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
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.
