Healthcare networks prove to be an urgent issue in terms of intrusion detection due to the critical consequences of cyber threats and the extreme sensitivity of medical information.The proposed Auto-Stack ID in the st...Healthcare networks prove to be an urgent issue in terms of intrusion detection due to the critical consequences of cyber threats and the extreme sensitivity of medical information.The proposed Auto-Stack ID in the study is a stacked ensemble of encoder-enhanced auctions that can be used to improve intrusion detection in healthcare networks.TheWUSTL-EHMS 2020 dataset trains and evaluates themodel,constituting an imbalanced class distribution(87.46% normal traffic and 12.53% intrusion attacks).To address this imbalance,the study balances the effect of training Bias through Stratified K-fold cross-validation(K=5),so that each class is represented similarly on training and validation splits.Second,the Auto-Stack ID method combines many base classifiers such as TabNet,LightGBM,Gaussian Naive Bayes,Histogram-Based Gradient Boosting(HGB),and Logistic Regression.We apply a two-stage training process based on the first stage,where we have base classifiers that predict out-of-fold(OOF)predictions,which we use as inputs for the second-stage meta-learner XGBoost.The meta-learner learns to refine predictions to capture complicated interactions between base models,thus improving detection accuracy without introducing bias,overfitting,or requiring domain knowledge of the meta-data.In addition,the auto-stack ID model got 98.41% accuracy and 93.45%F1 score,better than individual classifiers.It can identify intrusions due to its 90.55% recall and 96.53% precision with minimal false positives.These findings identify its suitability in ensuring healthcare networks’security through ensemble learning.Ongoing efforts will be deployed in real time to improve response to evolving threats.展开更多
Introduction:Direct-to-consumer(DTC)telemedicine has emerged as an option for transgender patients seeking gender affirming hormone therapy(GAHT).We aimed to characterize the healthcare services provided by DTC teleme...Introduction:Direct-to-consumer(DTC)telemedicine has emerged as an option for transgender patients seeking gender affirming hormone therapy(GAHT).We aimed to characterize the healthcare services provided by DTC telemedicine companies offering GAHT and to compare their costs to a tertiary care center.Methods:We identified DTC telemedicine platforms offering GAHT via internet searches and extracted information from their websites related to evaluation,treatment,monitoring,and cost.Cost of theDTC GAHT was compared to cost for comparable services at a tertiary care center.Results:Six DTC companies were identified.All platforms utilized an informed consent model without prerequisite mental health evaluation for GAHT.Platforms did not provide comprehensive mental health services.All platforms endorsed the use of regular follow up visits throughout the treatment period although interval of laboratory assessment varied.Cost estimates were comparable for uninsured patients and higher compared to those for insured patients.Cost estimates were lowest with private and public insurance at the tertiary center.Conclusions:DTC telemedicine platforms offering GAHT appear to be in line with the recently released World Professional Association for Transgender Health standards of care regarding the laboratory evaluation and monitoring,but it is unclear whether they are compliant with other recommendations.These platforms offer competitive costs for TGD patients without insurance.展开更多
背景全科医学作为一种新兴医疗模式,强调以社区为基础的全面医疗服务,旨在提高医疗的可及性和效率。然而,尽管全科医学在提升基本医疗卫生服务能力中扮演关键角色,但全科医学研究仍处于发展滞后的状态,需要通过科学研究和社会支持来改...背景全科医学作为一种新兴医疗模式,强调以社区为基础的全面医疗服务,旨在提高医疗的可及性和效率。然而,尽管全科医学在提升基本医疗卫生服务能力中扮演关键角色,但全科医学研究仍处于发展滞后的状态,需要通过科学研究和社会支持来改善这一状况,并提高其学科地位。目的在研究人员、科研机构和学科整体三个自下而上的层级中,分层次地分析影响全科医学领域科研能力发展的因素。方法于2023年12月—2024年3月,本文采用范围综述方法,对中国知网、万方数据知识服务平台、PubMed、Web of Science数据库进行检索,并手动浏览国际全科医学科研和行业学会的信息发布平台,以及谷歌搜索引擎,收集2000—2023年发表的全科医学科研能力发展影响因素的相关文献,并通过手动检索补充灰色文献。通过两阶段的筛选,最终纳入相关文献,使用Excel进行数据整理,归类影响因素,并通过归纳性的主题分析法分析数据,最终以日冕图的形式展示研究结果。结果本研究最终纳入122篇文献,包括原创研究62篇、系统综述2篇、非原创论文54篇、灰色文献4篇,基于文献分析,共归纳出21项影响全科医学科研能力发展的因素。相关因素被分为个体(研究人员)、群体(机构)和整体(学科)三个层级。个体层面的因素涉及研究人员的科研知识和技能、申请资金的能力、对科研的兴趣和积极性、学习科研知识和发展科研合作的机遇、用于科研工作的时间以及科研和临床工作的结合情况;群体层面的因素涉及科研机构的科研人力、科研资源、科研环境、科研管理机制、科研培训能力、外部科研合作资源和机构管理者的重视程度;学科层面的因素则涉及学科的科研特点、核心的科研和协调机构、政府、学协会、学术期刊和国际合作者的外部影响,以及科研经费等方面。结论本研究综述了全球范围内关于全科医学科研能力发展的文献,识别出21个关键的影响因素。在我国的实际环境中,上述因素可能会集中表现为学科组织分散、总体资源有限、学科理论不清、社会认知不足等一系列相互影响的问题。这要求该领域的研究者更加主动地以学科核心理念为导向,对机构的科研绩效导向和管理机制,以及个人的科研领域和路径进行合理的调整和重塑,并增强对全科医生具有全科医学学科特色的科研理论、方法和能力的培养,以增强形成学科合力的基础。展开更多
基金funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R319),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia and Prince Sultan University for covering the article processing charges(APC)associated with this publicationResearchers Supporting Project Number(RSPD2025R1107),King Saud University,Riyadh,Saudi Arabia.
文摘Healthcare networks prove to be an urgent issue in terms of intrusion detection due to the critical consequences of cyber threats and the extreme sensitivity of medical information.The proposed Auto-Stack ID in the study is a stacked ensemble of encoder-enhanced auctions that can be used to improve intrusion detection in healthcare networks.TheWUSTL-EHMS 2020 dataset trains and evaluates themodel,constituting an imbalanced class distribution(87.46% normal traffic and 12.53% intrusion attacks).To address this imbalance,the study balances the effect of training Bias through Stratified K-fold cross-validation(K=5),so that each class is represented similarly on training and validation splits.Second,the Auto-Stack ID method combines many base classifiers such as TabNet,LightGBM,Gaussian Naive Bayes,Histogram-Based Gradient Boosting(HGB),and Logistic Regression.We apply a two-stage training process based on the first stage,where we have base classifiers that predict out-of-fold(OOF)predictions,which we use as inputs for the second-stage meta-learner XGBoost.The meta-learner learns to refine predictions to capture complicated interactions between base models,thus improving detection accuracy without introducing bias,overfitting,or requiring domain knowledge of the meta-data.In addition,the auto-stack ID model got 98.41% accuracy and 93.45%F1 score,better than individual classifiers.It can identify intrusions due to its 90.55% recall and 96.53% precision with minimal false positives.These findings identify its suitability in ensuring healthcare networks’security through ensemble learning.Ongoing efforts will be deployed in real time to improve response to evolving threats.
文摘Introduction:Direct-to-consumer(DTC)telemedicine has emerged as an option for transgender patients seeking gender affirming hormone therapy(GAHT).We aimed to characterize the healthcare services provided by DTC telemedicine companies offering GAHT and to compare their costs to a tertiary care center.Methods:We identified DTC telemedicine platforms offering GAHT via internet searches and extracted information from their websites related to evaluation,treatment,monitoring,and cost.Cost of theDTC GAHT was compared to cost for comparable services at a tertiary care center.Results:Six DTC companies were identified.All platforms utilized an informed consent model without prerequisite mental health evaluation for GAHT.Platforms did not provide comprehensive mental health services.All platforms endorsed the use of regular follow up visits throughout the treatment period although interval of laboratory assessment varied.Cost estimates were comparable for uninsured patients and higher compared to those for insured patients.Cost estimates were lowest with private and public insurance at the tertiary center.Conclusions:DTC telemedicine platforms offering GAHT appear to be in line with the recently released World Professional Association for Transgender Health standards of care regarding the laboratory evaluation and monitoring,but it is unclear whether they are compliant with other recommendations.These platforms offer competitive costs for TGD patients without insurance.
文摘背景全科医学作为一种新兴医疗模式,强调以社区为基础的全面医疗服务,旨在提高医疗的可及性和效率。然而,尽管全科医学在提升基本医疗卫生服务能力中扮演关键角色,但全科医学研究仍处于发展滞后的状态,需要通过科学研究和社会支持来改善这一状况,并提高其学科地位。目的在研究人员、科研机构和学科整体三个自下而上的层级中,分层次地分析影响全科医学领域科研能力发展的因素。方法于2023年12月—2024年3月,本文采用范围综述方法,对中国知网、万方数据知识服务平台、PubMed、Web of Science数据库进行检索,并手动浏览国际全科医学科研和行业学会的信息发布平台,以及谷歌搜索引擎,收集2000—2023年发表的全科医学科研能力发展影响因素的相关文献,并通过手动检索补充灰色文献。通过两阶段的筛选,最终纳入相关文献,使用Excel进行数据整理,归类影响因素,并通过归纳性的主题分析法分析数据,最终以日冕图的形式展示研究结果。结果本研究最终纳入122篇文献,包括原创研究62篇、系统综述2篇、非原创论文54篇、灰色文献4篇,基于文献分析,共归纳出21项影响全科医学科研能力发展的因素。相关因素被分为个体(研究人员)、群体(机构)和整体(学科)三个层级。个体层面的因素涉及研究人员的科研知识和技能、申请资金的能力、对科研的兴趣和积极性、学习科研知识和发展科研合作的机遇、用于科研工作的时间以及科研和临床工作的结合情况;群体层面的因素涉及科研机构的科研人力、科研资源、科研环境、科研管理机制、科研培训能力、外部科研合作资源和机构管理者的重视程度;学科层面的因素则涉及学科的科研特点、核心的科研和协调机构、政府、学协会、学术期刊和国际合作者的外部影响,以及科研经费等方面。结论本研究综述了全球范围内关于全科医学科研能力发展的文献,识别出21个关键的影响因素。在我国的实际环境中,上述因素可能会集中表现为学科组织分散、总体资源有限、学科理论不清、社会认知不足等一系列相互影响的问题。这要求该领域的研究者更加主动地以学科核心理念为导向,对机构的科研绩效导向和管理机制,以及个人的科研领域和路径进行合理的调整和重塑,并增强对全科医生具有全科医学学科特色的科研理论、方法和能力的培养,以增强形成学科合力的基础。
