence: A mixed-method systematic and scoping review 探索人工耳蜗与人工智能的交叉：一种混合方法的系统和范围综述

Intelligence-based medicine

Pub Date : 2025-01-01 DOI: 10.1016/j.ibmed.2025.100296

Aurenzo Gonçalves Mocelin , Pedro Angelo Basei de Paula , Daniel Tiepolo Kochinski , Thayná Cristina Wiezbicki , Rogério de Azevedo Hamerschmidt , Mayara Risnei Watanabe , Rogério Hamerschmidt

Objective

This study systematically evaluates the role of artificial intelligence (AI) in cochlear implant (CI) technology, focusing on speech enhancement, automated fitting, AI-assisted surgery, predictive modeling, and rehabilitation. The review identifies key advancements, existing limitations, and areas for future development.

Methods

Following PRISMA guidelines, we conducted a systematic search across PubMed, IEEE Xplore, Scopus, ScienceDirect, and Embase. We included peer-reviewed primary data studies on AI applications in CIs. The selected studies were categorized into thematic subdomains, such as noise suppression, adaptive programming, AI-driven surgical planning, and telemedicine applications.

Results

From an initial pool of 743 records, 129 studies met the eligibility criteria and were included in the final analysis. These studies were categorized into eleven thematic subdomains. The review identified the main application areas and emerging research fronts at the intersection of artificial intelligence and cochlear implant technologies, including speech enhancement, automated fitting, predictive modeling, rehabilitation support, and AI-assisted surgery.

Discussion and conclusion

AI is transforming CI technology by improving speech perception, personalization, and surgical precision. However, challenges persist, including computational constraints, data heterogeneity, and the need for large-scale clinical validation. Future research should prioritize energy-efficient AI architectures, regulatory approval pathways, and ethical considerations in automated decision-making. Advancing AI-driven telemedicine solutions can expand CI accessibility, reducing the need for in-person programming. Addressing these challenges will accelerate the development of more adaptive and user-centered CI solutions, ultimately enhancing auditory rehabilitation and quality of life for CI users.

目的系统评估人工智能（AI）在人工耳蜗（CI）技术中的作用，重点关注语音增强、自动验配、人工智能辅助手术、预测建模和康复。该审查确定了主要进展、现有限制和未来发展的领域。方法遵循PRISMA指南，我们在PubMed、IEEE explore、Scopus、ScienceDirect和Embase中进行了系统搜索。我们纳入了人工智能在ci中的应用的同行评议的原始数据研究。选定的研究被分类为主题子领域，如噪声抑制、自适应编程、人工智能驱动的手术计划和远程医疗应用。结果从最初的743份记录中，有129项研究符合资格标准，并被纳入最终分析。这些研究分为11个主题子领域。该综述确定了人工智能和人工耳蜗技术交叉的主要应用领域和新兴研究前沿，包括语音增强、自动装配、预测建模、康复支持和人工智能辅助手术。人工智能正在通过提高语音感知、个性化和手术精度来改变CI技术。然而，挑战依然存在，包括计算限制、数据异质性和大规模临床验证的需要。未来的研究应优先考虑节能的人工智能架构、监管审批途径和自动化决策中的道德考虑。推进人工智能驱动的远程医疗解决方案可以扩大CI的可访问性，减少对亲自编程的需求。解决这些挑战将加速开发更具适应性和以用户为中心的CI解决方案，最终提高CI用户的听觉康复和生活质量。

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引用次数: 0

Forecasting pediatric emergency department arrivals: Evaluating the role of exogenous variables using deep learning models 预测儿科急诊科到达：使用深度学习模型评估外生变量的作用

Intelligence-based medicine

Pub Date : 2025-01-01 DOI: 10.1016/j.ibmed.2025.100313

Egbe-Etu Etu , Jordan Larot , Kindness Etu , Joshua Emakhu , Sara Masoud , Imokhai Tenebe , Gaojian Huang , Satheesh Gunaga , Joseph Miller

Background

Forecasting pediatric emergency department (ED) demand remains a critical challenge in healthcare operations. This study aimed to identify exogenous variables influencing pediatric ED visits and evaluate the performance of different forecasting models.

Method

Using a retrospective observational design, we analyzed 192,347 pediatric ED visits across nine hospitals in Southeast Michigan between 2017 and 2019. Patient data were aggregated into daily arrival counts and enriched with exogenous variables such as weather, air quality, pollen, calendar, Google search trends, and chief complaints. Feature selection was performed using XGBoost and SHapley Additive exPlanations to identify the most influential predictors. Three forecasting models were developed: a Naïve baseline, Long Short-Term Memory (LSTM), and an attention-based neural network. The models were evaluated across 1-day, 7-day, and 14-day forecasting horizons using mean absolute percentage error (MAPE) and R² metrics.

Results

LSTM and attention-based model significantly outperformed the Naïve baseline across all horizons. The LSTM model incorporating calendar data achieved the best 1-day forecast (MAPE: 8.71 %, R²: 0.67). For 7-day forecasts, the attention-based model using chief complaint data performed best (MAPE: 9.18 %, R²: 0.57). At 14 days, the attention-based model without exogenous inputs outperformed most LSTM variants, reflecting superior performance in long-range forecasting. Among exogenous variables, calendar and chief complaint data added the most predictive value, while Google Trends and pollen data introduced noise and diminished model performance.

Conclusion

Combining deep learning architectures with selected external data improves pediatric ED arrival forecasting. From an operational perspective, such forecasts can support more efficient staffing, reduce wait times, and mitigate ED crowding.

背景预测儿科急诊科（ED）的需求仍然是医疗保健业务的关键挑战。本研究旨在确定影响儿科急诊科就诊的外生变量，并评估不同预测模型的性能。方法采用回顾性观察设计，分析2017年至2019年密歇根州东南部9家医院的192,347例儿科急诊科就诊情况。患者数据汇总为每日到达计数，并丰富了外生变量，如天气、空气质量、花粉、日历、谷歌搜索趋势和主诉。使用XGBoost和SHapley加性解释进行特征选择，以确定最具影响力的预测因子。开发了三种预测模型：Naïve基线，长短期记忆（LSTM）和基于注意的神经网络。使用平均绝对百分比误差（MAPE）和R2指标对模型进行1天、7天和14天的预测期评估。结果slstm和基于注意力的模型在所有视界上都显著优于Naïve基线。结合日历数据的LSTM模型获得了最好的1天预测（MAPE: 8.71%, R2: 0.67）。对于7天的预测，使用主诉数据的基于注意力的模型表现最好（MAPE: 9.18%, R2: 0.57）。在第14天，没有外源输入的基于注意力的模型优于大多数LSTM变体，反映出在长期预测方面的优越性能。在外源变量中，日历和主诉数据的预测价值最高，而谷歌趋势和花粉数据引入了噪声，降低了模型的性能。结论将深度学习架构与选定的外部数据相结合可以提高儿科急诊科的到来预测。从操作的角度来看，这样的预测可以支持更有效的人员配置，减少等待时间，并缓解急诊科拥挤。

{"title":"Forecasting pediatric emergency department arrivals: Evaluating the role of exogenous variables using deep learning models","authors":"Egbe-Etu Etu , Jordan Larot , Kindness Etu , Joshua Emakhu , Sara Masoud , Imokhai Tenebe , Gaojian Huang , Satheesh Gunaga , Joseph Miller","doi":"10.1016/j.ibmed.2025.100313","DOIUrl":"10.1016/j.ibmed.2025.100313","url":null,"abstract":"<div><h3>Background</h3><div>Forecasting pediatric emergency department (ED) demand remains a critical challenge in healthcare operations. This study aimed to identify exogenous variables influencing pediatric ED visits and evaluate the performance of different forecasting models.</div></div><div><h3>Method</h3><div>Using a retrospective observational design, we analyzed 192,347 pediatric ED visits across nine hospitals in Southeast Michigan between 2017 and 2019. Patient data were aggregated into daily arrival counts and enriched with exogenous variables such as weather, air quality, pollen, calendar, Google search trends, and chief complaints. Feature selection was performed using XGBoost and SHapley Additive exPlanations to identify the most influential predictors. Three forecasting models were developed: a Naïve baseline, Long Short-Term Memory (LSTM), and an attention-based neural network. The models were evaluated across 1-day, 7-day, and 14-day forecasting horizons using mean absolute percentage error (MAPE) and R<sup>2</sup> metrics.</div></div><div><h3>Results</h3><div>LSTM and attention-based model significantly outperformed the Naïve baseline across all horizons. The LSTM model incorporating calendar data achieved the best 1-day forecast (MAPE: 8.71 %, R<sup>2</sup>: 0.67). For 7-day forecasts, the attention-based model using chief complaint data performed best (MAPE: 9.18 %, R<sup>2</sup>: 0.57). At 14 days, the attention-based model without exogenous inputs outperformed most LSTM variants, reflecting superior performance in long-range forecasting. Among exogenous variables, calendar and chief complaint data added the most predictive value, while Google Trends and pollen data introduced noise and diminished model performance.</div></div><div><h3>Conclusion</h3><div>Combining deep learning architectures with selected external data improves pediatric ED arrival forecasting. From an operational perspective, such forecasts can support more efficient staffing, reduce wait times, and mitigate ED crowding.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"12 ","pages":"Article 100313"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145519506","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}

引用次数: 0

A novel pixel pair shuffling based image watermarking for tamper detection and self-recovery 一种基于像素对变换的篡改检测和自恢复图像水印

Intelligence-based medicine

Pub Date : 2025-01-01 DOI: 10.1016/j.ibmed.2025.100324

Radha Ramesh Murapaka , A.V.S. Pavan Kumar , Aditya Kumar Sahu

This work has introduced a novel image watermarking scheme leveraging a pixel pair-based shuffling (PPSh) technique for tamper detection and self-recovery. The proposed technique consists of five steps, initiating from secret bits generation, collectively known as watermark bits. Then, the next step is watermark embedding, after that, watermark extraction, tamper detection, and finally, dual self-recovery approaches have been implemented. For watermark bit generation, two prominent interpolation techniques, such as bipolar and bilinear, are applied to the cover image (CI) to obtain the compressed image. Later, Advanced Encryption Standard (AES) and Camellia with Cipher Block Chaining (CBC) mode of operation is utilized on the compressed image to generate watermark bits. Afterwards, a PPSh-based watermark embedding strategy has been utilized to achieve the watermarked image (WI) while maintaining a standard payload capacity. Further, a variety of image processing attacks is performed on the WI to check the imperceptibility and similarity of the proposed scheme. Consequently, tamper region detection is followed by the watermark extraction procedure. Therefore, to reconstruct the tampered pixels, inpainting based dual recovery approaches are presented, named as TELEA and Naiver-Stokes (NS). The robustness and imperceptibility of the proposed scheme is measured through peak-signal-to-noise ratio (PSNR), structural similarity index matrix (SSIM), and mean square error (MSE). The proposed technique has achieved an average PSNR and SSIM of 54.24 dB and 0.9983, respectively. With an increment of more than 2 dB in terms of PSNR the proposed technique outperforms the existing watermarking techniques. Additionally, the proposed technique obtains a recovery increment up to 5 dB in terms of PSNR for 10 %–50 % tampering rates against the existing methods.

这项工作引入了一种新的图像水印方案，利用基于像素对的洗牌（PPSh）技术进行篡改检测和自我恢复。该技术包括五个步骤，从生成秘密比特（统称为水印比特）开始。然后进行水印嵌入，再进行水印提取、篡改检测，最后实现双自恢复方法。对于水印位的生成，采用双极和双线性两种重要的插值技术对封面图像进行插值，得到压缩后的图像。随后，利用高级加密标准AES （Advanced Encryption Standard）和CBC （Cipher Block chains）操作模式的Camellia在压缩图像上生成水印位。然后，利用基于ppsh的水印嵌入策略，在保持标准载荷容量的情况下实现水印图像。此外，在WI上进行了各种图像处理攻击，以检查所提出方案的不可感知性和相似性。因此，篡改区域检测之后是水印提取程序。因此，为了重建被篡改的像素，提出了基于插值的双重恢复方法，称为TELEA和naver - stokes （NS）。通过峰值信噪比（PSNR）、结构相似指数矩阵（SSIM）和均方误差（MSE）来衡量该方案的鲁棒性和不可感知性。该技术的平均PSNR和SSIM分别为54.24 dB和0.9983。该方法的PSNR增量大于2 dB，优于现有的水印技术。此外，与现有方法相比，该技术在10% - 50%的篡改率下，可获得高达5 dB的PSNR恢复增量。

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引用次数: 0