ExpressionHash: Securing Telecare Medical Information Systems Using BioHashing

The COVID-19 outbreak and its medical distancing phenomenon have effectively turned the global healthcare challenge into an opportunity for Telecare Medical Information Systems.Such systems employ the latest mobile and digital technologies and provide several advantages like minimal physical contact between patient and healthcare provider,easy mobility,easy access,consistent patient engagement,and cost-effectiveness.Any leakage or unauthorized access to users’medical data can have serious consequences for any medical information system.The majority of such systems thus rely on biometrics for authenticated access but biometric systems are also prone to a variety of attacks like spoong,replay,Masquerade,and stealing of stored templates.In this article,we propose a new cancelable biometric approach which has tentatively been named as“Expression Hash”for Telecare Medical Information Systems.The idea is to hash the expression templates with a set of pseudo-random keys which would provide a unique code(expression hash).This code can then be serving as a template for verication.Different expressions would result in different sets of expression hash codes,which could be used in different applications and for different roles of each individual.The templates are stored on the server-side and the processing is also performed on the server-side.The proposed technique is a multi-factor authentication system and provides advantages like enhanced privacy and security without the need for multiple biometric devices.In the case of compromise,the existing code can be revoked and can be directly replaced by a new set of expression hash code.The well-known JAFFE(The Japanese Female Facial Expression)dataset has been for empirical testing and the results advocate for the efcacy of the proposed approach.

Analysis and Improvement of a Privacy-Preserving Authentication Scheme for Telecare Medical Information System Environment

Telecare Medical Information System(TMIS) can provide various telemedicine services to patients. However, information is communicated over an open channel. An attacker may intercept, replay, or modify this information. Therefore, many authentication schemes are proposed to provide secure communication for TMIS. Recently, Yu et al proposed a privacy-preserving authentication scheme in the Internet of Medical Things(IoMT)-enabled TMIS environments. They emphasize that their scheme is resistant to various attacks and ensures anonymity. Unfortunately, this paper demonstrates that Yu et al's scheme is vulnerable to impersonation attacks, replay attacks, and tracking attacks and cannot mutually authenticate. To overcome the shortcomings of Yu et al's scheme, we mainly improve the authentication and key agreement process and propose a corresponding improved scheme. We also compare the improved scheme with several existing authentication schemes in terms of security and computational efficiency.

Design of an Information Security Service for Medical Artificial Intelligence

The medical convergence industry has gradually adopted ICT devices,which has led to legacy security problems related to ICT devices.However,it has been difficult to solve these problems due to data resource issues.Such problems can cause a lack of reliability in medical artificial intelligence services that utilize medical information.Therefore,to provide reliable services focused on security internalization,it is necessary to establish a medical convergence environment-oriented security management system.This study proposes the use of system identification and countermeasures to secure systemreliabilitywhen using medical convergence environment information in medical artificial intelligence.We checked the life cycle of medical information and the flow and location of information,analyzed the security threats that may arise during the life cycle,and proposed technical countermeasures to overcome such threats.We verified the proposed countermeasures through a survey of experts.Security requirements were defined based on the information life cycle in the medical convergence environment.We also designed technical countermeasures for use in the security management systems of hospitals of diverse sizes.

Medical Information Breaches Occurrence with Respect to Social Media Usage, in Selected Medical Institutions in Uganda

The purpose of this manuscript is to present research findings based on the reported cases of medical information breaches due to Social Media (SM) usage, in selected medical institutions in Uganda. The study employed online survey techniques. Altogether, 710 questionnaires (Google forms) were developed, and operationalized. The main respondents included 566 medical students, and 143 medical staff from Mbarara University of Science and Technology (MUST), and Kampala International University (KIU), accordingly. Using SPSS, the main statistical analysis tools employed include frequency distribution summary, and Chi-square (x2) test. According to the frequency distribution summary, 27% to 42% of the respondents within categorical divides acknowledged occurrence of medical information breaches due to SM usage. Notably, higher levels of the breaches were reported among male students (64%), age-group 18 to 35 years (68%), and WhatsApp users (63%). On the other hand, Chi-square results showed significant levels (p p > 0.05) between medical institutions and medical information breaches. Overall, the vulnerable areas of the breaches identified would serve as important reference points in the process of rationalizing SM usage in medical institutions. Nevertheless, further studies could focus on identification of the key SM usage factors associated with medical information breaches in medical institutions in Uganda.

个人健康档案中基于指纹特征的个性化加密密钥(英文)

An embedded method which can provide privacy-safeguard and data-security layer for the Personal Health Records (PHR) is proposed. In our method, the fingerprint image of a patient or doctor is obtained with fingerprint scanner and the values of fingerprint features points are calculated and saved in an IC card. As a result, saving the fingerprint image is not required in our method. Based on the user's password, a transformation is applied on the fingerprint topology structural values. After that, we take these points' coordinates on the transformed topology structure as a cryptographic key, which is used with Advanced Encryption Standard (AES) algorithm to encrypt the users' privacy information, such as prescription, laboratory sheet, medical certificate, etc. The experimental results demonstrate that our method could bring patients the self-control and self-management on their own medical privacy information.

Data mining in clinical big data:the frequently used databases,steps,and methodological models

Many high quality studies have emerged from public databases,such as Surveillance,Epidemiology,and End Results(SEER),National Health and Nutrition Examination Survey(NHANES),The Cancer Genome Atlas(TCGA),and Medical Information Mart for Intensive Care(MIMIC);however,these data are often characterized by a high degree of dimensional heterogeneity,timeliness,scarcity,irregularity,and other characteristics,resulting in the value of these data not being fully utilized.Data-mining technology has been a frontier field in medical research,as it demonstrates excellent performance in evaluating patient risks and assisting clinical decision-making in building disease-prediction models.Therefore,data mining has unique advantages in clinical big-data research,especially in large-scale medical public databases.This article introduced the main medical public database and described the steps,tasks,and models of data mining in simple language.Additionally,we described data-mining methods along with their practical applications.The goal of this work was to aid clinical researchers in gaining a clear and intuitive understanding of the application of data-mining technology on clinical big-data in order to promote the production of research results that are beneficial to doctors and patients.

The recent advances in self-powered medical information sensors

Monitoring various medical information distributed throughout the body is of great importance in early clinic diagnosis and treatment of disease.To discover abnormal medical signals and find their causes in good time,the human body should be monitored continuously and accurately.To meet the requirements,various battery-less and self-powered information acquisition techniques are invented.In this review,the recent advances in self-powered medical information sensors(SMIS)with different functions,structure design,and electric performance are summarized and discussed.The SMIS mainly involves triboelectric nanogenerator(TENG),piezoelectric nanogenerator(PENG),pyroelectric nanogenerator(PyNG)/thermoelectric generator(TEG)and solar cell.Additionally,this review also analyzed the remaining challenges and prospected the development direction of SMIS in future.

Deep Learning Blockchain Integration Framework for Ureteropelvic Junction Obstruction Diagnosis Using Ultrasound Images

UreteroPelvic Junction Obstruction(UPJO)is a common hydronephrosis disease in children that can result in an even progressive loss of renal function.Ultrasonography is an economical,radiationless,noninvasive,and high noise preliminary diagnostic step for UPJO.Artificial intelligence has been widely applied to medical fields and can greatly assist doctors'diagnostic abilities.The demand for a highly secure network environment in transferring electronic medical data online,therefore,has led to the development of blockchain technology.In this study,we built and tested a framework that integrates a deep learning diagnosis model with blockchain technology.Our diagnosis model is a combination of an attention-based pyramid semantic segmentation network and a discrete wavelet transformation-processed residual classification network.We also compared the performance between benchmark models and our models.Our diagnosis model outperformed benchmarks on the segmentation task and classification task with MloU=87.93,MPA=93.52,and accuracy=91.77%.For the blockchain system,we applied the InterPlanetary File System protocol to build a secure and private sharing environment.This framework can automatically grade the severity of UPJO using ultrasound images,guarantee secure medical data sharing,assist in doctors'diagnostic ability,relieve patients'burden,and provide technical support for future federated learning and linkage of the Internet of Medical Things(loMT).

Red blood cell distribution width improves the prediction of 28-daymortality for patients with sepsis-induced acute kidney injury:A retrospective analysis from MIMIC-IV database usingpropensity score matching

Background:The predictive value of red blood cell distribution width(RDW)for mortality in patients withsepsis-induced acute kidney injury(SI-AKI)remains unclear.The present study aimed to investigate the potentialassociation between RDW at admission and outcomes in patients with SI-AKI.Methods:The Medical Information Mart for Intensive Care(MIMIC)-IV(version 2.0)database,released in Juneof 2022,provides medical data of SI-AKI patients to conduct our related research.Based on propensity scorematching(PSM)method,the main risk factors associated with mortality in SI-AKI were evaluated using Coxproportional hazards regression analysis to construct a predictive nomogram.The concordance index(C-index)and decision curve analysis were used to validate the predictive ability and clinical utility of this model.Patientswith SI-AKI were classified into the high-and low-RDW groups according to the best cut-off value obtained bycalculating the maximum value of the Youden index.Results:A total of 7574 patients with SI-AKI were identified according to the filter criteria.Compared withthe low-RDW group,the high-RDW group had higher 28-day(9.49%vs.31.40%,respectively,P<0.001)and7-day(3.96%vs.13.93%,respectively,P<0.001)mortality rates.Patients in the high-RDW group were moreprone to AKI progression than those in the low-RDW group(20.80%vs.13.60%,respectively,P<0.001).Basedon matched patients,we developed a nomogram model that included age,white blood cells,RDW,combinedhypertension and presence of a malignant tumor,treatment with vasopressor,dialysis,and invasive ventilation,sequential organ failure assessment,and AKI stages.The C-index for predicting the probability of 28-day survivalwas 0.799.Decision curve analysis revealed that the model with RDW offered greater net benefit than that withoutRDW.Conclusion:The present findings demonstrated the importance of RDW,which improved the predictive ability ofthe nomogram model for the probability of survival in patients with SI-AKI.

Analysis of the correlation between the longitudinal trajectory of SOFA scores and prognosis in patients with sepsis at 72 hour after admission based on group trajectory modeling

Background:To identify the distinct trajectories of the Sequential Organ Failure Assessment(SOFA)scores at 72 h for patients with sepsis in the Medical Information Mart for Intensive Care(MIMIC)-IV database and determine their effects on mortality and adverse clinical outcomes.Methods:A retrospective cohort study was carried out involving patients with sepsis from the MIMIC-IV database.Group-based trajectory modeling(GBTM)was used to identify the distinct trajectory groups for the SOFA scores in patients with sepsis in the intensive care unit(ICU).The Cox proportional hazards regression model was used to investigate the relationship between the longitudinal change trajectory of the SOFA score and mortality and adverse clinical outcomes.Results:A total of 16,743 patients with sepsis were included in the cohort.The median survival age was 66 years(interquartile range:54-76 years).The 7-day and 28-day in-hospital mortality were 6.0%and 17.6%,respectively.Five different trajectories of SOFA scores according to the model fitting standard were determined:group 1(32.8%),group 2(30.0%),group 3(17.6%),group 4(14.0%)and group 5(5.7%).Univariate and multivariate Cox regression analyses showed that,for different clinical outcomes,trajectory group 1 was used as the reference,while trajectory groups 2-5 were all risk factors associated with the outcome(P<0.001).Subgroup analysis revealed an interaction between the two covariates of age and mechanical ventilation and the different trajectory groups of patients’SOFA scores(P<0.05).Conclusion:This approach may help identify various groups of patients with sepsis,who may be at different levels of risk for adverse health outcomes,and provide subgroups with clinical importance.