Informatics in Medicine Unlocked最新文献_第3页

Learning unbiased risk prediction based algorithms in healthcare: A case study with primary care patients 医疗保健中基于无偏风险预测算法的学习：初级保健患者的案例研究

Informatics in Medicine Unlocked Pub Date : 2025-01-01 DOI: 10.1016/j.imu.2025.101627

Vibhuti Gupta , Julian Broughton , Ange Rukundo , Lubna J. Pinky

{"title":"Learning unbiased risk prediction based algorithms in healthcare: A case study with primary care patients","authors":"Vibhuti Gupta , Julian Broughton , Ange Rukundo , Lubna J. Pinky","doi":"10.1016/j.imu.2025.101627","DOIUrl":"10.1016/j.imu.2025.101627","url":null,"abstract":"<div><div>The proliferation of Artificial Intelligence (AI) has revolutionized the healthcare domain with technological advancements in conventional diagnosis and treatment methods. These advancements lead to faster disease detection, and management and provide personalized healthcare solutions. However, most of the clinical AI methods developed and deployed in hospitals have algorithmic and data-driven biases due to insufficient representation of specific race, gender, and age group which leads to misdiagnosis, disparities, and unfair outcomes. Thus, it is crucial to thoroughly examine these biases and develop computational methods that can mitigate biases effectively. This paper critically analyzes this problem by exploring different types of data and algorithmic biases during both pre-processing and post-processing phases to uncover additional, previously unexplored biases in a widely used real-world healthcare dataset of primary care patients. Additionally, effective strategies are proposed to address gender, race, and age biases, ensuring that risk prediction outcomes are equitable and impartial. Through experiments with various machine learning algorithms leveraging the Fairlearn tool, we have identified biases in the dataset, compared the impact of these biases on the prediction performance, and proposed effective strategies to mitigate these biases. Our results demonstrate clear evidence of racial, gender-based, and age-related biases in the healthcare dataset used to guide resource allocation for patients and have profound impact on the prediction performance which leads to unfair outcomes. Thus, it is crucial to implement mechanisms to detect and address unintended biases to ensure a safe, reliable, and trustworthy AI system in healthcare.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"54 ","pages":"Article 101627"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143519066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Using implementation science to develop and deploy an oncology electronic health record 使用实现科学开发和部署肿瘤电子健康记录

Informatics in Medicine Unlocked Pub Date : 2025-01-01 DOI: 10.1016/j.imu.2025.101625

Carla Taramasco , Rene Noel , Gastón Márquez , Diego Robles

{"title":"Using implementation science to develop and deploy an oncology electronic health record","authors":"Carla Taramasco , Rene Noel , Gastón Márquez , Diego Robles","doi":"10.1016/j.imu.2025.101625","DOIUrl":"10.1016/j.imu.2025.101625","url":null,"abstract":"<div><div>The management of oncology clinical processes involves the efficient management of data using electronic clinical records to effectively monitor and treat oncology patients. As the process of treating and monitoring cancer patients involves multiple stakeholders with differing perspectives, the implementation and deployment of oncology clinical registries represent a significant challenge. In this study, we address this complexity by employing a technique that helps translate implementation strategies into requirement identification methods, which are subsequently disseminated throughout the implementation and deployment phases of health information systems. We applied this technique to develop an electronic health record for the national cancer plan in Chile. The findings indicate that six implementation strategies are essential to addressing stakeholder needs, as well as three requirement identification techniques to describe the underlying problem. Furthermore, a study conducted with 27 stakeholders revealed that the perception of the oncology electronic clinical record has considerable acceptance in three critical functionalities related to the clinical process of oncology patient management. The use of implementation science strategies provides an alternative approach to understanding the underlying problem that stakeholders face when they require healthcare technologies.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"53 ","pages":"Article 101625"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143420327","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The love-hate state of mobile device management in healthcare: An international survey 医疗保健领域移动设备管理的爱恨情仇：一项国际调查

Informatics in Medicine Unlocked Pub Date : 2025-01-01 DOI: 10.1016/j.imu.2024.101603

George A. Gellert, Gabriel L. Gellert, Rachel Pickering, Sean P. Kelly

{"title":"The love-hate state of mobile device management in healthcare: An international survey","authors":"George A. Gellert, Gabriel L. Gellert, Rachel Pickering, Sean P. Kelly","doi":"10.1016/j.imu.2024.101603","DOIUrl":"10.1016/j.imu.2024.101603","url":null,"abstract":"<div><h3>Objective</h3><div>To gather insights regarding mobile device fleet deployment, management and security in healthcare delivery organizations (HDOs), including unmet needs and gaps in capabilities, across four nations.</div></div><div><h3>Methods</h3><div>An exploratory online survey of health information technology leaders working in HDOs to gather information about respondents’ organizational deployment of mobile devices as well as existing and needed mobile management capabilities.</div></div><div><h3>Results</h3><div>HDO mobile device losses were high, with 42% reporting average annual loss rates of 11–30%. Reported organizational effectiveness in protecting confidential information on lost mobile devices was low, with 50% of respondents ranking at six or below on a 10-point scale. Perception of end user satisfaction accessing applications/data on mobile devices was low, with 56–60% ranking satisfaction at six or below on a 10-point scale. Less than half of HDOs reported seven core mobile device management capabilities. Reported costs of mobile device information security breach across nations were between $100,000 and $1 million (USD). Respondents estimated aggregate weekly downtime exceeds 500h among 28% in Australia, 49% in Germany, 45% in the UK, and 47% in the US.</div></div><div><h3>Conclusions</h3><div>HDOs reported substantial perceived gaps and challenges in effectively managing mobility. System leaders desire what mobile device workflows add to care delivery, but effectively and efficiently managing a mobile device fleet remains a significant challenge. Mobility management tools are needed to facilitate rapid mobile device authentication, and efficiency of information access, while reducing clinician friction. Existing shared mobile device management solutions can help HDOs reduce costs and improve access security, user experience and workflow flexibility.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"52 ","pages":"Article 101603"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143178372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Glaucoma identification with retinal fundus images using deep learning: Systematic review 利用深度学习识别视网膜眼底图像青光眼：系统综述

Informatics in Medicine Unlocked Pub Date : 2025-01-01 DOI: 10.1016/j.imu.2025.101644

Dulani Meedeniya , Thisara Shyamalee , Gilbert Lim , Pratheepan Yogarajah

引用次数: 0

Enhanced ROI guided deep learning model for Alzheimer’s detection using 3D MRI images 增强的ROI引导深度学习模型用于阿尔茨海默病的三维MRI图像检测

Informatics in Medicine Unlocked Pub Date : 2025-01-01 DOI: 10.1016/j.imu.2025.101650

Israt Jahan Khan , Md. Fahim Bin Amin , Md. Delwar Shahadat Deepu , Hazera Khatun Hira , Asif Mahmud , Anas Mashad Chowdhury , Salekul Islam , Md. Saddam Hossain Mukta , Swakkhar Shatabda , Alzheimer’s Disease Neuroimaging Initiative

{"title":"Enhanced ROI guided deep learning model for Alzheimer’s detection using 3D MRI images","authors":"Israt Jahan Khan , Md. Fahim Bin Amin , Md. Delwar Shahadat Deepu , Hazera Khatun Hira , Asif Mahmud , Anas Mashad Chowdhury , Salekul Islam , Md. Saddam Hossain Mukta , Swakkhar Shatabda , Alzheimer’s Disease Neuroimaging Initiative","doi":"10.1016/j.imu.2025.101650","DOIUrl":"10.1016/j.imu.2025.101650","url":null,"abstract":"<div><div>Alzheimer’s disease is an incurable condition that predominantly affects the human brain, leading to the shrinkage of various brain regions and the disruption of neuronal connections. Current state-of-the-art methods for detecting Alzheimer’s disease using 3D MRI images are resource-intensive and time-consuming. In this paper, we propose a Regions of Interest (ROI)-guided detection paradigm to address these challenges. We employ a 3D ResNet integrated with a Convolutional Block Attention Module (CBAM), demonstrating that emphasising ROIs in brain imaging can substantially reduce both computational expenditure and training time. Our model exhibits robust performance in discriminating Alzheimer’s disease from mild cognitive impairment, achieving an accuracy of 88% across the entire brain and 92% within targeted ROIs on the ADNI dataset. The accuracy on the OASIS dataset is even higher, reaching 98% for all regions and 98.33% for the ROIs. When distinguishing Alzheimer’s disease from cognitively normal individuals, the accuracy improves further, achieving 93.33% for the ROIs on the ADNI dataset and 97.8% on the OASIS dataset. In differentiating cognitively normal individuals from those with mild cognitive impairment, the model attains an accuracy of 88.2% for the ROIs on the ADNI dataset and 98.6% on the OASIS dataset. These findings highlight a notable enhancement in detection accuracy through the utilisation of fewer, yet more salient brain regions, underscoring the efficacy of our ROI-guided approach.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"56 ","pages":"Article 101650"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144124751","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 lightweight classification system for the early detection of diabetic retinopathy 用于糖尿病视网膜病变早期检测的轻量级分类系统

Informatics in Medicine Unlocked Pub Date : 2025-01-01 DOI: 10.1016/j.imu.2025.101655

Ashim Chakraborty, George Wilson, Cristina Luca

引用次数: 0

Novel hybrid intelligence model for early Alzheimer's diagnosis utilizing multimodal biomarker fusion 利用多模态生物标志物融合的新型阿尔茨海默病早期诊断混合智能模型

Informatics in Medicine Unlocked Pub Date : 2025-01-01 DOI: 10.1016/j.imu.2025.101668

Shehu Mohammed , Neha Malhotra , Arun Singh , Awad M. Awadelkarim , Shakeel Ahmed , Saiprasad Potharaju

{"title":"Novel hybrid intelligence model for early Alzheimer's diagnosis utilizing multimodal biomarker fusion","authors":"Shehu Mohammed , Neha Malhotra , Arun Singh , Awad M. Awadelkarim , Shakeel Ahmed , Saiprasad Potharaju","doi":"10.1016/j.imu.2025.101668","DOIUrl":"10.1016/j.imu.2025.101668","url":null,"abstract":"<div><div>One of the significant causes of dementia and a leading peril to global public health is Alzheimer's disease (AD), which calls for early and accurate diagnosis. The paper proposes a novel hybrid machine learning framework that integrates Gradient Boosting Machine (GBM) and Deep Neural Networks (DNN) for predicting Alzheimer's disease from multimodal biomarkers. The database comprises 35 demographic, behavioral, and clinical features. Feature selection procedures produced key predicting variables (i.e., MMSE scores, performance in Activities of Daily Living (ADL), cholesterol level, and behavior problems). A hybrid model was created by combining individual models, and it proved to be the most effective compared to particular models, achieving 92.6 % accuracy and a 0.94 AUC score on the database. The synergy between the capability of GBM for tabular data and the ability of DNN for complex interaction gives a good outcome. The research demonstrates the efficacy of blending machine learning techniques for supporting Alzheimer's disease (AD) identification and provides a method for early identification at a broader level. It is hoped that more biomarkers will be incorporated, and the model will be validated on larger and more phenotypically diverse databases to achieve clinical usability and generalizability.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"57 ","pages":"Article 101668"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144563334","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

WAE-DTI: Ensemble-based architecture for drug–target interaction prediction using descriptors and embeddings WAE-DTI：基于集成的基于描述符和嵌入的药物-靶标相互作用预测体系结构

Informatics in Medicine Unlocked Pub Date : 2025-01-01 DOI: 10.1016/j.imu.2024.101604

Tariq Sha’ban, Ahmad M. Mustafa, Mostafa Z. Ali

{"title":"WAE-DTI: Ensemble-based architecture for drug–target interaction prediction using descriptors and embeddings","authors":"Tariq Sha’ban, Ahmad M. Mustafa, Mostafa Z. Ali","doi":"10.1016/j.imu.2024.101604","DOIUrl":"10.1016/j.imu.2024.101604","url":null,"abstract":"<div><div>Drug Target Interaction (DTI) prediction is one of the main challenges in the pharmaceutical and drug discovery domains due to its high costs, time-consuming nature, and complexity of manual experiments required to evaluate interactions between large numbers of drugs and targets. In addition, a single drug can bind to multiple targets. In contrast, a single target can also bind to a number of drugs; this complicates the DTI task. Existing silico models often struggle with these challenges, particularly in managing diverse datasets. To address these issues, we introduce the Weighted Average Ensemble Drug–Target Interaction (WAE-DTI) model. Our approach integrates several descriptors and fingerprint representations to enhance prediction accuracy and generalization, namely atom pair fingerprint, Avalon, MACCS, MH, Morgan, RDKit, SEC, topological torsion, and LDP for drug representation, and ESM-2 for target representation. WAE-DTI employs a weighted average ensemble technique to handle diverse datasets effectively. The model demonstrates significant improvements over state-of-the-art methods, achieving an average mean squared error of 0.190 ±0.001 on the Davis dataset, 0.127 ±0.001 on Kiba, 0.143 ±0.001 on DTC, 0.284 ±0.004 on Metz, 0.308 ±0.001 on ToxCast, and 0.934 ±0.004 on STITCH. As for the classification task, WAE-DTI outperforms existing models with an AUPRC of 0.943 ±0.001 on BioSNAP, 0.474 ±0.011 on Davis, and 0.707 ±0.005 on BindingDB. Our code is publicly available at <span><span><sup>1</sup></span></span>.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"52 ","pages":"Article 101604"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143178371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Do transformers generalise better than bespoke tools for anonymisation? 变形金刚比定制的匿名化工具更能泛化吗？

Informatics in Medicine Unlocked Pub Date : 2025-01-01 DOI: 10.1016/j.imu.2024.101607

Roman Klapaukh , Carol El-Hayek , Douglas IR Boyle

引用次数: 0

Brain tumor classification using a hybrid ensemble of Xception and parallel deep CNN models 使用异常和并行深度CNN模型混合集成的脑肿瘤分类

Informatics in Medicine Unlocked Pub Date : 2025-01-01 DOI: 10.1016/j.imu.2025.101629

Seoyoung Yoon

{"title":"Brain tumor classification using a hybrid ensemble of Xception and parallel deep CNN models","authors":"Seoyoung Yoon","doi":"10.1016/j.imu.2025.101629","DOIUrl":"10.1016/j.imu.2025.101629","url":null,"abstract":"<div><h3>Objective</h3><div>Accurate classification of brain tumors is essential for effective diagnosis and treatment planning. The purpose of this study is to develop and evaluate a hybrid ensemble brain tumor classification method to leverage the strengths of two different architectures for improving the accuracy, robustness, and reliability of brain tumor classification.</div></div><div><h3>Methodology</h3><div>This study introduces a novel and innovative classifier that concatenates the Xception convolutional neural network (CNN) with kernel size of (3,3) and a parallel deep CNN (PDCNN) with kernel size of (5,5) and (12,12) to classify brain tumor images from the Kaggle dataset into four categories: meningioma, glioma, pituitary, and no tumor.</div></div><div><h3>Results</h3><div>The Xception model alone achieved a classification accuracy of 98.26 %, while the PDCNN model achieved 94.85 % on the same dataset. By concatenating these two models, the proposed hybrid ensemble approach enhanced overall classification accuracy to 99.09 %. In comparison with state-of-the-art models, VGG19 achieved an accuracy of 94.69 %, while ResNet152V2 achieved 96.27 % on the same dataset. The proposed hybrid ensemble model with Xception and PDCNN consistently outperformed both VGG19 and ResNet152V2.</div></div><div><h3>Conclusion</h3><div>This synergy of concatenating the Xception and PDCNN architectures demonstrates the innovativeness and effectiveness of leveraging complementary strengths in feature extraction and classification, leading to enhanced performance in brain tumor detection. The results highlight the potential of ensemble deep learning models in advancing automated medical image analysis and improving clinical outcomes.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"54 ","pages":"Article 101629"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143600465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0