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1.
Sensors (Basel) ; 24(3)2024 Jan 31.
Artigo em Inglês | MEDLINE | ID: mdl-38339643

RESUMO

This research uses a low-resolution infrared array sensor to address real-time human activity recognition while prioritizing the preservation of privacy. The proposed system captures thermal pixels that are represented as a human silhouette. With camera and image processing, it is easy to detect human activity, but that reduces privacy. This work proposes a novel human activity recognition system that uses interpolation and mathematical measures that are unobtrusive and do not involve machine learning. The proposed method directly and efficiently recognizes multiple human states in a real-time environment. This work also demonstrates the accuracy of the outcomes for various scenarios using traditional ML approaches. This low-resolution IR array sensor is effective and would be useful for activity recognition in homes and healthcare centers.


Assuntos
Atividades Humanas , Privacidade , Humanos , Aprendizado de Máquina , Processamento de Imagem Assistida por Computador
2.
Sci Rep ; 14(1): 3944, 2024 02 16.
Artigo em Inglês | MEDLINE | ID: mdl-38365940

RESUMO

Deep learning has proven to be highly effective in diagnosing COVID-19; however, its efficacy is contingent upon the availability of extensive data for model training. The data sharing among hospitals, which is crucial for training robust models, is often restricted by privacy regulations. Federated learning (FL) emerges as a solution by enabling model training across multiple hospitals while preserving data privacy. However, the deployment of FL can be resource-intensive, necessitating efficient utilization of computational and network resources. In this study, we evaluate the performance and resource efficiency of five FL algorithms in the context of COVID-19 detection using Convolutional Neural Networks (CNNs) in a decentralized setting. The evaluation involves varying the number of participating entities, the number of federated rounds, and the selection algorithms. Our findings indicate that the Cyclic Weight Transfer algorithm exhibits superior performance, particularly when the number of participating hospitals is limited. These insights hold practical implications for the deployment of FL algorithms in COVID-19 detection and broader medical image analysis.


Assuntos
COVID-19 , Humanos , COVID-19/diagnóstico , Algoritmos , Hospitais , Redes Neurais de Computação , Privacidade
3.
JMIR Mhealth Uhealth ; 12: e48526, 2024 Feb 09.
Artigo em Inglês | MEDLINE | ID: mdl-38335026

RESUMO

BACKGROUND: Smart home technology (SHT) can be useful for aging in place or health-related purposes. However, surveillance studies have highlighted ethical issues with SHTs, including user privacy, security, and autonomy. OBJECTIVE: As digital technology is most often designed for younger adults, this review summarizes perceptions of SHTs among users aged 50 years and older to explore their understanding of privacy, the purpose of data collection, risks and benefits, and safety. METHODS: Through an integrative review, we explored community-dwelling adults' (aged 50 years and older) perceptions of SHTs based on research questions under 4 nonmutually exclusive themes: privacy, the purpose of data collection, risk and benefits, and safety. We searched 1860 titles and abstracts from Ovid MEDLINE, Ovid Embase, Cochrane Database of Systematic Reviews, and Cochrane Central Register of Controlled Trials, Scopus, Web of Science Core Collection, and IEEE Xplore or IET Electronic Library, resulting in 15 included studies. RESULTS: The 15 studies explored user perception of smart speakers, motion sensors, or home monitoring systems. A total of 13 (87%) studies discussed user privacy concerns regarding data collection and access. A total of 4 (27%) studies explored user knowledge of data collection purposes, 7 (47%) studies featured risk-related concerns such as data breaches and third-party misuse alongside benefits such as convenience, and 9 (60%) studies reported user enthusiasm about the potential for home safety. CONCLUSIONS: Due to the growing size of aging populations and advances in technological capabilities, regulators and designers should focus on user concerns by supporting higher levels of agency regarding data collection, use, and disclosure and by bolstering organizational accountability. This way, relevant privacy regulation and SHT design can better support user safety while diminishing potential risks to privacy, security, autonomy, or discriminatory outcomes.


Assuntos
Vida Independente , Privacidade , Idoso , Humanos , Pessoa de Meia-Idade , Percepção , Tecnologia
4.
Opt Lett ; 49(3): 546-549, 2024 Feb 01.
Artigo em Inglês | MEDLINE | ID: mdl-38300055

RESUMO

Computer vision technology has been applied in various fields such as identification, surveillance, and robot vision. However, computer vision algorithms used for human-related tasks operate on human images, which raises data security and privacy concerns. In this Letter, we propose an image-free human keypoint detection technique using a few coded illuminations and a single-pixel detector. Our proposed method can complete the keypoint detection task at an ultralow sampling rate on a measured one-dimensional sequence without image reconstruction, thus protecting privacy from the data collection stage and preventing the acquisition of detailed visual information from the source. The network is designed to optimize both the illumination patterns and the human keypoint predictor with an encoder-decoder framework. For model training and validation, we used 2000 images from Leeds Sport Dataset and COCO Dataset. By incorporating EfficientNet backbone, the inference time is reduced from 4 s to 0.10 s. In the simulation, the proposed network achieves 91.7% average precision. Our experimental results show an average precision of 88.4% at a remarkably low sampling rate of 0.015. In summary, our proposed method has the advantages of privacy protection and resource efficiency, which can be applied to many monitoring and healthcare tasks, such as clinical monitoring, construction site monitoring, and home service robots.


Assuntos
Algoritmos , Privacidade , Humanos , Simulação por Computador , Processamento de Imagem Assistida por Computador , Iluminação
5.
Math Biosci Eng ; 21(1): 1610-1624, 2024 Jan 02.
Artigo em Inglês | MEDLINE | ID: mdl-38303480

RESUMO

Deep learning technology has shown considerable potential in various domains. However, due to privacy issues associated with medical data, legal and ethical constraints often result in smaller datasets. The limitations of smaller datasets hinder the applicability of deep learning technology in the field of medical image processing. To address this challenge, we proposed the Federated Particle Swarm Optimization algorithm, which is designed to increase the efficiency of decentralized data utilization in federated learning and to protect privacy in model training. To stabilize the federated learning process, we introduced Tri-branch feature pyramid network (TFPNet), a multi-branch structure model. TFPNet mitigates instability during the aggregation model deployment and ensures fast convergence through its multi-branch structure. We conducted experiments on four different public datasets:CVC-ClinicDB, Kvasir, CVC-ColonDB and ETIS-LaribPolypDB. The experimental results show that the Federated Particle Swarm Optimization algorithm outperforms single dataset training and the Federated Averaging algorithm when using independent scattered data, and TFPNet converges faster and achieves superior segmentation accuracy compared to other models.


Assuntos
Algoritmos , Processamento de Imagem Assistida por Computador , Privacidade
6.
Sci Rep ; 14(1): 3685, 2024 02 14.
Artigo em Inglês | MEDLINE | ID: mdl-38355815

RESUMO

The increasing use of social media platforms as personalized advertising channels is a double-edged sword. A high level of personalization on these platforms increases users' sense of losing control over personal data: This could trigger the privacy fatigue phenomenon manifested in emotional exhaustion and cynicism toward privacy, which leads to a lack of privacy-protective behavior. Machine learning has shown its effectiveness in the early prediction of people's psychological state to avoid such consequences. Therefore, this study aims to classify users with low and medium-to-high levels of privacy fatigue, based on their information privacy awareness and big-five personality traits. A dataset was collected from 538 participants via an online questionnaire. The prediction models were built using the Support Vector Machine, Naïve Bayes, K-Nearest Neighbors, Decision Tree, and Random Forest classifiers, based on the literature. The results showed that awareness and conscientiousness trait have a significant relationship with privacy fatigue. Support Vector Machine and Naïve Bayes classifiers outperformed the other classifiers by attaining a classification accuracy of 78%, F1 of 87%, recall of 100% and 98%, and precision of 78% and 79% respectively, using five-fold cross-validation.


Assuntos
Publicidade , Mídias Sociais , Humanos , Privacidade , Teorema de Bayes , Aprendizado de Máquina , Máquina de Vetores de Suporte , Fadiga
7.
Harm Reduct J ; 21(1): 30, 2024 Feb 04.
Artigo em Inglês | MEDLINE | ID: mdl-38311762

RESUMO

BACKGROUND: In 2021, a Chinese court, based on the newly enacted Civil Code, first revoked a marriage license due to the spouse's failure to disclose their HIV infection before the marriage. This landmark case ignited a fresh debate on whether people living with HIV (PLHIV) have a legal duty to inform their spouses and sexual partners. Advances in medicine have partially isolated HIV transmission from sexual contact, extending the legal basis for the obligation to disclose beyond disease prevention. This study investigates some possibly unforeseen challenges for PLHIV in China to fulfill this duty, and the outcomes of their decisions in light of the government's goal to promote health. METHODS: This study aims to provide a detailed examination of the legal provisions and practices concerning partner notification among PLHIV in China. A mixed-methods research approach was employed between 2019 and 2020, combining questionnaire surveys, in-depth interviews, and participatory observations. A total of 433 valid responses were obtained through a questionnaire posted on a Chinese online platform for PLHIV. Following the collection and random coding of the questionnaire data, 40 individuals living with HIV were selected for in-depth interviews. Subsequently, a six-month field investigation was conducted in Guan ai jia yuan (Caring Home) in Jinhua City to further explore this issue. RESULTS: A considerable proportion of PLHIV exhibit a high rate of disclosure to their spouses (nearly 80%). In the context of sexual partners, 56% of PLHIV stated that their sexual partners were aware of their HIV infection. Whether married PLHIV disclosing to their spouses or unmarried/divorced PLHIV disclosing to sexual partners, however, a substantial majority expressed apprehension about the potential disruption to their relationships that the disclosure might cause. The sole exception was observed among married PLHIV in extramarital relationships who demonstrated a slightly diminished level of concern in this context. Reasons for non-disclosure predominantly included undetectable viral load and the adoption of protective measures. DISCUSSION: This study reveals that a prevailing "HIV stigma" hinders PLHIV from voluntarily fulfilling the disclosure duties bestowed by Article 38 of the Regulations on the Prevention and Control of HIV/AIDS, and the unclear legal provisions of the new Civil Code play a significant role in this regard. Addressing this issue necessitates not only increasing societal tolerance toward PLHIV and reducing instances of social exclusion but also shifting the legal basis of disclosure duties from disease prevention to rights and obligations within the legal relationships of the parties involved. When it comes to the recipients of disclosure, for instance, it is crucial to differentiate between spouses and sexual partners. As for PLHIV failing to fulfill their disclosure duties, apart from interventions involving indirect notifications, the addition of further legal responsibilities may not be advisable. Intentional transmission actions, on the other hand, should still be subject to severe penalties. CLINICAL TRIAL NUMBER: Not applicable.


Assuntos
Infecções por HIV , Humanos , Infecções por HIV/prevenção & controle , Revelação , Saúde Pública , Promoção da Saúde , Privacidade , Parceiros Sexuais
8.
J Med Internet Res ; 26: e48443, 2024 Jan 25.
Artigo em Inglês | MEDLINE | ID: mdl-38271060

RESUMO

BACKGROUND: The widespread use of electronic health records in the clinical and biomedical fields makes the removal of protected health information (PHI) essential to maintain privacy. However, a significant portion of information is recorded in unstructured textual forms, posing a challenge for deidentification. In multilingual countries, medical records could be written in a mixture of more than one language, referred to as code mixing. Most current clinical natural language processing techniques are designed for monolingual text, and there is a need to address the deidentification of code-mixed text. OBJECTIVE: The aim of this study was to investigate the effectiveness and underlying mechanism of fine-tuned pretrained language models (PLMs) in identifying PHI in the code-mixed context. Additionally, we aimed to evaluate the potential of prompting large language models (LLMs) for recognizing PHI in a zero-shot manner. METHODS: We compiled the first clinical code-mixed deidentification data set consisting of text written in Chinese and English. We explored the effectiveness of fine-tuned PLMs for recognizing PHI in code-mixed content, with a focus on whether PLMs exploit naming regularity and mention coverage to achieve superior performance, by probing the developed models' outputs to examine their decision-making process. Furthermore, we investigated the potential of prompt-based in-context learning of LLMs for recognizing PHI in code-mixed text. RESULTS: The developed methods were evaluated on a code-mixed deidentification corpus of 1700 discharge summaries. We observed that different PHI types had preferences in their occurrences within the different types of language-mixed sentences, and PLMs could effectively recognize PHI by exploiting the learned name regularity. However, the models may exhibit suboptimal results when regularity is weak or mentions contain unknown words that the representations cannot generate well. We also found that the availability of code-mixed training instances is essential for the model's performance. Furthermore, the LLM-based deidentification method was a feasible and appealing approach that can be controlled and enhanced through natural language prompts. CONCLUSIONS: The study contributes to understanding the underlying mechanism of PLMs in addressing the deidentification process in the code-mixed context and highlights the significance of incorporating code-mixed training instances into the model training phase. To support the advancement of research, we created a manipulated subset of the resynthesized data set available for research purposes. Based on the compiled data set, we found that the LLM-based deidentification method is a feasible approach, but carefully crafted prompts are essential to avoid unwanted output. However, the use of such methods in the hospital setting requires careful consideration of data security and privacy concerns. Further research could explore the augmentation of PLMs and LLMs with external knowledge to improve their strength in recognizing rare PHI.


Assuntos
Inteligência Artificial , Registros Eletrônicos de Saúde , Humanos , Processamento de Linguagem Natural , Privacidade , China
9.
J Biomed Inform ; 150: 104583, 2024 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-38191010

RESUMO

OBJECTIVE: The primary objective of our study is to address the challenge of confidentially sharing medical images across different centers. This is often a critical necessity in both clinical and research environments, yet restrictions typically exist due to privacy concerns. Our aim is to design a privacy-preserving data-sharing mechanism that allows medical images to be stored as encoded and obfuscated representations in the public domain without revealing any useful or recoverable content from the images. In tandem, we aim to provide authorized users with compact private keys that could be used to reconstruct the corresponding images. METHOD: Our approach involves utilizing a neural auto-encoder. The convolutional filter outputs are passed through sparsifying transformations to produce multiple compact codes. Each code is responsible for reconstructing different attributes of the image. The key privacy-preserving element in this process is obfuscation through the use of specific pseudo-random noise. When applied to the codes, it becomes computationally infeasible for an attacker to guess the correct representation for all the codes, thereby preserving the privacy of the images. RESULTS: The proposed framework was implemented and evaluated using chest X-ray images for different medical image analysis tasks, including classification, segmentation, and texture analysis. Additionally, we thoroughly assessed the robustness of our method against various attacks using both supervised and unsupervised algorithms. CONCLUSION: This study provides a novel, optimized, and privacy-assured data-sharing mechanism for medical images, enabling multi-party sharing in a secure manner. While we have demonstrated its effectiveness with chest X-ray images, the mechanism can be utilized in other medical images modalities as well.


Assuntos
Algoritmos , Privacidade , Disseminação de Informação
10.
PLoS Genet ; 20(1): e1011037, 2024 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-38206971

RESUMO

Explicitly sharing individual level data in genomics studies has many merits comparing to sharing summary statistics, including more strict QCs, common statistical analyses, relative identification and improved statistical power in GWAS, but it is hampered by privacy or ethical constraints. In this study, we developed encG-reg, a regression approach that can detect relatives of various degrees based on encrypted genomic data, which is immune of ethical constraints. The encryption properties of encG-reg are based on the random matrix theory by masking the original genotypic matrix without sacrificing precision of individual-level genotype data. We established a connection between the dimension of a random matrix, which masked genotype matrices, and the required precision of a study for encrypted genotype data. encG-reg has false positive and false negative rates equivalent to sharing original individual level data, and is computationally efficient when searching relatives. We split the UK Biobank into their respective centers, and then encrypted the genotype data. We observed that the relatives estimated using encG-reg was equivalently accurate with the estimation by KING, which is a widely used software but requires original genotype data. In a more complex application, we launched a finely devised multi-center collaboration across 5 research institutes in China, covering 9 cohorts of 54,092 GWAS samples. encG-reg again identified true relatives existing across the cohorts with even different ethnic backgrounds and genotypic qualities. Our study clearly demonstrates that encrypted genomic data can be used for data sharing without loss of information or data sharing barrier.


Assuntos
Estudo de Associação Genômica Ampla , Privacidade , Humanos , Estudo de Associação Genômica Ampla/métodos , Genótipo , Software , Genômica
11.
J Comput Biol ; 31(2): 99-116, 2024 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-38271572

RESUMO

Identifying cancer subtype-specific driver genes from a large number of irrelevant passengers is crucial for targeted therapy in cancer treatment. Recently, the rapid accumulation of large-scale cancer genomics data from multiple institutions has presented remarkable opportunities for identification of cancer subtype-specific driver genes. However, the insufficient subtype samples, privacy issues, and heterogenous of aberration events pose great challenges in precisely identifying cancer subtype-specific driver genes. To address this, we introduce privatedriver, the first model for identifying subtype-specific driver genes that integrates genomics data from multiple institutions in a data privacy-preserving collaboration manner. The process of identifying subtype-specific cancer driver genes using privatedriver involves the following two steps: genomics data integration and collaborative training. In the integration process, the aberration events from multiple genomics data sources are combined for each institution using the forward and backward propagation method of NetICS. In the collaborative training process, each institution utilizes the federated learning framework to upload encrypted model parameters instead of raw data of all institutions to train a global model by using the non-negative matrix factorization algorithm. We applied privatedriver on head and neck squamous cell and colon cancer from The Cancer Genome Atlas website and evaluated it with two benchmarks using macro-Fscore. The comparison analysis demonstrates that privatedriver achieves comparable results to centralized learning models and outperforms most other nonprivacy preserving models, all while ensuring the confidentiality of patient information. We also demonstrate that, for varying predicted driver gene distributions in subtype, our model fully considers the heterogeneity of subtype and identifies subtype-specific driver genes corresponding to the given prognosis and therapeutic effect. The success of privatedriver reveals the feasibility and effectiveness of identifying cancer subtype-specific driver genes in a data protection manner, providing new insights for future privacy-preserving driver gene identification studies.


Assuntos
Neoplasias do Colo , Privacidade , Humanos , Oncogenes , Algoritmos , Benchmarking
12.
BMC Health Serv Res ; 24(1): 44, 2024 Jan 09.
Artigo em Inglês | MEDLINE | ID: mdl-38195476

RESUMO

BACKGROUND: Hospital Examination Reservation System (HERS) was designed for reducing appointment examination waiting time and enhancing patients' medical satisfaction in China, but implementing HERS would encounter many difficulties. This study would investigate the factors that influence patients' utilization of HERS through UTAUT2, and provide valuable insights for hospital managements to drive the effective implementation of HERS. It is helpful for improving patients' medical satisfaction. METHODS: We conducted a survey through the Sojump platform, targeting patients were who have already used HERS. We collected questionnaire information related to factors behavior intention, performance expectancy, and effort expectancy. Subsequently, we employed a structural equation model to analyze the factors influencing patients' utilization of HERS. RESULTS: A total of 394 valid questionnaires were collected. Habit was the main direct positive factor influencing the behavioral intention of HERS (ß = 0.593; 95%CI: 0.072, 1.944; P = 0.002), followed by patient innovation (ß = 0.269; 95%CI: 0.002, 0.443; P < 0.001), effort expectancy (ß = 0.239; 95%CI: -0.022, 0.478; P = 0.048). Patient innovation and facilitating conditions also have an indirect effect on behavioral intention. Perceived privacy exposure has a significantly negative effect on behavioral intention (ß=-0.138; 95%CI: -0.225, -0.047; P < 0.001). The above variables explained 56.7% of the variation in behavioral intention. CONCLUSIONS: When HERS is implemented in hospitals, managements should arrange volunteers to guide patients to bring up the habit and solve the using difficulties, and managements could invite patients with high innovation to recommend HERS to others, what's more, it is a valid way to retain the old form of appointment to pass the transition period to the new system. HERS utilization and patients' medical satisfaction will be enhanced through the guidance of hospital management means.


Assuntos
Hospitais , Intenção , Humanos , Feminino , China , Satisfação do Paciente , Privacidade
13.
Stud Health Technol Inform ; 310: 820-824, 2024 Jan 25.
Artigo em Inglês | MEDLINE | ID: mdl-38269923

RESUMO

Healthcare data is a scarce resource and access is often cumbersome. While medical software development would benefit from real datasets, the privacy of the patients is held at a higher priority. Realistic synthetic healthcare data can fill this gap by providing a dataset for quality control while at the same time preserving the patient's anonymity and privacy. Existing methods focus on American or European patient healthcare data but none is exclusively focused on the Australian population. Australia is a highly diverse country that has a unique healthcare system. To overcome this problem, we used a popular publicly available tool, Synthea, to generate disease progressions based on the Australian population. With this approach, we were able to generate 100,000 patients following Queensland (Australia) demographics.


Assuntos
Instalações de Saúde , Privacidade , Humanos , Austrália , Queensland , Progressão da Doença
14.
Stud Health Technol Inform ; 310: 1156-1160, 2024 Jan 25.
Artigo em Inglês | MEDLINE | ID: mdl-38269996

RESUMO

AI tools are being introduced within health services around the globe. It is important that tools are developed and validated using the available health information of the population where it is intended to be used. We set out to determine what patients thought about the use of their health information for this purpose. In interviews we found that the patients of a health service in Auckland, Aotearoa New Zealand, are generally comfortable with their health information being used for these purposes but with conditions (around public good, governance, privacy, security, transparency, and restrictions on commercial gain) and with careful consideration of their perspectives. We suggest that health services should take the time to have these conversations with their communities and to provide open and clear communication around these developments in their services.


Assuntos
Comunicação , Serviços de Saúde , Humanos , Nova Zelândia , Privacidade
15.
Rev Infirm ; 73(297): 39-40, 2024 Jan.
Artigo em Francês | MEDLINE | ID: mdl-38242622

RESUMO

Rigorous monitoring of vital functions in intensive care requires optimal visibility of patients and their environment. Conversely, respect for privacy is an ethical imperative to respect. Liquid crystal electrical film is a device that can be applied to windows and can take opaque or transparent form on demand. Its use could satisfy the visibility of patients and respect for their privacy.


Assuntos
Unidades de Terapia Intensiva , Privacidade , Humanos , Cuidados Críticos , Pacientes
16.
BMC Health Serv Res ; 24(1): 6, 2024 Jan 03.
Artigo em Inglês | MEDLINE | ID: mdl-38172824

RESUMO

BACKGROUND: This scoping review aims to systematically and critically describe the numerous legal challenges brought about by the utilization of digital oral health in the delivery of oral healthcare. METHODS: A systematic search was conducted. The following electronic databases were reviewed from inception up to March 2023: MEDLINE, Embase, Scopus, and LILACS. The search included any scientific document and paper in English, Spanish, or Portuguese on legal issues raised using digital health in oral healthcare delivery. Two reviewers conducted the selection process and data extraction. Legal issues raised concerning the adoption of digital health technology were analysed using the modified Mars' framework. RESULTS: Seventeen studies were included. Most of the documents identified and covered generic aspects of delivering digital oral healthcare (n = 11) without explicitly referring to any dental specialty. The most mentioned legal issues were data security (n = 15); liability and malpractice (n = 14); consent (n = 12); and confidentiality (n = 12). To a lower extent, patient-practitioner relationship (n = 11); and license and jurisdiction (n = 11) were also covered. These were followed by privacy of information (n = 10); adequacy of records (n = 9); and e-referrals (n = 8). On the other hand, fewer studies commented on social media use (n = 3), authentication (n = 2); or e-prescriptions (n = 2). Before implementing any digital health solution, practitioners need to be aware of the many legal issues that the introduction of these technologies involves, be clear where the responsibility lies, and apply extreme caution in following national guidelines. Current literature concentrates on a few well-known legal issues. Issues around authentication, use of social media, and e-prescriptions received less attention.


Assuntos
Confidencialidade , Saúde Bucal , Humanos , Atenção à Saúde , Privacidade
17.
PLoS One ; 19(1): e0294429, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-38289970

RESUMO

Cloud computing is vital in various applications, such as healthcare, transportation, governance, and mobile computing. When using a public cloud server, it is mandatory to be secured from all known threats because a minor attacker's disturbance severely threatens the whole system. A public cloud server is posed with numerous threats; an adversary can easily enter the server to access sensitive information, especially for the healthcare industry, which offers services to patients, researchers, labs, and hospitals in a flexible way with minimal operational costs. It is challenging to make it a reliable system and ensure the privacy and security of a cloud-enabled healthcare system. In this regard, numerous security mechanisms have been proposed in past decades. These protocols either suffer from replay attacks, are completed in three to four round trips or have maximum computation, which means the security doesn't balance with performance. Thus, this work uses a fuzzy extractor method to propose a robust security method for a cloud-enabled healthcare system based on Elliptic Curve Cryptography (ECC). The proposed scheme's security analysis has been examined formally with BAN logic, ROM and ProVerif and informally using pragmatic illustration and different attacks' discussions. The proposed security mechanism is analyzed in terms of communication and computation costs. Upon comparing the proposed protocol with prior work, it has been demonstrated that our scheme is 33.91% better in communication costs and 35.39% superior to its competitors in computation costs.


Assuntos
Confidencialidade , Telemedicina , Humanos , Segurança Computacional , Atenção à Saúde , Privacidade
18.
Am J Manag Care ; 30(1): 31-37, 2024 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-38271580

RESUMO

OBJECTIVES: To understand patient perceptions of specific applications of predictive models in health care. STUDY DESIGN: Original, cross-sectional national survey. METHODS: We conducted a national online survey of US adults with the National Opinion Research Center from November to December 2021. Measures of internal consistency were used to identify how patients differentiate between clinical and administrative predictive models. Multivariable logistic regressions were used to identify relationships between comfort with various types of predictive models and patient demographics, perceptions of privacy protections, and experiences in the health care system. RESULTS: A total of 1541 respondents completed the survey. After excluding observations with missing data for the variables of interest, the final analytic sample was 1488. We found that patients differentiate between clinical and administrative predictive models. Comfort with prediction of bill payment and missed appointments was especially low (21.6% and 36.6%, respectively). Comfort was higher with clinical predictive models, such as predicting stroke in an emergency (55.8%). Experiences of discrimination were significant negative predictors of comfort with administrative predictive models. Health system transparency around privacy policies was a significant positive predictor of comfort with both clinical and administrative predictive models. CONCLUSIONS: Patients are more comfortable with clinical applications of predictive models than administrative ones. Privacy protections and transparency about how health care systems protect patient data may facilitate patient comfort with these technologies. However, larger inequities and negative experiences in health care remain important for how patients perceive administrative applications of prediction.


Assuntos
Atenção à Saúde , Privacidade , Adulto , Humanos , Estudos Transversais , Inquéritos e Questionários , Modelos Logísticos
19.
Lancet Digit Health ; 6(2): e93-e104, 2024 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-38278619

RESUMO

BACKGROUND: Multicentre training could reduce biases in medical artificial intelligence (AI); however, ethical, legal, and technical considerations can constrain the ability of hospitals to share data. Federated learning enables institutions to participate in algorithm development while retaining custody of their data but uptake in hospitals has been limited, possibly as deployment requires specialist software and technical expertise at each site. We previously developed an artificial intelligence-driven screening test for COVID-19 in emergency departments, known as CURIAL-Lab, which uses vital signs and blood tests that are routinely available within 1 h of a patient's arrival. Here we aimed to federate our COVID-19 screening test by developing an easy-to-use embedded system-which we introduce as full-stack federated learning-to train and evaluate machine learning models across four UK hospital groups without centralising patient data. METHODS: We supplied a Raspberry Pi 4 Model B preloaded with our federated learning software pipeline to four National Health Service (NHS) hospital groups in the UK: Oxford University Hospitals NHS Foundation Trust (OUH; through the locally linked research University, University of Oxford), University Hospitals Birmingham NHS Foundation Trust (UHB), Bedfordshire Hospitals NHS Foundation Trust (BH), and Portsmouth Hospitals University NHS Trust (PUH). OUH, PUH, and UHB participated in federated training, training a deep neural network and logistic regressor over 150 rounds to form and calibrate a global model to predict COVID-19 status, using clinical data from patients admitted before the pandemic (COVID-19-negative) and testing positive for COVID-19 during the first wave of the pandemic. We conducted a federated evaluation of the global model for admissions during the second wave of the pandemic at OUH, PUH, and externally at BH. For OUH and PUH, we additionally performed local fine-tuning of the global model using the sites' individual training data, forming a site-tuned model, and evaluated the resultant model for admissions during the second wave of the pandemic. This study included data collected between Dec 1, 2018, and March 1, 2021; the exact date ranges used varied by site. The primary outcome was overall model performance, measured as the area under the receiver operating characteristic curve (AUROC). Removable micro secure digital (microSD) storage was destroyed on study completion. FINDINGS: Clinical data from 130 941 patients (1772 COVID-19-positive), routinely collected across three hospital groups (OUH, PUH, and UHB), were included in federated training. The evaluation step included data from 32 986 patients (3549 COVID-19-positive) attending OUH, PUH, or BH during the second wave of the pandemic. Federated training of a global deep neural network classifier improved upon performance of models trained locally in terms of AUROC by a mean of 27·6% (SD 2·2): AUROC increased from 0·574 (95% CI 0·560-0·589) at OUH and 0·622 (0·608-0·637) at PUH using the locally trained models to 0·872 (0·862-0·882) at OUH and 0·876 (0·865-0·886) at PUH using the federated global model. Performance improvement was smaller for a logistic regression model, with a mean increase in AUROC of 13·9% (0·5%). During federated external evaluation at BH, AUROC for the global deep neural network model was 0·917 (0·893-0·942), with 89·7% sensitivity (83·6-93·6) and 76·6% specificity (73·9-79·1). Site-specific tuning of the global model did not significantly improve performance (change in AUROC <0·01). INTERPRETATION: We developed an embedded system for federated learning, using microcomputing to optimise for ease of deployment. We deployed full-stack federated learning across four UK hospital groups to develop a COVID-19 screening test without centralising patient data. Federation improved model performance, and the resultant global models were generalisable. Full-stack federated learning could enable hospitals to contribute to AI development at low cost and without specialist technical expertise at each site. FUNDING: The Wellcome Trust, University of Oxford Medical and Life Sciences Translational Fund.


Assuntos
COVID-19 , Atenção Secundária à Saúde , Humanos , Inteligência Artificial , Privacidade , Medicina Estatal , COVID-19/diagnóstico , Hospitais , Reino Unido
20.
BMC Med Inform Decis Mak ; 24(1): 27, 2024 Jan 30.
Artigo em Inglês | MEDLINE | ID: mdl-38291386

RESUMO

BACKGROUND: Synthetic data is an emerging approach for addressing legal and regulatory concerns in biomedical research that deals with personal and clinical data, whether as a single tool or through its combination with other privacy enhancing technologies. Generating uncompromised synthetic data could significantly benefit external researchers performing secondary analyses by providing unlimited access to information while fulfilling pertinent regulations. However, the original data to be synthesized (e.g., data acquired in Living Labs) may consist of subjects' metadata (static) and a longitudinal component (set of time-dependent measurements), making it challenging to produce coherent synthetic counterparts. METHODS: Three synthetic time series generation approaches were defined and compared in this work: only generating the metadata and coupling it with the real time series from the original data (A1), generating both metadata and time series separately to join them afterwards (A2), and jointly generating both metadata and time series (A3). The comparative assessment of the three approaches was carried out using two different synthetic data generation models: the Wasserstein GAN with Gradient Penalty (WGAN-GP) and the DöppelGANger (DGAN). The experiments were performed with three different healthcare-related longitudinal datasets: Treadmill Maximal Effort Test (TMET) measurements from the University of Malaga (1), a hypotension subset derived from the MIMIC-III v1.4 database (2), and a lifelogging dataset named PMData (3). RESULTS: Three pivotal dimensions were assessed on the generated synthetic data: resemblance to the original data (1), utility (2), and privacy level (3). The optimal approach fluctuates based on the assessed dimension and metric. CONCLUSION: The initial characteristics of the datasets to be synthesized play a crucial role in determining the best approach. Coupling synthetic metadata with real time series (A1), as well as jointly generating synthetic time series and metadata (A3), are both competitive methods, while separately generating time series and metadata (A2) appears to perform more poorly overall.


Assuntos
Metadados , Privacidade , Humanos , Fatores de Tempo , Bases de Dados Factuais
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