Machine learning cryptography methods for IoT in healthcare.

BACKGROUND: The increased application of Internet of Things (IoT) in healthcare, has fueled concerns regarding the security and privacy of patient data. Lightweight Cryptography (LWC) algorithms can be seen as a potential solution to address this concern. Due to the high variation of LWC, the primary objective of this study was to identify a suitable yet effective algorithm for securing sensitive patient information on IoT devices. METHODS: This study evaluates the performance of eight LWC algorithms-AES, PRESENT, MSEA, LEA, XTEA, SIMON, PRINCE, and RECTANGLE-using machine learning models. Experiments were conducted on a Raspberry Pi 3 microcontroller using 16 KB to 2048 KB files. Machine learning models were trained and tested for each LWC algorithm and their performance was evaluated based using precision, recall, F1-score, and accuracy metrics. RESULTS: The study analyzed the encryption/decryption execution time, energy consumption, memory usage, and throughput of eight LWC algorithms. The RECTANGLE algorithm was identified as the most suitable and efficient LWC algorithm for IoT in healthcare due to its speed, efficiency, simplicity, and flexibility. CONCLUSIONS: This research addresses security and privacy concerns in IoT healthcare and identifies key performance factors of LWC algorithms utilizing the SLR research methodology. Furthermore, the study provides insights into the optimal choice of LWC algorithm for enhancing privacy and security in IoT healthcare environments.

Pandemic Management: Health Data and Public Health Surveillance.

The inefficiency of the healthcare system in addressing pandemics is highlighted after COVID-19 which is mostly rooted in data availability and accuracy. As it is believed we might witness more pandemics in future, our research's main objective is to propose an integrated health system to support healthcare preparedness for future infectious outbreaks and pandemics. The system could support managers and authorities in healthcare and disaster management, and policymakers through data collection, sharing, and analysis.

Enhancing Health Equity by Predicting Missed Appointments in Health Care: Machine Learning Study.

BACKGROUND: The phenomenon of patients missing booked appointments without canceling them-known as Did Not Show (DNS), Did Not Attend (DNA), or Failed To Attend (FTA)-has a detrimental effect on patients' health and results in massive health care resource wastage. OBJECTIVE: Our objective was to develop machine learning (ML) models and evaluate their performance in predicting the likelihood of DNS for hospital outpatient appointments at the MidCentral District Health Board (MDHB) in New Zealand. METHODS: We sourced 5 years of MDHB outpatient records (a total of 1,080,566 outpatient visits) to build the ML prediction models. We developed 3 ML models using logistic regression, random forest, and Extreme Gradient Boosting (XGBoost). Subsequently, 10-fold cross-validation and hyperparameter tuning were deployed to minimize model bias and boost the algorithms' prediction strength. All models were evaluated against accuracy, sensitivity, specificity, and area under the receiver operating characteristic (AUROC) curve metrics. RESULTS: Based on 5 years of MDHB data, the best prediction classifier was XGBoost, with an area under the curve (AUC) of 0.92, sensitivity of 0.83, and specificity of 0.85. The patients' DNS history, age, ethnicity, and appointment lead time significantly contributed to DNS prediction. An ML system trained on a large data set can produce useful levels of DNS prediction. CONCLUSIONS: This research is one of the very first published studies that use ML technologies to assist with DNS management in New Zealand. It is a proof of concept and could be used to benchmark DNS predictions for the MDHB and other district health boards. We encourage conducting additional qualitative research to investigate the root cause of DNS issues and potential solutions. Addressing DNS using better strategies potentially can result in better utilization of health care resources and improve health equity.

Patients' perspectives on digital health tools.

Objective: Digital technology has changed the way healthcare is delivered and accessed. However, the focus is mostly on technology and clinical aspects. This review aimed to integrate and critically analyse the available knowledge regarding patients' perspectives on digital health tools and identify facilitators and barriers to their uptake. Methods: A narrative review was conducted using the Scopus and Google Scholar databases. Information related to facilitators and barriers to uptake was synthesised and interpreted using thematic and content analyses, respectively. Results: Seventy-one out of 1722 articles identified were eligible for inclusion. Patient empowerment, self-management, and personalisation were identified as the main factors that contributed to patient uptake in using digital health tools. Digital literacy, health literacy, and privacy concerns were identified as barriers to the uptake of digital health technology. Conclusion: Digital health technologies have changed the way healthcare is experienced by patients. Research highlights the disconnect between the development and implementation of digital health tools and the patients they are created for. This review may serve as the foundation for future research incorporating patients' perspectives to help increase patients' engagement with emerging technologies. Innovation: Participatory design approaches have the potential to support the creation of patient-centred digital health tools.

Clinical information system (CIS) implementation in developing countries: requirements, success factors, and recommendations.

OBJECTIVE: Clinical Information System (CIS) usage can reduce healthcare costs over time, improve the quality of medical care and safety, and enhance clinical efficiency. However, CIS implementation in developing countries poses additional, different challenges from the developed countries. Therefore, this research aimed to systematically review the literature, gathering and integrating research findings on Success Factors (SFs) in CIS implementation for developing countries. This helps to integrate past knowledge and develop a set of recommendations, presented as a framework, for implementing CIS in developing countries. MATERIALS AND METHODS: A systematic literature review was conducted, followed by qualitative data analysis on the published articles related to requirements and SF for CIS implementation. Eighty-three articles met the inclusion criteria and were included in the data analysis. Thematic analysis and cross-case analysis were applied to identify and categorize the requirements and SF for CIS implementation in developing countries. RESULTS: Six major requirement categories were identified including project management, financial resources, government involvement and support, human resources, organizational, and technical requirements. Subcategories related to SF are classified under each major requirement. A set of recommendations is provided, presented in a framework, based on the project management lifecycle approach. CONCLUSION: The proposed framework could support CIS implementations in developing countries while enhancing their rate of success. Future studies should focus on identifying barriers to CIS implementation in developing countries. The country-specific empirical studies should also be conducted based on this research's findings to match the local context.

A Robust Deep Learning Ensemble-Driven Model for Defect and Non-Defect Recognition and Classification Using a Weighted Averaging Sequence-Based Meta-Learning Ensembler.

The need to overcome the challenges of visual inspections conducted by domain experts drives the recent surge in visual inspection research. Typical manual industrial data analysis and inspection for defects conducted by trained personnel are expensive, time-consuming, and characterized by mistakes. Thus, an efficient intelligent-driven model is needed to eliminate or minimize the challenges of defect identification and elimination in processes to the barest minimum. This paper presents a robust method for recognizing and classifying defects in industrial products using a deep-learning architectural ensemble approach integrated with a weighted sequence meta-learning unification framework. In the proposed method, a unique base model is constructed and fused together with other co-learning pretrained models using a sequence-driven meta-learning ensembler that aggregates the best features learned from the various contributing models for better and superior performance. During experimentation in the study, different publicly available industrial product datasets consisting of the defect and non-defect samples were used to train, validate, and test the introduced model, with remarkable results obtained that demonstrate the viability of the proposed method in tackling the challenges of the manual visual inspection approach.

A Hard Voting Policy-Driven Deep Learning Architectural Ensemble Strategy for Industrial Products Defect Recognition and Classification.

Manual or traditional industrial product inspection and defect-recognition models have some limitations, including process complexity, time-consuming, error-prone, and expensiveness. These issues negatively impact the quality control processes. Therefore, an efficient, rapid, and intelligent model is required to improve industrial products' production fault recognition and classification for optimal visual inspections and quality control. However, intelligent models obtained with a tradeoff of high accuracy for high latency are tedious for real-time implementation and inferencing. This work proposes an ensemble deep-leaning architectural framework based on a deep learning model architectural voting policy to compute and learn the hierarchical and high-level features in industrial artefacts. The voting policy is formulated with respect to three crucial viable model characteristics: model optimality, efficiency, and performance accuracy. In the study, three publicly available industrial produce datasets were used for the proposed model's various experiments and validation process, with remarkable results recorded, demonstrating a significant increase in fault recognition and classification performance in industrial products. In the study, three publicly available industrial produce datasets were used for the proposed model's various experiments and validation process, with remarkable results recorded, demonstrating a significant increase in fault recognition and classification performance in industrial products.

Internet of things (IoT) applications for elderly care: a reflective review.

Increasing in elderly population put extra pressure on healthcare systems globally in terms of operational costs and resources. To minimize this pressure and provide efficient healthcare services, the application of the Internet of Things (IoT) and wearable technology could be promising. These technologies have the potential to improve the quality of life of the elderly population while reducing strain on healthcare systems and minimizing their operational cost. Although IoT and wearable applications for elderly healthcare purposes were reviewed previously, there is a further need to summarize their current applications in this fast-developing area. This paper provides a comprehensive overview of IoT and wearable technologies' applications including the types of data collected and the types of devices for elderly healthcare. This paper provides insights into existing areas of IoT/wearable applications while presenting new research opportunities in emerging areas of applications, such as robotic technology and integrated applications. The analysis in this paper could be useful to healthcare solution designers and developers in defining technology supported futuristic healthcare strategies to serve elderly people and increasing their quality of life.

Disaster eHealth: Scoping Review.

BACKGROUND: Although both disaster management and disaster medicine have been used for decades, their efficiency and effectiveness have been far from perfect. One reason could be the lack of systematic utilization of modern technologies, such as eHealth, in their operations. To address this issue, researchers' efforts have led to the emergence of the disaster eHealth (DEH) field. DEH's main objective is to systematically integrate eHealth technologies for health care purposes within the disaster management cycle (DMC). OBJECTIVE: This study aims to identify, map, and define the scope of DEH as a new area of research at the intersection of disaster management, emergency medicine, and eHealth. METHODS: An extensive scoping review using published materials was carried out in the areas of disaster management, disaster medicine, and eHealth to identify the scope of DEH. This review procedure was iterative and conducted in multiple scientific databases in 2 rounds, one using controlled indexed terms and the other using similar uncontrolled terms. In both rounds, the publications ranged from 1990 to 2016, and all the appropriate research studies discovered were considered, regardless of their research design, methodology, and quality. Information extracted from both rounds was thematically analyzed to define the DEH scope, and the results were evaluated by the field experts through a Delphi method. RESULTS: In both rounds of the research, searching for eHealth applications within DMC yielded 404 relevant studies that showed eHealth applications in different disaster types and disaster phases. These applications varied with respect to the eHealth technology types, functions, services, and stakeholders. The results led to the identification of the scope of DEH, including eHealth technologies and their applications, services, and future developments that are applicable to disasters as well as to related stakeholders. Reference to the elements of the DEH scope indicates what, when, and how current eHealth technologies can be used in the DMC. CONCLUSIONS: Comprehensive data gathering from multiple databases offered a grounded method to define the DEH scope. This scope comprises concepts related to DEH and the boundaries that define it. The scope identifies the eHealth technologies relevant to DEH and the functions and services that can be provided by these technologies. In addition, the scope tells us which groups can use the provided services and functions and in which disaster types or phases. DEH approaches could potentially improve the response to health care demands before, during, and after disasters. DEH takes advantage of eHealth technologies to facilitate DMC tasks and activities, enhance their efficiency and effectiveness, and enhance health care delivery and provide more quality health care services to the wider population regardless of their geographical location or even disaster types and phases.

mHealth and big-data integration: promises for healthcare system in India.

BACKGROUND: The use of mobile devices in health (mobile health/mHealth) coupled with related technologies promises to transform global health delivery by creating new delivery models that can be integrated with existing health services. These delivery models could facilitate healthcare delivery into rural areas where there is limited access to high-quality access care. Mobile technologies, Internet of Things and 5G connectivity may hold the key to supporting increased velocity, variety and volume of healthcare data. OBJECTIVE: The purpose of this study is to identify and analyse challenges related to the current status of India's healthcare system-with a specific focus on mHealth and big-data analytics technologies. To address these challenges, a framework is proposed for integrating the generated mHealth big-data and applying the results in India's healthcare. METHOD: A critical review was conducted using electronic sources between December 2018 and February 2019, limited to English language articles and reports published from 2010 onwards. MAIN OUTCOME: This paper describes trending relationships in mHealth with big-data as well as the accessibility of national opportunities when specific barriers and constraints are overcome. The paper concentrates on the healthcare delivery problems faced by rural and low-income communities in India to illustrate more general aspects and identify key issues. A model is proposed that utilises generated data from mHealth devices for big-data analysis that could result in providing insights into the India population health status. The insights could be important for public health planning by the government towards reaching the Universal Health Coverage. CONCLUSION: Biomedical, behavioural and lifestyle data from individuals may enable customised and improved healthcare services to be delivered. The analysis of data from mHealth devices can reveal new knowledge to effectively and efficiently support national healthcare demands in less developed nations, without fully accessible healthcare systems.

IoT, Cloud Computing and Big Data: Integrated Framework for Healthcare in Disasters.

Currently, healthcare in disaster management context faces a number of challenges mostly due to the lack of availability of reliable data from diverse sources required to be accessible by appropriate authorities. Therefore, the main objective of this study is the introduction of a framework based on the integration of three technologies, Internet of Things (IoT), cloud computing and big data to solve this issue in all disaster phases and provide precise and effective healthcare. This framework supports healthcare managers by enabling data sharing among them and assists them in performing analytical calculations to discover meaningful, logical and accurate trend(s) required for strategic planning and better preparedness in the face of disasters. Also, the outcome of the framework may help decision makers to identify and predict the health consequences of the disasters for any specific geographical location in any country based on its geographical properties and disaster background.

Healthcare in Disasters and the Role of RFID.

Disasters either natural or man-made are inevitable, and therefore disaster management has always been an important function of government. Since during a disaster healthcare is often adversely affected, a lot of effort has been made in terms of researching effective responses and ways of improving the quality of delivered care to direct casualties and the rest of the community. In this regard, information technology plays an important role to help healthcare systems achieve this goal. One of these technologies that has become popular recently is Radio-Frequency Identification (RFID). This paper explores the relationship between emergency management and disaster healthcare and examines the role of RFID. It is suggested that RFID will become an integral part of disaster healthcare and a means of improving response performance.