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Journal of Medical Systems Feb 2024This systematic review examines the recent use of artificial intelligence, particularly machine learning, in the management of operating rooms. A total of 22 selected... (Review)
Review
This systematic review examines the recent use of artificial intelligence, particularly machine learning, in the management of operating rooms. A total of 22 selected studies from February 2019 to September 2023 are analyzed. The review emphasizes the significant impact of AI on predicting surgical case durations, optimizing post-anesthesia care unit resource allocation, and detecting surgical case cancellations. Machine learning algorithms such as XGBoost, random forest, and neural networks have demonstrated their effectiveness in improving prediction accuracy and resource utilization. However, challenges such as data access and privacy concerns are acknowledged. The review highlights the evolving nature of artificial intelligence in perioperative medicine research and the need for continued innovation to harness artificial intelligence's transformative potential for healthcare administrators, practitioners, and patients. Ultimately, artificial intelligence integration in operative room management promises to enhance healthcare efficiency and patient outcomes.
Topics: Humans; Artificial Intelligence; Operating Rooms; Neural Networks, Computer; Algorithms; Machine Learning
PubMed: 38353755
DOI: 10.1007/s10916-024-02038-2 -
Neural Networks : the Official Journal... Jun 2024Unsupervised domain adaptation (UDA) via deep learning has attracted appealing attention for tackling domain-shift problems caused by distribution discrepancy across... (Review)
Review
Unsupervised domain adaptation (UDA) via deep learning has attracted appealing attention for tackling domain-shift problems caused by distribution discrepancy across different domains. Existing UDA approaches highly depend on the accessibility of source domain data, which is usually limited in practical scenarios due to privacy protection, data storage and transmission cost, and computation burden. To tackle this issue, many source-free unsupervised domain adaptation (SFUDA) methods have been proposed recently, which perform knowledge transfer from a pre-trained source model to the unlabeled target domain with source data inaccessible. A comprehensive review of these works on SFUDA is of great significance. In this paper, we provide a timely and systematic literature review of existing SFUDA approaches from a technical perspective. Specifically, we categorize current SFUDA studies into two groups, i.e., white-box SFUDA and black-box SFUDA, and further divide them into finer subcategories based on different learning strategies they use. We also investigate the challenges of methods in each subcategory, discuss the advantages/disadvantages of white-box and black-box SFUDA methods, conclude the commonly used benchmark datasets, and summarize the popular techniques for improved generalizability of models learned without using source data. We finally discuss several promising future directions in this field.
Topics: Benchmarking; Knowledge; Privacy
PubMed: 38490115
DOI: 10.1016/j.neunet.2024.106230 -
Sensors (Basel, Switzerland) Jun 2021In the course of the digitization of production facilities, tracking and tracing of assets in the supply chain is becoming increasingly relevant for the manufacturing... (Review)
Review
In the course of the digitization of production facilities, tracking and tracing of assets in the supply chain is becoming increasingly relevant for the manufacturing industry. The collection and use of real-time position data of logistics, tools and load carriers are already standard procedure in entire branches of the industry today. In addition to asset tracking, the technologies used also offer new possibilities for collecting and evaluating position and biometric data of employees. Thus, these technologies can be used for monitoring performance or for tracking worker behaviour, which can lead to additional burdens and stress for employees. In this context, the collection and evaluation of employee data can influence the workplace of the affected employee in the company to his or her disadvantage. The approach of Privacy by Design can help to benefit from all the advantages of these systems, while ensuring that the impact on employee privacy is kept to a minimum. Currently, there is no survey available that reviews tracking and tracing systems supporting this important and emerging field. This work provides a systematic overview from the perspective of the impact on employee privacy. Additionally, this paper identifies and evaluates the techniques used with regard to employee privacy in industrial tracking and tracing systems. This helps to reveal new privacy preserving techniques that are currently underrepresented, therefore enabling new research opportunities in the industrial community.
Topics: Female; Humans; Male; Privacy; Technology; Workplace
PubMed: 34209327
DOI: 10.3390/s21134501 -
Healthcare Analytics (New York, N.Y.) Nov 2023The unexpected and rapid spread of the COVID-19 pandemic has amplified the acceptance of remote healthcare systems such as telemedicine. Telemedicine effectively... (Review)
Review
The unexpected and rapid spread of the COVID-19 pandemic has amplified the acceptance of remote healthcare systems such as telemedicine. Telemedicine effectively provides remote communication, better treatment recommendation, and personalized treatment on demand. It has emerged as the possible future of medicine. From a privacy perspective, secure storage, preservation, and controlled access to health data with consent are the main challenges to the effective deployment of telemedicine. It is paramount to fully overcome these challenges to integrate the telemedicine system into healthcare. In this regard, emerging technologies such as blockchain and federated learning have enormous potential to strengthen the telemedicine system. These technologies help enhance the overall healthcare standard when applied in an integrated way. The primary aim of this study is to perform a systematic literature review of previous research on privacy-preserving methods deployed with blockchain and federated learning for telemedicine. This study provides an in-depth qualitative analysis of relevant studies based on the architecture, privacy mechanisms, and machine learning methods used for data storage, access, and analytics. The survey allows the integration of blockchain and federated learning technologies with suitable privacy techniques to design a secure, trustworthy, and accurate telemedicine model with a privacy guarantee.
PubMed: 37223223
DOI: 10.1016/j.health.2023.100192 -
Sensors (Basel, Switzerland) Apr 2021Owing to progressive population aging, elderly people (aged 65 and above) face challenges in carrying out activities of daily living, while placement of the elderly in a... (Review)
Review
Owing to progressive population aging, elderly people (aged 65 and above) face challenges in carrying out activities of daily living, while placement of the elderly in a care facility is expensive and mentally taxing for them. Thus, there is a need to develop their own homes into smart homes using new technologies. However, this raises concerns of privacy and data security for users since it can be handled remotely. Hence, with advancing technologies it is important to overcome this challenge using privacy-preserving and non-intrusive models. For this review, 235 articles were scanned from databases, out of which 31 articles pertaining to in-home technologies that assist the elderly in living independently were shortlisted for inclusion. They described the adoption of various methodologies like different sensor-based mechanisms, wearables, camera-based techniques, robots, and machine learning strategies to provide a safe and comfortable environment to the elderly. Recent innovations have rendered these technologies more unobtrusive and privacy-preserving with increasing use of environmental sensors and less use of cameras and other devices that may compromise the privacy of individuals. There is a need to develop a comprehensive system for smart homes which ensures patient safety, privacy, and data security; in addition, robots should be integrated with the existing sensor-based platforms to assist in carrying out daily activities and therapies as required.
Topics: Activities of Daily Living; Aged; Aging; Computer Security; Humans; Privacy; Technology
PubMed: 33925161
DOI: 10.3390/s21093082 -
PloS One 2022Mitochondrial diseases are a large group of genetically heterogeneous and clinically diverse disorders. Diagnosis often takes many years for which treatment may not... (Review)
Review
BACKGROUND
Mitochondrial diseases are a large group of genetically heterogeneous and clinically diverse disorders. Diagnosis often takes many years for which treatment may not exist. Registries are often used to conduct research, establish natural disease progression, engage the patient community, and develop best disease management practices. In Canada, there are limited centralized registries for mitochondrial disease patients, presenting a challenge for patients and professionals.
OBJECTIVE
To support the creation of such a registry, a systematic scoping review was conducted to map the landscape of mitochondrial disease patient registries worldwide, with a focus on registry design and challenges. Furthermore, it addresses a knowledge gap by providing a narrative synthesis of published literature that describes these registries.
METHODS
Arksey and O'Malley's methodological framework was followed to systematically search English-language literature in PubMed and CINAHL describing the designs of mitochondrial disease patient registries, supplemented by a grey literature search. Data were extracted in Microsoft Excel. Stakeholder consultations were also performed with patient caregivers, advocates, and researchers to provide perspectives beyond those found in the literature. These data were thematically analyzed and were reported in accordance with the PRISMA-ScR reporting guidelines.
RESULTS
A total of 17 articles were identified describing 13 unique registries located in North America, Europe, Australia, and West Asia. These papers described the registries' designs, their strengths, and weaknesses, as well as their tangible outcomes such as facilitating recruitment for research and supporting epidemiological studies.
CONCLUSION
Based on our findings in this review, recommendations were formulated. These include establishing registry objectives, respecting patients and their roles in the registry, adopting international data standards, data evaluations, and considerations to privacy legislation, among others. These recommendations could be used to support designing a future Canadian mitochondrial disease patient registry, and to further research directly engaging these registries worldwide.
Topics: Humans; Canada; Registries; Research Personnel; Mitochondrial Diseases; Europe
PubMed: 36301904
DOI: 10.1371/journal.pone.0276883 -
Multimedia Tools and Applications Feb 2023In today's world, health and medicine play an undeniable role in human life. Traditional and current Electronic Health Records (EHR) systems that are used to exchange...
In today's world, health and medicine play an undeniable role in human life. Traditional and current Electronic Health Records (EHR) systems that are used to exchange information between medical stakeholders (patients, physicians, insurance companies, pharmaceuticals, medical researchers, etc.) suffer weaknesses in terms of security and privacy due to having centralized architecture. Blockchain technology ensures the privacy and security of EHR systems thanks to the use of encryption. Moreover, due to its decentralized nature, this technology prevents central failure and central attack points. In this paper, a systematic literature review (SLR) is proposed to analyze the existing Blockchain-based approaches for improving privacy and security in electronic health systems. The research methodology, paper selection process, and the search query are explained. 51 papers returned from our search criteria published between 2018 and Dec 2022 are reviewed. The main ideas, type of Blockchain, evaluation metrics, and used tools of each selected paper are discussed in detail. Finally, future research directions, open challenges, and some issues are discussed.
PubMed: 36811000
DOI: 10.1007/s11042-023-14488-w -
Healthcare (Basel, Switzerland) Feb 2021Blockchain technology was introduced through Bitcoin in a 2008 whitepaper by the mysterious Satoshi Nakamoto. Since its inception, it has gathered great attention... (Review)
Review
Blockchain technology was introduced through Bitcoin in a 2008 whitepaper by the mysterious Satoshi Nakamoto. Since its inception, it has gathered great attention because of its unique properties-immutability and decentralized authority. This technology is now being implemented in various fields such as healthcare, IoT, data management, etc., apart from cryptocurrencies. As it is a newly emerging technology, researchers and organizations face many challenges in integrating this technology into other fields. Consent management is one of the essential processes in an organization because of the ever-evolving privacy laws, which are introduced to provide more control to users over their data. This paper is a systematic review of Blockchain's application in the field of consent and privacy data management. The review discusses the adaptation of Blockchain in healthcare, IoT, identity management, and data storage. This analysis is formed on the principles of the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) and a process of systematic mapping review. We provide analysis of the development, challenges, and limitations of blockchain technology for consent management.
PubMed: 33535465
DOI: 10.3390/healthcare9020137 -
JMIR MHealth and UHealth Apr 2022Loneliness and social isolation are associated with multiple health problems, including depression, functional impairment, and death. Mobile sensing using smartphones... (Review)
Review
BACKGROUND
Loneliness and social isolation are associated with multiple health problems, including depression, functional impairment, and death. Mobile sensing using smartphones and wearable devices, such as fitness trackers or smartwatches, as well as ambient sensors, can be used to acquire data remotely on individuals and their daily routines and behaviors in real time. This has opened new possibilities for the early detection of health and social problems, including loneliness and social isolation.
OBJECTIVE
This scoping review aimed to identify and synthesize recent scientific studies that used passive sensing techniques, such as the use of in-home ambient sensors, smartphones, and wearable device sensors, to collect data on device users' daily routines and behaviors to detect loneliness or social isolation. This review also aimed to examine various aspects of these studies, especially target populations, privacy, and validation issues.
METHODS
A scoping review was undertaken, following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews). Studies on the topic under investigation were identified through 6 databases (IEEE Xplore, Scopus, ACM, PubMed, Web of Science, and Embase). The identified studies were screened for the type of passive sensing detection methods for loneliness and social isolation, targeted population, reliability of the detection systems, challenges, and limitations of these detection systems.
RESULTS
After conducting the initial search, a total of 40,071 papers were identified. After screening for inclusion and exclusion criteria, 29 (0.07%) studies were included in this scoping review. Most studies (20/29, 69%) used smartphone and wearable technology to detect loneliness or social isolation, and 72% (21/29) of the studies used a validated reference standard to assess the accuracy of passively collected data for detecting loneliness or social isolation.
CONCLUSIONS
Despite the growing use of passive sensing technologies for detecting loneliness and social isolation, some substantial gaps still remain in this domain. A population heterogeneity issue exists among several studies, indicating that different demographic characteristics, such as age and differences in participants' behaviors, can affect loneliness and social isolation. In addition, despite extensive personal data collection, relatively few studies have addressed privacy and ethical issues. This review provides uncertain evidence regarding the use of passive sensing to detect loneliness and social isolation. Future research is needed using robust study designs, measures, and examinations of privacy and ethical concerns.
Topics: Humans; Loneliness; Reproducibility of Results; Smartphone; Social Isolation; Wearable Electronic Devices
PubMed: 35412465
DOI: 10.2196/34638 -
Medicine Aug 2018Published studies have reported conflicting and heterogeneous results regarding the association between human leukocyte antigen (HLA)-DRB1 polymorphisms and alopecia... (Meta-Analysis)
Meta-Analysis Review
BACKGROUND
Published studies have reported conflicting and heterogeneous results regarding the association between human leukocyte antigen (HLA)-DRB1 polymorphisms and alopecia areata (AA). This study aimed to review and quantitatively analyze the association between HLA-DRB1 polymorphisms and AA.
METHODS
In this study, all relevant publications were searched through December 2016. Odds ratios (ORs) and confidence intervals (CIs) for comparisons between case and control groups were calculated. Stata 14.0 software was used to perform statistical analysis. This research does not require formal ethical approval because the data used in this analysis do not involve personal information and thus do not affect privacy.
RESULTS
Twelve articles were identified. For HLA-DRB1*04 and HLA-DRB1*16 polymorphisms, the OR (95% CIs) was 1.49 (1.24-1.78) and 1.61 (1.08-2.41), and P was <.01 and <.01, respectively. For HLA-DRB1*0301, HLA-DRB1*09, and HLA-DRB1*13 polymorphisms, the OR (95% CIs) was 0.42 (0.28-0.63), 0.74 (0.55-0.99), and 0.62 (0.40-0.98), and P was <.01, <.01, and <.01, respectively. Statistical evidence revealed no publication bias (Pā>ā.05).
CONCLUSION
The present meta-analysis suggested that HLA-DRB1*04 and HLA-DRB1*16 polymorphisms might be associated with increased AA risk, while HLA-DRB1*0301, HLA-DRB1*09, and HLA-DRB1*13 polymorphisms might decrease the AA risk. Studies with adequate methodological quality on gene-gene and gene-environment interactions are needed to validate the results in the future.
Topics: Alleles; Alopecia Areata; Case-Control Studies; Genetic Predisposition to Disease; HLA-DRB1 Chains; Humans; Odds Ratio; Polymorphism, Single Nucleotide
PubMed: 30095639
DOI: 10.1097/MD.0000000000011790