Health Big Data Research Articles

Towards a Model of Big Health Care Data Analytics in Panama: Chronic Kidney Disease.

Background:A growing number of mobile applications have been designed for the chronic disease patient as the primary user. Mobile health applications for self-care have the potential to help patients living with chronic conditions such as kidney disease, and can be used to manage aspects such as the consumption of substances that are harmful to health. Chronic kidney disease causes significant morbidity throughout Panama, and is also responsible for an increase in cardiovascular disease.Objective:In this paper, we present a review of the applications offered by the Android store, based on a search and selection of the most efficient options that fulfill a set of criteria and functionalities.Methods:We evaluate a big health data model in terms of its usefulness for studies, research and projections of Panamanian patients with this chronic disease.Results and Discusion:We present a mobile application based on the most important standards and functionalities for the Panamanian population affected by this disease. Our analysis also highlights the importance of mobile applications for the self-care of chronic diseases and their usefulness to both patients and health care providers, since they can support better health habits and give good results in terms of following a diet, promoting a healthy lifestyle, and encouraging physical activity. The analysis presented here will form the basis for the development of an application that will be simple, user-friendly and powerful, in the sense that it will empower patients with the resources they need for self-care. .Conclusion:Mobile applications are found to show promise for the self-care of chronic conditions, and can improve the quality of life of Panamanian patients. In addition, we intend to develop a data repository for scientific research within Central America.

Design and Application of Sports-Oriented Public Health Big Data Analysis Platform.

People's pursuit of public health continues to improve with the rapid economic development. Physical activity is an important way to achieve public health. Excessive physical activity intensity and uncomfortable forms of physical activity can affect people's physical and mental health. Reasonable physical activity intensity and reasonable physical activity form will be beneficial to public health. People need to choose the corresponding sports mode according to physical function parameters and mental health parameters. However, it is difficult to understand the relationship between physical activity patterns and public health-related parameters, which limits people to establish reasonable exercise patterns. This research uses big data technology to design an intelligent sports-oriented public health data analysis scheme. It mainly uses MLCNN method and LSTM method to extract physical function parameter features, mental health parameter features, and sports parameter features. The research results show that the MLCNN method and LSTM can accurately extract and predict the parametric features related to sports and public health. The largest relative mean error is only 2.52%, which is the predicted value of the physical performance parameter characteristics. The smallest prediction error is also 2.27%, and this part of the relative error comes from the prediction of sports parameters.

Association between cardiometabolic factors and lifestyle factors: Hamamatsu Wellness Project Health Big Data Analysis

Association between cardiometabolic factors and lifestyle factors: Hamamatsu Wellness Project Health Big Data Analysis

Influence of Taijiquan Exercise on Mentality and Emotion Regulation by Intelligent Medical Big Data Analysis.

Taijiquan training emphasizes the relaxation of the mind and the body, pay attention to maintain the peace of mind, and minimize the impact of external interference on the body so as to make the mind more comfortable. This study mainly explores the influence of Taijiquan practice on emotion regulation based on intelligent medical health big data analysis. The significance of Taijiquan in developing and improving the positive emotions of middle-aged people and maintaining physical and mental health is expounded. There are two methods of data collection: full collection and incremental collection. In this study, when the psychological testing equipment was launched, a full amount of historical data was collected; after the psychological testing equipment was online, the collection method was generally carried out in the way of incremental collection. The subjects exercised Taijiquan three times a week, one hour each time, and the exercise content was the 24-style Taijiquan designated by the workstation. At the same time, the subjects were asked not to engage in other regular physical exercise projects in their spare time. By longitudinal tracking and comparison of the Taijiquan intervention group after participating in the Taijiquan exercise intervention, the differences in the state of mind and emotion regulation strategies, and 12 subjects were selected voluntarily to participate in the emotional Stroop (the color words used in the classic Stroop paradigm were replaced with emotional and nonemotional words written in different colors, and the subjects were still tasked with responding to colors) experimental paradigm. In this paper, the moderate-intensity Taijiquan project is selected, which is in line with the effective value threshold theory of exercise load. It studies the effects of exercise on the body shape, cardiopulmonary function, flexibility, and balance ability of the body according to the metabolism theory and the movement balance theory of the human body adapting to the environment. Before the experiment, there was no significant difference between the Taijiquan training group and the control group, but after the experiment, there was a significant difference between the Taijiquan training group and the control group (P<0.05). Taijiquan has a significant effect on improving students' body shape, cardiopulmonary function, flexibility, balance, and mood.

Search-based health status detection and disease classification using species-level profiles of metagenomes

Microbiome biomarker-based modeling has been widely used in classifying health states. However, many diseases do not have explicit biomarkers, or exhibit shortages in detection accuracy using specific species. Based on microbiome big data and cutting-edge computing engine, here we report the search-based strategy of health status detection for shotgun metagenomes. Comparing the species-level profiles against large-scale metagenomes, outlier samples are screened out as unhealthy, and their detailed disease types can be identified by top matches. Benchmarking on a multi-cohort dataset with over 3,000 metagenomes, the search-based approach achieved a promising overall accuracy that was superior to marker-based models constructed by random forest (RF), supporting vector machine (SVM) and extreme gradient boosting (XGBoost). More importantly, the search-based method also featured a balanced performance on different diseases. Hence, this case study further demonstrates the potential and capability of metagenome big data in human health, as well as moves one-step forward of search-based approach in microbiome research and application.

Leveraging National Claims and Hospital Big Data: Cohort Study on a Statin-Drug Interaction Use Case.

BackgroundLinking different sources of medical data is a promising approach to analyze care trajectories. The aim of the INSHARE (Integrating and Sharing Health Big Data for Research) project was to provide the blueprint for a technological platform that facilitates integration, sharing, and reuse of data from 2 sources: the clinical data warehouse (CDW) of the Rennes academic hospital, called eHOP (entrepôt Hôpital), and a data set extracted from the French national claim data warehouse (Système National des Données de Santé [SNDS]).ObjectiveThis study aims to demonstrate how the INSHARE platform can support big data analytic tasks in the health field using a pharmacovigilance use case based on statin consumption and statin-drug interactions.MethodsA Spark distributed cluster-computing framework was used for the record linkage procedure and all analyses. A semideterministic record linkage method based on the common variables between the chosen data sources was developed to identify all patients discharged after at least one hospital stay at the Rennes academic hospital between 2015 and 2017. The use-case study focused on a cohort of patients treated with statins prescribed by their general practitioner or during their hospital stay.ResultsThe whole process (record linkage procedure and use-case analyses) required 88 minutes. Of the 161,532 and 164,316 patients from the SNDS and eHOP CDW data sets, respectively, 159,495 patients were successfully linked (98.74% and 97.07% of patients from SNDS and eHOP CDW, respectively). Of the 16,806 patients with at least one statin delivery, 8293 patients started the consumption before and continued during the hospital stay, 6382 patients stopped statin consumption at hospital admission, and 2131 patients initiated statins in hospital. Statin-drug interactions occurred more frequently during hospitalization than in the community (3800/10,424, 36.45% and 3253/14,675, 22.17%, respectively; P<.001). Only 121 patients had the most severe level of statin-drug interaction. Hospital stay burden (length of stay and in-hospital mortality) was more severe in patients with statin-drug interactions during hospitalization.ConclusionsThis study demonstrates the added value of combining and reusing clinical and claim data to provide large-scale measures of drug-drug interaction prevalence and care pathways outside hospitals. It builds a path to move the current health care system toward a Learning Health System using knowledge generated from research on real-world health data.

Blockchain and IPFS Integrated Framework in Bilevel Fog-Cloud Network for Security and Privacy of IoMT Devices.

Internet of Medical Things (IoMT) has emerged as an integral part of the smart health monitoring system in the present world. The smart health monitoring deals with not only for emergency and hospital services but also for maintaining a healthy lifestyle. The industry 5.0 and 5/6G has allowed the development of cost-efficient sensors and devices which can collect a wide range of human biological data and transfer it through wireless network communication in real time. This led to real-time monitoring of patient data through multiple IoMT devices from remote locations. The IoMT network registers a large number of patients and devices every day, along with the generation of huge amount of big data or health data. This patient data should retain data privacy and data security on the IoMT network to avoid any misuse. To attain such data security and privacy of the patient and IoMT devices, a three-level/tier network integrated with blockchain and interplanetary file system (IPFS) has been proposed. The proposed network is making the best use of IPFS and blockchain technology for security and data exchange in a three-level healthcare network. The present framework has been evaluated for various network activities for validating the scalability of the network. The network was found to be efficient in handling complex data with the capability of scalability.

Big Data Processing of Diagnostic Imaging Examinations Using a Self-Developed Digital Platform

Background: The aim of this survey is the presentation of a health information system that is eligible for processing and archiving clinical and economic health data by creating an online database that spots the origin of data on digital maps. Data concerns the cost of imaging examinations, number of hospital beds and number of clinical examinations.&#x0D; Methods: Geographical information Systems (GIS) can be used for the rational management of diagnostic imaging examinations and the creation of a data basement that contains clinical protocols, patients’ history and financial data. Users can exchange large volumes of the specific data very fast even between health units and hospitals. GIS Statistics enables users to estimate several financial indicators. Data concerns 5 Greek Public Health Units (H.Us) in the 6th Health Region.&#x0D; Results: GIS platform enables users to process big health data and share them on a geodatabase. SPSS analysis in combination with GIS statistics tools reveal an overtime increase of imaging exams in the majority of hospitals.&#x0D; Conclusions: The presented digital platform is a very useful tool for the rational management of clinical and financial data of healthcare units. It provides easy access to big data and enables users to gain in time data from digital maps in order to improve public health.

Study on the incidence of adult herpes zoster in Yichang city and its association with early-life famine exposure

Based on Yichang health big data platform, 850 608 patients from September 2018 to September 2019 were included in this study. According to the date of birth, the participants were divided into early childhood famine exposure group, fetal famine exposure group and non-famine exposure group. The incidence of adult herpes zoster (HZ) in Yichang city was analyzed, and the correlation between early life famine exposure and adult HZ was analyzed. In 2019, the crude incidence rate of adult HZ in Yichang was 6.83‰. The crude incidence rate of adult HZ in females (7.26‰) was higher than that in males (6.40‰). Compared with the non-famine exposure group, fetal famine exposure was associated with the incidence of adult HZ (OR=1.21; 95%CI: 1.01-1.45, P=0.041). After stratification by sex, fetal famine exposure was only found to be associated with the onset of adult HZ in females (OR=1.28, 95%CI:1.02-1.61, P=0.034).

The Right Not to be Subject to Automated Individual Decision-Making/Profiling Concerning Big Health Data. Developing an Algorithmic Culture

The Right Not to be Subject to Automated Individual Decision-Making/Profiling Concerning Big Health Data. Developing an Algorithmic Culture

Smart hospitals and A&amp;E departments in Hong Kong: Advantages, considerations and way forward

The concept of smart hospital is not new, but its development has unprecedentedly been accelerated by the COVID-19 pandemic. Throughput and performance dashboard on ED operation running in real-time manner would facilitate ED managers to maintain smooth ED operation or even predict and anticipate the service surge. The ED health big data would also have potential impact on the development of geriatric and ambulatory emergency medicine to accurately identify high-acuity patients and stratify low-risk patients for ambulatory care. [Extracted from the article] Copyright of Hong Kong Journal of Emergency Medicine is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

UbiEi-Edge: Human Big Data Decoding Using Deep Learning on Mobile Edge.

Human big data decoding is of great potential to reveal the complex patterns of human dynamics like physiological and biomechanical signals. In this study, we take special interest in brain visual dynamics, e.g., eye movement signals, and investigate how to leverage eye signal decoding to provide a voice-free communication possibility for ALS patients who lose ability to control their muscles. Due to substantial complexity of visual dynamics, we propose a deep learning framework to decode the visual dynamics when the user performs eye-writing tasks. Further, to enable real-time inference of the eye signals, we design and develop a mobile edge computing platform, called UbiEi-Edge, which can wirelessly receive the eye signals via low-energy Bluetooth, execute the deep learning algorithm, and visualize decoding results. This real word implementation, developed on an Android Phone, aims to provide real-time data streaming and automatic, real-time decoding of brain visual dynamics, thereby enabling a new paradigm for ALS patients to communicate with the external world. Our experiment has demonstrated the feasibility and effectiveness of the proposed novel mobile edge computing prototype. The study, by innovatively bridging AI, edge computing, and mobile health, will greatly advance the brain dynamics decoding-empowered human-centered computing and smart health big data applications.

Examining the barriers to accepting big health data from a health marketeer’s perspective

Studies have shown that the sharing of big health data can improve patient management across primary and secondary care sectors. It can also reduce costs and can enhance the medical research process. Unfortunately, many big health data initiatives are being impeded because of a range of complex issues. This study was initiated to identify the said issues and develop a tool for health marketers to use to negate the barriers in big healthcare data projects. The study demonstrates how the Interactive Communication Technology Adoption Model can be operationalized to support qualitative researchers.

Privacy Protection and Secondary Use of Health Data: Strategies and Methods.

Health big data has already been the most important big data for its serious privacy disclosure concerns and huge potential value of secondary use. Measurements must be taken to balance and compromise both the two serious challenges. One holistic solution or strategy is regarded as the preferred direction, by which the risk of reidentification from records should be kept as low as possible and data be shared with the principle of minimum necessary. In this article, we present a comprehensive review about privacy protection of health data from four aspects: health data, related regulations, three strategies for data sharing, and three types of methods with progressive levels. Finally, we summarize this review and identify future research directions.

Facing the National Recovery and Resilience Plan: Sources of Data, Indicators, and Participatory Strategies in Healthcare and Social Fields.

Innovation in governance and services should be the target of the Italian National Recovery and Resilience Plan. Monitoring processes, impacts, and outcomes requires a system of new indicators that are practical to collect. Secondary data sources, their availability, and their information potential should be evaluated, and primary sources should be implemented to supplement traditional disease surveillance. This work highlights the most relevant aspects for bridging the mismatching between complex community needs and current health/social supply and how those aspects could be faced. As a result, we propose a structured multi-phases process for setting the design and functionalities of a cooperative information system, built on the integration between secondary and primary data for informing policies about chronic low back pain (CLBP), a widely recognized determinant of disability and significant economic burden. In particular, we propose the Dress-KINESIS, a tool for improving community capacity development and participation that allows one to freely collect big health and social data and link it to existing secondary data. The system also may be able to monitor how the resources are distributed across different care sectors and suggest how to improve efficiency based on the patient’s CLBP risk stratification. Moreover, it is potentially customizable in other fields of health.

Medical Data Mining Course Development in Postgraduate Medical Education: Web-Based Survey and Case Study.

BackgroundMedical postgraduates’ demand for data capabilities is growing, as biomedical research becomes more data driven, integrative, and computational. In the context of the application of big data in health and medicine, the integration of data mining skills into postgraduate medical education becomes important.ObjectiveThis study aimed to demonstrate the design and implementation of a medical data mining course for medical postgraduates with diverse backgrounds in a medical school.MethodsWe developed a medical data mining course called “Practical Techniques of Medical Data Mining” for postgraduate medical education and taught the course online at Peking Union Medical College (PUMC). To identify the background knowledge, programming skills, and expectations of targeted learners, we conducted a web-based questionnaire survey. After determining the instructional methods to be used in the course, three technical platforms—Rain Classroom, Tencent Meeting, and WeChat—were chosen for online teaching. A medical data mining platform called Medical Data Mining - R Programming Hub (MedHub) was developed for self-learning, which could support the development and comprehensive testing of data mining algorithms. Finally, we carried out a postcourse survey and a case study to demonstrate that our online course could accommodate a diverse group of medical students with a wide range of academic backgrounds and programming experience.ResultsIn total, 200 postgraduates from 30 disciplines participated in the precourse survey. Based on the analysis of students’ characteristics and expectations, we designed an optimized course structured into nine logical teaching units (one 4-hour unit per week for 9 weeks). The course covered basic knowledge of R programming, machine learning models, clinical data mining, and omics data mining, among other topics, as well as diversified health care analysis scenarios. Finally, this 9-week course was successfully implemented in an online format from May to July in the spring semester of 2020 at PUMC. A total of 6 faculty members and 317 students participated in the course. Postcourse survey data showed that our course was considered to be very practical (83/83, 100% indicated “very positive” or “positive”), and MedHub received the best feedback, both in function (80/83, 96% chose “satisfied”) and teaching effect (80/83, 96% chose “satisfied”). The case study showed that our course was able to fill the gap between student expectations and learning outcomes.ConclusionsWe developed content for a data mining course, with online instructional methods to accommodate the diversified characteristics of students. Our optimized course could improve the data mining skills of medical students with a wide range of academic backgrounds and programming experience.

MODB: a comprehensive mitochondrial genome database for Mollusca.

Mollusca is the largest marine phylum, comprising about 23% of all named marine organisms, Mollusca systematics are still in flux, and an increase in human activities has affected Molluscan reproduction and development, strongly impacting diversity and classification. Therefore, it is necessary to explore the mitochondrial genome of Mollusca. The Mollusca mitochondrial database (MODB) was established for the Life and Health Big Data Center of Yantai University. This database is dedicated to collecting, sorting and sharing basic information regarding mollusks, especially their mitochondrial genome information. We also integrated a series of analysis and visualization tools, such as BLAST, MUSCLE, GENEWISE and LASTZ. In particular, a phylogenetic tree was implemented in this database to visualize the evolutionary relationships between species. The original version contains 616 species whose mitochondrial genomes have been sequenced. The database provides comprehensive information and analysis platform for researchers interested in understanding the biological characteristics of mollusks.Database URL: http://modb.ytu.edu.cn/

Influential Usage of Big Data and Artificial Intelligence in Healthcare.

Artificial intelligence (AI) is making computer systems capable of executing human brain tasks in many fields in all aspects of daily life. The enhancement in information and communications technology (ICT) has indisputably improved the quality of people's lives around the globe. Especially, ICT has led to a very needy and tremendous improvement in the health sector which is commonly known as electronic health (eHealth) and medical health (mHealth). Deep machine learning and AI approaches are commonly presented in many applications using big data, which consists of all relevant data about the medical health and diseases which a model can access at the time of execution or diagnosis of diseases. For example, cardiovascular imaging has now accurate imaging combined with big data from the eHealth record and pathology to better characterize the disease and personalized therapy. In clinical work and imaging, cancer care is getting improved by knowing the tumor biology and helping in the implementation of precision medicine. The Markov model is used to extract new approaches for leveraging cancer. In this paper, we have reviewed existing research relevant to eHealth and mHealth where various models are discussed which uses big data for the diagnosis and healthcare system. This paper summarizes the recent promising applications of AI and big data in medical health and electronic health, which have potentially added value to diagnosis and patient care.

Empirical Study on Theories and Techniques of Adolescent Physical Health Promotion under the Background of Big Data

In order to make my country’s youth health management more scientific, more standardized, and more effective, it is necessary to conduct in-depth research on the management of youth physical health promotion. Through the investigation and analysis of the physical health data of adolescents in my country, this paper proposes that the results of health intervention training as part of the empirical research on the construction of adolescent health big data management service system can effectively improve the relationship hypothesis of the physical health of adolescents and by selecting the example of our country’s “Adolescent Physical Health Data Analysis in 2020” for regression analysis. The research results show that the theory of adolescent physical health promotion can improve the physical health of adolescents by interfering with students’ physical exercise. In the processing of data, GBDT is suitable when the training set is relatively large, and as the sample size increases, the accuracy rate can reach 79.79%. In terms of the classification accuracy of male sitting forward bending promotion, the accuracy of the RF method is higher than that of GBDT. In terms of the promotion classification effect of boys’ 1000 m running, the RF method achieved the highest promotion accuracy rate of 77.62%. In the male pull-ups to promote the classification effect, when the proportion of the training set is 60%, the RF method gets the highest accuracy rate, which is 92.04%. The results of the classification effect for girls standing long jump promotion show that the classification accuracy rate for girls standing long jump promotion is between 51% and 56%. When the training set is less than 60%, the RF method is the best, the highest is 53.93%, and the rest is the GBDT method, the highest is 55.46%; in Macro-F1, the RF and GBDT indicators have their own advantages. In the promotion of the classification effect on the final fitness level of girls, the accuracy rates of RF and GBDT methods range from 90% to 96%, and the accuracy rates of the NN method range from 80% to 87%; when the practice rate reaches 80%, the GBDT method achieves the highest accuracy rate of 95.06%; on the Macro-F1 index, the GBDT method is obviously the best.

A Smart Data Pre-Processing Approach to Effective Management of Big Health Data in IoT Edge

A Smart Data Pre-Processing Approach to Effective Management of Big Health Data in IoT Edge