Health Big Data Research Articles

Comprehensive information management system for a medical research cohort biobank based on quality by design

BackgroundWith the development of big health and big data, cohort research has become a medical research hotspot. As an important repository of human genetic resources, biobanks must adapt to the requirements of large-scale and efficient operation. Thus, biobanks urgently need to design and build a legal, convenient, and efficient information management system.MethodsThis study applies the concept of “quality by design” to build a comprehensive biobank information management system based on the analysis of user requirements, legal and regulatory risks, and industry-standard requirements. The system integrates the management of scientific research projects, biological specimens, clinical information, quality control, and multi-dimensional information query and development. After 10 months of its operation, the comprehensive management system was evaluated through statistical analysis of the efficiency of the construction of the pregnancy–birth cohort and the quality of genetic resources.ResultsSince the system’s launch, the statistics on cohort construction efficiency show that the enrollment rate of eligible pregnant women has increased, and the rate of missing volunteers has dropped. The time needed to establish a 1000-person cohort (with complete biological samples and clinical information in early, middle, and late pregnancy) was reduced, and the effective tracking rate of the samples was 77.42%. The error rate of the deep cryogenic refrigerator decreased, with a clinical information integrity rate of 96.47%.ConclusionsThe comprehensive biobank information management system constructed with the “quality by design” concept is well suited to meet the requirements of medical research. This study provides a solution for designing a comprehensive information system for medical institutions’ biobanks.

Power of Public Investment in Curated Big Health Data

Power of Public Investment in Curated Big Health Data

Translational Research and Communities.

Impactful translational research requires new approaches to computational analysis and bioethics, both of which have been advanced by adoption of community-engagement strategies. Community knowledge and experience will hone data collection, research, and insights and accelerate the impact of derived translational applications to improve individual health, medical decision-making, and public health policy. In the context of translational research with big health data, meaningful community-researcher engagement will require developing and deploying coengagement tools across the research life cycle and developing approaches for novel coproduction.

Medical Application of Big Data: Between Systematic Review and Randomized Controlled Trials

In terms of medical health, we are currently living in the era of data science, which has brought tremendous change. Big data related to healthcare includes medical data, genome data, and lifelog data. Among medical data, public medical data is very important for actual research and medical policy reflection because it has data on a large number of patients and is representative. However, there are many difficulties in actually using such public health big data and designing a study, and conducting systematic review (SR) on the research topic can help a lot in the methodology. In this review, in addition to the importance of research using big data for the public interest, we will introduce important public medical big data in Korea and show how SR can be specifically applied in research using public medical big data.

Blockchain Technology for Embracing Healthcare 4.0

Nowadays, health data are fragmented and scattered across various systems and technologies. Complex support infrastructures, data system silos, and administrative bureaucracy have led to inefficiencies and inefficacies. This article aims to propose a blockchain platform to share big health data in real time between the authorized actors while ensuring a high level of health information protection. The proposed platform can offer a highly innovative approach to administering benefits and keeping all parties in sync. Implementing a blockchain for managing health data is a valuable support for diagnosing and monitoring the progress of the therapies of individual patients under treatment. In addition, it helps to reduce the time required for the exchange of information and keep the healthcare operations under control. Healthcare organizations and ecosystems will benefit from having a complete picture of a patient's health condition and reaching a global audience through the blockchain platform.

Effect of the varicella vaccination on the clinical characteristics of herpes zoster cases aged 20 years and under

To discuss the effect of varicella vaccination on the clinical characteristics of herpes zoster (shingles) cases aged 20 years and under, and analyze its clinical features. Based on the Yichang Health Big Data Platform, a descriptive study was conducted to collect the information of cases aged 20 years and under in three medical institutions of Yichang Central People's Hospital, Yichang First People's Hospital and Yichang Second People's Hospital from March 2019 to September 2020. According to the history of varicella vaccine, cases were divided into vaccination group and non-vaccination group, and their clinical features and outcomes were compared. The results showed that 46 shingles cases, aged from 7 to 20 years old, were included in this study. 26 males (56.5%), 20 females (43.5%), 15 cases in vaccination group (32.6%) and 31 cases in non-vaccination group (67.4%). 28 cases had thoracic involvement, followed by lumbar (n=8), cranial (n=7) involvements and extremities (n=7). The spread of herpes skin area: 2 cases involved too large area, 21 cases of 10 cm×10 cm, 14 cases of 5 cm×5 cm, 9 cases of 1 cm×1 cm. Herpes number: 26 cases had 10-49 herpes, followed by <10 herpes (n=9), uncountable herpes (n=7) and 50-99 herpes (n=4). The clinical course[M(Q1,Q3)] lasted 20.5 (13.5,24.8) d averagely, 5 cases had postherpetic neuralgia (PHN) and 1 case had respiratory complications. Shingles decrustation time was significantly shorter in vaccination group (Z=-2.01, P<0.05), and there was no significant difference in other characteristics by vaccination. In conclusion, the number and spread of shingles in most children and adolescents are less, and the complications such as PHN are less. Varicella vaccination can reduce the decrustation time and relieve shingles cases with some clinical symptoms.

Medical service utilization and costs of autism spectrum disorder: Evidence from hospital records in Beijing, China.

As more and more people are diagnosed with autism spectrum disorder (ASD), it is necessary to better understand their costs. Detailed information on medical service utilization and costs could aid in designing equitable, effective policies to support individuals with ASD and their families. In this retrospective analysis, individuals with a hospital encounter (outpatient visit or inpatient admission) were collected from Beijing Municipal Health Big Data and Policy Research Center (BMHBD), from January 1, 2017 to December 31, 2021. We analyzed the costs, hospital visits/admissions and their changing trends over 5 years. Poisson regression and logit regression were conducted to analyze the influencing factors of visits, admissions and costs. The study population consisted of 26,826 users of medical services (26,583 outpatients and 243 inpatients; mean age: 4.82 ± 3.47 years for outpatients; 11.62 ± 6.74 years for inpatients). 99.1% were outpatients(mean ± standard deviation (SD) costs per year: $422.06 ± $11.89), while 0.9% were inpatients (mean ± SD costs per year: $4411.71 ± $925.81). More than 50% of outpatients received medication and diagnostic testing services. Among those with an inpatient admission, 91% received treatment services. Medication costs were the major contributor to medical costs for adults. Diagnostic test and treatment costs were the major contributors for children and adolescents. The findings demonstrated a significant economic burden for those diagnosed with ASD and highlighted opportunities for improving the care of this vulnerable group. This study adds to the literature by focusing on age differences among health-care utilization in individuals with ASD.

The Potentials of Big Data in Achieving Universal Health Coverage in Iran

Big data refers to large and complex data sets that cannot be easily processed, managed, or analyzed using traditional data processing tools and techniques. The role of big data in health encompasses a wide range of applications that leverage large and complex data sets to improve health outcomes and healthcare delivery. The goal of universal health coverage (UHC) is to ensure that everyone has access to the health services they need when and where they need them, without financial barriers or catastrophic health expenditures that can lead to poverty. Big data in Iran’s health system has the potential to significantly improve healthcare delivery, enhance patient outcomes, and reduce healthcare costs. Moreover, big data can assist in ongoing monitoring and evaluating the progress toward achieving UHC goals in Iran.

Big health data for elderly employees job performance of SOEs: visionary and enticing challenges.

The method is providing and overview of the organization in the management perspective, within the health big data analysis, especially for the elderly employees, the organizations could sign the elderly employees within the right tasks, it reducing the costs by increasing the employees' job performance and organization performance. By addressing the importance role of big health data analytics (BDHA) in the healthcare system .moreover BDHA enables a patient's medical records to be searched in a dynamic, interactive manner. One billion records were made in two hours. Current clinical reporting compares large health data profiles and meta-big health data, giving health apps basic interfaces. A combination of Hadoop/MapReduce and HBase was used to generate the necessary hospital-specific large heath data. One billion (10TB) and three billion (30TB) HBase large health data files might be created in a week or a month using the concept. Apache Hadoop technologies tested simulated medical records. Inconsistencies reduced big health data. An encounter-centered big health database was difficult to set up due to the complicated medical system connections between big health data profiles. Associated with job performance such as the gender, current/past job positions and the health conditions are important. For genders the 66.36% of respondents in the experiments are females, 33.64 are males, majority of are healthy which are 66.97%, 30.58% are common geriatric disease, rest 2.45% are suffering from occupational disease; In terms of the current/past job positions, 20% of the respondents are working as accountant, followed by sales and management level. The Diagnostic and Statistical Manual, lists 157 distinct illnesses. Individuals may be diagnosed with one or more illnesses as a consequence of medical health professionals watching and analyzing their symptoms. It has been discovered that mental health issues have a negative impact on employees' job performance. For example, research on individuals with anxiety and depression has a direct impact on concentrations, decision-making process, and risk-taking behavior, which can be determined for job performance. Machine learning focuses on approaches that can be used to create accurate predictions about future characteristics based on previous training and post training. Principles such as job task and computational learning are crucial for machine learning algorithms that use a large amount of big health data.

Data like any other? Sexual and reproductive health, Big Data and the Sustainable Development Goals

This article examines the possibilities and pitfalls of using Big Data to address sexual and reproductive health concerns as related to the Sustainable Development Goals (SDGs), paying particular attention to contextual difference in development settings. The global datafication of sexual and reproductive life has taken place at great speed. However, evidential deficiencies and a lack of critical engagement of the specific issues around working with sexual and reproductive health Big Data in development contexts is apparent. Informed by critical data studies, and framed by a political economy perspective which calls attention to power structures, we seek to deepen our understanding of the role and challenges that Big Data around sexual and reproductive health in the Low and Middle-Income Countries can play in addressing the SDGs. First, we explore the ways in which sexual datafication processes produce Big Data. We then consider how such Big Data could directly contribute to addressing the SDGs beyond simply monitoring and evaluating. Next, we unpick how the sensitive and stigmatised nature of sexual and reproductive health can have ramifications in data-driven contexts where significant power asymmetries exist. By doing so, we provide a more nuanced articulation of the challenges of datafication by contextualising the stigma around sexual and reproductive in a datafied context. We argue that whilst Big Data in relation to sexual and reproductive health shows potential to support the SDGs, there are specificities that must be considered to ensure that the push for data-driven approaches does no harm.

Design of College Students' physical health monitoring APP based on sports health big data

AbstractAt present, the living habits of college students are relatively poor, and the amount of exercise is reduced, leading to their physical fitness getting worse and worse. Therefore, people began to study the physical health monitoring of college students. Machine learning and high‐performance computing in medical applications provide technical support for intelligent medical technology. With the rapid development of computer network, human beings have entered the information and digital era, and sports health big data has become more and more popular. The combination of sports health big data and student physical health monitoring technology, in a sense, can realize automatic data processing through intelligent medical and sports health database and other information technologies, thus promoting the popularization of health monitoring technology. However, the current health monitoring equipment has many problems, such as complex collection, low accuracy and limited processing of health data. To solve this problem, this paper developed an Application (APP) based on sports health big data technology that can monitor multiple vital signs such as human heart rate and body temperature, and analyze the physical health of college students, so that college students can easily understand their health status in daily life, so as to promote the healthy development of students' physique and encourage them to actively participate in physical exercise. The experiment proved that, in the heart rate monitoring, when the speed is 6 km/h, the error rate of the college students' physical health monitoring APP designed in this paper is 8.15%. The average accuracy rate of student steps monitoring is 97.12%. This showed the accuracy and availability of the APP's monitoring function for human vital signs. It has certain application value and significance to help students improve their physical quality.

Predicting drug adverse effects using a new Gastro-Intestinal Pacemaker Activity Drug Database (GIPADD)

Electrical data could be a new source of big-data for training artificial intelligence (AI) for drug discovery. A Gastro-Intestinal Pacemaker Activity Drug Database (GIPADD) was built using a standardized methodology to test drug effects on electrical gastrointestinal (GI) pacemaker activity. The current report used data obtained from 89 drugs with 4867 datasets to evaluate the potential use of the GIPADD for predicting drug adverse effects (AEs) using a machine-learning (ML) approach and to explore correlations between AEs and GI pacemaker activity. Twenty-four “electrical” features (EFs) were extracted using an automated analytical pipeline from the electrical signals recorded before and after acute drug treatment at three concentrations (or more) on four-types of GI tissues (stomach, duodenum, ileum and colon). Extracted features were normalized and merged with an online side-effect resource (SIDER) database. Sixty-six common AEs were selected. Different algorithms of classification ML models, including Naïve Bayes, discriminant analysis, classification tree, k-nearest neighbors, support vector machine and an ensemble model were tested. Separated tissue models were also tested. Averaging experimental repeats and dose adjustment were performed to refine the prediction results. Random datasets were created for model validation. After model validation, nine AEs classification ML model were constructed with accuracy ranging from 67 to 80%. EF can be further grouped into ‘excitatory’ and ‘inhibitory’ types of AEs. This is the first time drugs are being clustered based on EF. Drugs acting on similar receptors share similar EF profile, indicating potential use of the database to predict drug targets too. GIPADD is a growing database, where prediction accuracy is expected to improve. The current approach provides novel insights on how EF may be used as new source of big-data in health and disease.

Methodological approaches based on machine learning in the use of big data in public health: a systematic review

Methodological approaches based on machine learning in the use of big data in public health: a systematic review

A novel sparse linear mixed model for multi-source mixed-frequency data fusion in telemedicine

Digital health and telemonitoring have resulted in a wealth of information to be collected to monitor, manage, and improve human health. The multi-source mixed-frequency health data overwhelm the modeling capacity of existing statistical and machine learning models, due to many challenging properties. Although predictive analytics for big health data plays an important role in telemonitoring, there is a lack of rigorous prediction model that can automatically predicts patients’ health conditions, e.g., Disease Severity Indicators (DSIs), from multi-source mixed-frequency data. Sleep disorder is a prevalent cardiac syndrome that is characterized by abnormal respiratory patterns during sleep. Although wearable devices are available to administrate sleep studies at home, the manual scoring process to generate the DSI remains a bottleneck in automated monitoring and diagnosis of sleep disorder. To address the multi-fold challenges for precise prediction of the DSI from high-dimensional multi-source mixed-frequency data in sleep disorder, we propose a sparse linear mixed model that combines the modified Cholesky decomposition with group lasso penalties to enable joint group selection of fixed effects and random effects. A novel Expectation Maximization (EM) algorithm integrated with an efficient Majorization Maximization (MM) algorithm is developed for model estimation of the proposed sparse linear mixed model with group variable selection. The proposed method was applied to the SHHS data for telemonitoring and diagnosis of sleep disorder and found that a few significant feature groups that are consistent with prior medical studies on sleep disorder. The proposed method also outperformed a few benchmark methods with the highest prediction accuracy.

Obstacles to Health Big Data Utilization Based on the Perceptions and Demands of Health Care Workers in South Korea: Web-Based Survey Study.

This study focuses on the potential of health big data in the South Korean context. Despite huge data reserves and pan-government efforts to increase data use, the utilization is limited to public interest research centered in public institutions that have data. To increase the use of health big data, it is necessary to identify and develop measures to meet the various demands for such data from individuals, private companies, and research institutes. The aim of this study was to identify the perceptions of and demands for health big data analysis and use among workers in health care-related occupations and to clarify the obstacles to the use of health big data. From May 8 to May 18, 2022, we conducted a web-based survey among 390 health care-related workers in South Korea. We used Fisher exact test and analysis of variance to estimate the differences among occupations. We expressed the analysis results by item in frequency and percentage and expressed the difficulties in analyzing health big data by mean and standard deviation. The respondents who revealed the need to use health big data in health care work-related fields accounted for 86.4% (337/390); 65.6% (256/390) of the respondents had never used health big data. The lack of awareness about the source of the desired data was the most cited reason for nonuse by 39.6% (153/386) of the respondents. The most cited obstacle to using health big data by the respondents was the difficulty in data integration and expression unit matching, followed by missing value processing and noise removal. Thus, the respondents experienced the greatest difficulty in the data preprocessing stage during the health big data analysis process, regardless of occupation. Approximately 91.8% (358/390) of the participants responded that they were willing to use the system if a system supporting big data analysis was developed. As suggestions for the specific necessary support system, the reporting and provision of appropriate data and expert advice on questions arising during the overall process of big data analysis were mentioned. Our findings indicate respondents' high awareness of and demand for health big data. Our findings also reveal the low utilization of health big data and the need to support health care workers in their analysis and use of such data. Hence, we recommend the development of a customized support system that meets the specific requirements of big data analysis by users such as individuals, nongovernmental agencies, and academia. Our study is significant because it identified important but overlooked failure factors. Thus, it is necessary to prepare practical measures to increase the utilization of health big data in the future.

469 Use of data-driven technology to optimise DOACs in morbidly obese patients

Abstract Introduction Despite the advantages of Direct Oral Anticoagulants (DOACs) over older classes of anticoagulants, clinical experience is limited in special populations; data from landmark trials on safety and efficacy are relatively scarce (compared to warfarin). This makes it challenging for clinicians to prescribe the right DOAC at the right dose for such patients (e.g., morbidly obese patients) (1). Insights derived from analysing real-world data have proven to be a vital source of clinical evidence backing the recommendation of medications (2). Therefore, data-driven technologies like machine learning can harness big data in electronic health records (EHRs) to optimise DOAC therapy and improve clinical outcomes. Aim The study aims to accurately predict clinical outcomes in morbidly obese patients, and identify the key variables in the model for optimising the safety and efficacy of Direct Oral Anticoagulant (DOAC) doses. Methods An observational, retrospective cohort study was carried out in partnership with an NHS Trust. Based on eligibility criteria, the dataset of morbidly obese patients on DOACs was extracted from EHRs, pre-processed and analysed considering the access granted. After partitioning the entire dataset into a 70:30 split, the training dataset (70%) was run through selected machine learning (ML) classifiers (Random Forest, decision trees, K-nearest neighbours (KNN), bootstrap aggregation algorithm, gradient boosting classifier, support vector machines, and logistic regression) to rank variables, and derive predictions which were evaluated against the test dataset (30%). A multivariate regression model was used to adjust for confounders and to explore the relationships between DOAC regimens and clinical outcomes. Results We identified 4,275 morbidly obese patients out of n=97,413 records overall. The bootstrap aggregation, decision trees, and random forest classifiers (from the ML algorithms tested) achieved superior prediction accuracies (98.6%, 97.9%, and 98.3%, respectively) for the individual DOAC doses, with excellent values for precision, recall, and F1 scores (performance metrics). The most important characteristics in the model for predicting mortality and stroke were age, treatment days, and length of stay. Among DOACs, apixaban (84%) was the most frequently prescribed DOAC followed by rivaroxaban (15%). Apixaban 2.5 mg (twice daily) received the highest ranking for relevance to mortality, while it raised the mortality risk (OR 1.430, 95% CI: 1.181, 1.1.732, p=0.001). There were mixed results for apixaban 5mg (twice daily), the most widely prescribed dose of apixaban (54%), with significantly reduced risk of mortality (OR 0.751, 95% CI: 0.632, 0.905, p=0.003), but significantly increased risk of stroke events (OR 32.457, 95% CI: 17.083-61.664, p=0.001). Conclusion Given the large sample size—a strength of our study, data-driven technologies were successfully employed in predicting the safety and efficacy of DOACs in morbidly obese patients using the real-world dataset; the key variables in the model for optimising clinical outcomes were identified. However, the limitations in our study, such as reporting errors, selection bias, and confounding bias, were not ruled out. Therefore, confirmatory studies (e.g., external validation with prospective data) are needed to confirm findings and provide a sound basis for universal deployment in clinical settings.

Research on Key Technologies of Health Big Data Information Sharing for Chronic Patients

Background: With the improvement of people's living conditions and the intensification of the aging level, chronic diseases such as hypertension and diabetes are constantly troubling human health. Chronic diseases have become the biggest problem for elderly patients, and there are many problems in the sharing of health information in the self-management of chronic diseases. Objective: To find a promising and practical way to share health information of chronic disease cloud, solve the problem of chronic disease health information sharing, so as to promote the long-term treatment of chronic disease, improve the quality of life of patients, protect the privacy of patients, and realize the common benefits of doctors, patients, medical institutions and medical research institutions. Methods: A patient-centered hybrid cloud health information sharing platform was proposed. The hybrid cloud technology was used to realize the sharing of chronic disease health information among doctors, patients, medical institutions and medical research institutions, and a prototype system was realized. Results: The scheme and its prototype system realized the recording and privacy sharing of health data by using hybrid cloud, and solved many problems of health information sharing in chronic disease self-management. Conclusion: Through system verification, the proposed hybrid cloud-based health information sharing method for chronic patients will greatly improve the utilization rate of information resources, and may change the current medical service model facing many challenges.

Genetic characterization of varicella-zoster virus in people aged 20 years and under in Yichang City of Hubei Province, 2019-2020

Objective: To analyze the genetic characteristics of varicella-zoster virus (VZV) in people aged 20 years and under in Yichang City of Hubei Province from 2019 to 2020. Methods: Based on the Yichang Health Big Data Platform, we investigated cases 20 and under clinically diagnosed as herpes zoster in three hospitals from March 2019 to September 2020. Collecting vesicle fluid and throat swab samples of the cases and completing questionnaires to obtain basic information. Real-time fluorescent quantitative PCR was used for positive identification of the virus. PCR amplification of VZV's open reading frame (ORF) and sequencing of the products to determine the VZV genotype. Analyze mutations at some specific single nucleotide polymorphism (SNP) sites. Results: Among 46 cases of herpes zoster, the male to female ratio was 1.3∶1 (26∶20) and the age ranged from 7 to 20 years old. Fifteen cases had been vaccinated against varicella, including 13 and 2 cases of 1 and 2 doses, respectively. VZV strains were detected in 34 samples (73.91%), all belonging to Clade 2. Phylogenetic tree analysis of the nucleotide of ORF22 showed, compared with Clade 2 referenced strains, the sequence matching degree of nucleotide for all 34 samples was 99.0% to 100.0%. Conclusion: The main VZV strain causing herpes zoster in people aged 20 years and under in Yichang from 2019 to 2020 was Clade 2.

An ontology-based approach to designing a NoSQL database for semi-structured and unstructured health data.

With the advent of ICT-based healthcare applications, various formats of health data are generated every day in huge volume. Such data, consisting of unstructured, semi-structured and structured data, has every characteristic of Big data. NoSQL databases are generally preferred for storing such type of health data with the objective of improving query performance. However, for efficient retrieval and processing of Big Health Data and for resource optimization, suitable data models and design of the NoSQL databases are important requirements. Unlike relational databases, no standard methods or tools exist for NoSQL database design. In this work, we adopt an ontology-based schema design approach. We propose that an ontology, which captures the domain knowledge, be used for developing a health data model. An ontology for primary healthcare is described in this paper. We also propose an algorithm for designing the schema of a NoSQL database, keeping in mind the characteristics of the target NoSQL store, using a related ontology, a sample query set, some statistical information of the queries, and performance requirements of the query set. The ontology proposed by us for primary healthcare domain and the above mentioned algorithm along with a set of queries are used for generating a schema targeting MongoDB datastore. The performance of the proposed design is compared with a relational model developed for the same primary healthcare data and the effectiveness of our proposed approach is demonstrated. The entire experiment has been carried out on MongoDB cloud platform.

Secure Electronic Medical Records Using Lattice Cryptography and Fog Computing Techniques

The usage of Information Technologies (IT), as well as data has increased over the years to improve businesses and human life generally thereby increasing productivity. In the health sector, Electronic Health Records (EHR) has helped in collecting demographic medical data which helps healthcare practitioners to provide quality health care. The EHR generates lots of medical data and are analyzed and processed to form big health data and sent to Agencies, Government to influence decisions in improving quality of life and to predict or prevent premature deaths and disease development. In this research work, we propose a model using lattice-based cryptography to encrypt health big data and deploy a decoy model which will be coined in fog computing facility to serve as honeypot or trap machine to attract attackers.