Medical Informatics Research Research Articles

The Development History and Research Tendency of Medical Informatics: Topic Evolution Analysis.

BackgroundMedical informatics has attracted the attention of researchers worldwide. It is necessary to understand the development of its research hot spots as well as directions for future research.ObjectiveThe aim of this study is to explore the evolution of medical informatics research topics by analyzing research articles published between 1964 and 2020.MethodsA total of 56,466 publications were collected from 27 representative medical informatics journals indexed by the Web of Science Core Collection. We identified the research stages based on the literature growth curve, extracted research topics using the latent Dirichlet allocation model, and analyzed topic evolution patterns by calculating the cosine similarity between topics from the adjacent stages.ResultsThe following three research stages were identified: early birth, early development, and rapid development. Medical informatics has entered the fast development stage, with literature growing exponentially. Research topics in medical informatics can be classified into the following two categories: data-centered studies and people-centered studies. Medical data analysis has been a research hot spot across all 3 stages, and the integration of emerging technologies into data analysis might be a future hot spot. Researchers have focused more on user needs in the last 2 stages. Another potential hot spot might be how to meet user needs and improve the usability of health tools.ConclusionsOur study provides a comprehensive understanding of research hot spots in medical informatics, as well as evolution patterns among them, which was helpful for researchers to grasp research trends and design their studies.

Analysis of E-mental health research: mapping the relationship between information technology and mental healthcare

BackgroundE-mental healthcare is the convergence of digital technologies with mental health services. It has been developed to fill a gap in healthcare for people who need mental wellbeing support that may not otherwise receive psychological treatment. With an increasing number of e-mental healthcare and research, this study aimed to investigate the trends of an e-mental health research field that integrates interdisciplinary fields and to examine the information technologies is being used in mental healthcare. To achieve the research objectives, bibliometric analysis, information extraction, and network analysis were applied to analyze e-mental health research data.MethodsE-mental health research data were obtained from 3663 bibliographic records from the Web of Science (WoS) and 3172 full-text articles from PubMed Central (PMC). The text mining techniques used for this study included bibliometric analysis, information extraction, and visualization.ResultsThe e-mental health research topic trends primarily involved e-health care services and medical informatics research. The clusters of research comprised 16 clusters, which refer to mental sickness, e-health, diseases, information technology (IT), and self-management. The information extraction analysis revealed a triple relation with IT and biomedical domains. Betweenness centrality was used as a measure of network graph centrality, based on the shortest path to rank the important entities and triple relation; nodes with higher betweenness centrality had greater control over the network because more information passes through that node. The IT entity-relations of “mobile” had the highest score at 0.043466. The top pairs were related to depression, mobile health, and text message.ConclusionsE-mental related publications were associated with various research fields, such as nursing, psychology, medical informatics, computer science, telecommunication, and healthcare innovation. We found that trends in e-mental health research are continually rising. These trends were related to the internet of things (IoT) and mobile applications (Apps), which were applied for mental healthcare services. Moreover, producing AI and machine learning for e-mental healthcare were being studied. This work supports the appropriate approaches and methods of e-mental health research that can help the researcher to identify important themes and choose the best fit with their own survey work.

Disability Affirmative Action Requirements for the U.S. HHS and Academic Medical Centers.

Medical professionals exert enormous influence over the lives of persons with disabilities by providing medical care and by generating medical research and medical information about them that form the basis for policies and practices that affect their everyday lives. Yet persons with disabilities remain substantially underrepresented within the U.S. Department of Health and Human Services' workforce, among academic medical centers' employees, and in the health professions. The U.S. HHS and academic medical centers appear to be noncompliant with their obligations to engage in disability affirmative action under sections 501 and 503 of the Rehabilitation Act. This essay describes these requirements and contrasts them with medical leaders' "well-being" agendas. It also describes ongoing medical research on "burnout" being conducted on medical employees and further enabled through the Dr. Lorna Breen Health Care Provider Protection Act. The essay calls for a wholesale change in approaches to persons with disabilities within the health and medical professions.

An Empirical Study of a Trustworthy Cloud Common Data Model Using Decentralized Identifiers

The Conventional Cloud Common Data Model (CDM) uses a centralized method of user identification and credentials. This needs to be solved in a decentralized way because there are limitations in interoperability such as closed identity management and identity leakage. In this paper, we propose a DID (Decentralized Identifier)-based cloud CDM that allows researchers to securely store medical research information by authenticating their identity and to access the CDM reliably. The proposed service model is used to provide the credential of the researcher in the process of creating and accessing CDM data in the designed secure cloud. This model is designed on a DID-based user-centric identification system to support the research of enrolled researchers in a cloud CDM environment involving multiple hospitals and laboratories. The prototype of the designed model is an extension of the encrypted CDM delivery method using DID and provides an identification system by limiting the use cases of CDM data by researchers registered in cloud CDM. Prototypes built for agent-based proof of concept (PoC) are leveraged to enhance security for researcher use of ophthalmic CDM data. For this, the CDM ID schema and ID definition are described by issuing IDs of CDM providers and CDM agents, limiting the IDs of researchers who are CDM users. The proposed method is to provide a framework for integrated and efficient data access control policy management. It provides strong security and ensures both the integrity and availability of CDM data.

Method for Trifuzol-Neo assay determination by GC-MS

The derivatives of 1,2,4-triazole are of the great scientific interest in the fields of pharmaceutics, agricultural chemistry, and construction The production of Trifuzol-Neo has already been launched, and it is now commercially available. Compounds based on 1,2,4-triazole may also be useful in veterinary practice due to their antiviral activity. Trifuzol-Neo, or piperidinium ([5-(2-furanyl)-4-phenyl-4Н-1,2,4-triazol-3-yl]thio)acetate (Figure 1), has already proved to be an effective immunostimulator for poultry.Since that, the quality control of raw materials and the ready product is required during the production stage. Currently, there is only one method for determination of the assay of Trifuzol-Neo, which involves HPLC-DMD (Center of Medical Research Information and Patent and Licensing Practice of Ukraine. The main disadvantages of HPLC-DMD are the use of relatively high amounts of costly solvents (acetonitrile in this case), considerable baseline instability, and time-consuming system stabilization. Gas-chromatography is able to avoid these constrictions since it involves gas as a mobile phase, which is a more cost-effective and reproducible alternative. The newly developed GC/MS method for Trifuzol-Neo assay determination consists of the following steps: weighing 20.0mg of Trifuzol-Neo powder, dillution in 20mL methanol, injection of 0.5uL of the obtained solution to GC column (at least three times). The same operations are done with a Trifuzol-Neo standard.

Regional differences in the incidence of asthma exacerbations in Japan: A heat map analysis of healthcare insurance claims data

BackgroundIn Japan, regional differences in asthma mortality have been reported; however, regional differences in asthma exacerbations have not been studied extensively. Therefore, using a health insurance claims database, we investigated the regional differences in the incidence of asthma exacerbations in Japan. MethodsThis study used data from Medi-Scope (Japan Medical Information Research Institute Inc., Japan)—a nationwide health insurance claims database. Patients with asthma at the index date (the latest date of an asthma-related prescription with an asthma diagnosis before October 1, 2018) were included in the analysis. The pre-index period was defined as 1 year before the index date, and the follow-up period as 1 year after the index date. The incidence of asthma exacerbation events was analyzed for each region. ResultsThe primary analysis population comprised 24,883 patients who were continuously prescribed ICS or ICS/LABA at least four times during the pre-index period. The incidence rate of asthma exacerbations with hospitalization was the highest in Chugoku (2.95/100 person-years [95% CI, 1.97–4.43]) and the lowest in Kanto (1.52/100 person-years [95% CI, 1.26–1.83]). The incidence rate of asthma exacerbations for the composite outcome of hospitalization, injectable corticosteroid prescription, and oral corticosteroid burst was the highest in Fukui (105.00/100 person-years [95% CI, 64.53–170.85]) and the lowest in Nagasaki (15.69/100 person-years [95% CI, 10.84–22.72]). ConclusionsRegional differences in the incidence of asthma exacerbations as well as their treatments were observed in Japan.

Bibliometric analysis of diabetes Economics Evaluation Literature

Objective To describe the current status of diabetes economics evaluation research through bibliometric analysis and provide information for medical researchers and policy makers. Method In the Web of Science platform, bibliometric analysis and visualization technologies with the DDA and Pajek's tools were used to analyze the publication year and journals, countries, core authors and research hotspots of the literatures of diabetes economics evaluation published since 1980. Result The earliest literature in this field was published in 1980 and the number of literatures has increased rapidly since 2002. Nearly 20% of the literatures on diabetes economics evaluation were published in the top 15 journals and 150 core authors were found in this field. The developed countries had outstanding performance on diabetes economics evaluation. The institutions with the highest number of publications were Harvard University and University of London. In the co-occurrence network constructed by keywords, there were 4 subject concepts: “Evaluation of Diabetes Economics”, “Diabetes Self-management and Drug Treatment”, “Diabetes Prevention and Early Warning”, “Diabetes Related Metabolic Diseases” and “Diabetes Screening Indicators”. Conclusion The research of diabetes economics evaluation has developed rapidly since 2000. There have been some specialized journals, core authors and research institutions with global influence in this field. The developed countries, such as the United States and the United Kingdom, play an important role in the international cooperation on diabetes economics evaluation., but there is still less focused research hotspots.

Measuring the Interactions Between Health Demand, Informatics Supply, and Technological Applications in Digital Medical Innovation for China: Content Mapping and Analysis.

BackgroundThere were 2 major incentives introduced by the Chinese government to promote medical informatics in 2009 and 2016. As new drugs are the major source of medical innovation, informatics-related concepts and techniques are a major source of digital medical innovation. However, it is unclear whether the research efforts of medical informatics in China have met the health needs, such as disease management and population health.ObjectiveWe proposed an approach to mapping the interplay between different knowledge entities by using the tree structure of Medical Subject Headings (MeSH) to gain insights into the interactions between informatics supply, health demand, and technological applications in digital medical innovation in China.MethodsAll terms under the MeSH tree parent node “Diseases [C]” or node “Health [N01.400]” or “Public Health [N06.850]” were labelled as H. All terms under the node “Information Science [L]” were labelled as I, and all terms under node “Analytical, Diagnostic and Therapeutic Techniques, and Equipment [E]” were labelled as T. The H-I-T interactions can be measured by using their co-occurrences in a given publication.ResultsThe H-I-T interactions in China are showing significant growth and a more concentrated interplay were observed. Computing methodologies, informatics, and communications media (such as social media and the internet) constitute the majority of I-related concepts and techniques used for resolving the health promotion and diseases management problems in China. Generally there is a positive correlation between the burden and informatics research efforts for diseases in China. We think it is not contradictory that informatics research should be focused on the greatest burden of diseases or where it can have the most impact. Artificial intelligence is a competing field of medical informatics research in China, with a notable focus on diagnostic deep learning algorithms for medical imaging.ConclusionsIt is suggested that technological transfers, namely the functionality to be realized by medical/health informatics (eg, diagnosis, therapeutics, surgical procedures, laboratory testing techniques, and equipment and supplies) should be strengthened. Research on natural language processing and electronic health records should also be strengthened to improve the real-world applications of health information technologies and big data in the future.

Visualization of Similar Patients in a Clinical Decision Support System for Rare Diseases - A Focus Group Study.

The diagnosis of patients with rare diseases is often delayed. A Clinical Decision Support System using similarity analysis of patient-based data may have the potential to support the diagnosis of patients with rare diseases. This qualitative study has the objective to investigate how the result of a patient similarity analysis should be presented to a physician to enable diagnosis support. We conducted a focus group with physicians practicing in rare diseases as well as medical informatics researchers. To prepare the focus group, a literature search was performed to check the current state of research regarding visualization of similar patients. We then created software-mockups for the presentation of these visualization methods for the discussion within the focus group. Two persons took independently field notes for data collection of the focus group. A questionnaire was distributed to the participants to rate the visualization methods. The results show that four visualization methods are promising for the visualization of similar patients: "Patient on demand table", "Criteria selection", "Time-Series chart" and "Patient timeline. "Patient on demand table" shows a direct comparison of patient characteristics, whereas "Criteria selection" allows the selection of different patient criteria to get deeper insights into the data. The "Time-Series chart" shows the time course of clinical parameters (e.g. blood pressure) whereas a "Patient timeline" indicates which time events exist for a patient (e.g. several symptoms on different dates). In the future, we will develop a software-prototype of the Clinical Decision Support System to include the visualization methods and evaluate the clinical usage.

Risk Factors for Developing Posttransplant Diabetes After Pediatric Kidney Transplant in a Canadian Tertiary Care Children’s Hospital Between 1995 and 2016

BackgroundPosttransplant diabetes mellitus (PTDM) is a serious complication in kidney transplant recipients (KTRs) due to its negative impact on graft and patient survival. Although reported in 3% to 20% of pediatric KTRs, it has not been as well characterized in adults. In this study we describe incidence and risk factors associated with development of PTDM in pediatric KTRs. MethodsThis work is a retrospective cohort study of nondiabetic pediatric patients, aged 6 months to 19 years, who underwent a first kidney transplant during 1995 to 2016. We estimated the cumulative incidence rate and used multivariable logistic regression to identify the diabetogenic risk factors for PTDM. ResultsA total of 142 KTRs were included in this study. The cumulative incidence of PTDM was 31% and 14.1% in the first and second year posttransplant, respectively. Significant risk factors for PTDM in the first year after transplant included: dysglycemia in the first 8 to 30 days posttransplant (adjusted odds ratio [aOR], 3.02; 95% confidence interval [CI], 1.21 to 7.53; p=0.018) and use of sirolimus in the first 30 days posttransplant (aOR, 5.33; 95% CI, 1.16 to 24.35; p=0.031). No significant association was found with typical diabetogenic factors. ConclusionsThe incidence of PTDM is high among pediatric KTRs. Independent risk factors associated with PTDM included meeting the criteria for dysglycemia or diabetes and sirolimus use in the first month posttransplant. Typical diabetogenic risk factors for type 2 diabetes were not associated with increased risk. This study provides valuable information for posttransplant medical care and future research.

Research on Intelligent Prescription Review System Based on Medical Big Data

According to statistics, the incidence of adverse drug reactions in my country is 10%-30%, and about 190,000 people die from serious adverse drug reactions each year. Therefore, relying on the pharmaceutical knowledge base to provide clinicians with real-time rationalized medication diagnosis and treatment opinions, and to prevent the occurrence of contraindications, overdose and other wrong medications, is an important topic worthy of medical information research. Therefore, this article introduces the expert system and ontology technology into the application of smart medical services, and builds a prescription intelligent review system based on the pharmacy knowledge base, and through the construction of prescription ontology, pharmacy knowledge base modeling, and review process reorganization During the process, AI intelligent review of prescriptions issued by doctors is carried out in real time.

Polypharmacy Is Associated With Amiodarone-Induced Hypothyroidism.

Background: Amiodarone is rich in iodine, so in clinical practice amiodarone-induced hypothyroidism (AIH) is a major side effect. This drug is used in patients with arrhythmias, especially atrial fibrillation, the most common sustained arrhythmia. Polypharmacy, which can result in complex drug-drug interactions, occurs in more than 70% of the patients with atrial fibrillation. Therefore, polypharmacy may be involved in the expression of AIH. In this study, we investigated the association between polypharmacy and AIH.Methods: We conducted a retrospective study using data from January 2006 to May 2020 collected from a large, organized database of prescriptions constructed by the Japan Medical Information Research Institute, Inc. (Tokyo, Japan). To investigate the association between number of prescribed drugs with amiodarone and AIH, we divided patients into two groups: polypharmacy (≥ 5 prescribed drugs) and non-polypharmacy (< 5 prescribed drugs). We then performed a sequence symmetry analysis on the two groups: incident thyroxine after incident amiodarone and incident thyroxine before incident amiodarone. Finally, we conducted a case-control study on two further groups: those prescribed thyroxine after incident amiodarone (AIH group; n=555) and those not prescribed thyroxine after incident amiodarone (non-AIH group; n=6,192).Results: Sequence symmetry analysis revealed a significant association between amiodarone and thyroxine in both the polypharmacy and non-polypharmacy groups. The ranges for the adjusted sequence ratio in the two groups were 12.0-16.7 and 7.3-9.0, respectively. The case-control study showed that ≥5 prescribed drugs at the first prescription of amiodarone were found to significantly increase the odds of AIH (odds ratio: 1.48, 95% confidence interval: 1.18-1.84).Conclusion: Polypharmacy was suggested as an independent risk factor for AIH. Careful assessment of the appropriateness of prescription is warranted.

PUBLICATION OF RESEARCH DATA MANAGEMENT IN OPEN ACCESS JOURNAL ANALYSIS BASED ON SCOPUS DATA

The study aims to determine: (1) the number of open access resources for research data management publications indexed by Scopus, including the year of publication, source of publication, authors, institutions, countries, types of documents and funding agencies; (2) mapping research data management based on keywords. The results of the study showed that the number of open access resources for research data management publications has started since 1981 and the number has continued to increase starting in 2014 and the highest number occurred in 2019, namely 49 publications. The most publicized journals that open access to research data management was the Data Science Journal, which was 11 publications. The most productive author of conducting research data management publications was Cox, A.M. and Pinfield, S. The largest institutions contributing to the publication of open access research data management were the University of Toronto and New York University. The countries that contributed the most were the United States with 50 publications, then China with 38 publications. The most open access research data management in the form of articles as many as 107 and 37 conference paper publications. The institutions that provided the most funding sponsors were the Deutsche Forschungsgemeinschaft and the National Science Foundation. The results of keyword mapping with VOSViewer showed that big data, research data management, information management, data management, medical research topics, software, information processing, and metadata were the most researched topics.

Unpacking Prevalence and Dichotomy in Quick Sequential Organ Failure Assessment and Systemic Inflammatory Response Syndrome Parameters: Observational Data-Driven Approach Backed by Sepsis Pathophysiology.

BackgroundConsidering morbidity, mortality, and annual treatment costs, the dramatic rise in the incidence of sepsis and septic shock among intensive care unit (ICU) admissions in US hospitals is an increasing concern. Recent changes in the sepsis definition (sepsis-3), based on the quick Sequential Organ Failure Assessment (qSOFA), have motivated the international medical informatics research community to investigate score recalculation and information retrieval, and to study the intersection between sepsis-3 and the previous definition (sepsis-2) based on systemic inflammatory response syndrome (SIRS) parameters.ObjectiveThe objective of this study was three-fold. First, we aimed to unpack the most prevalent criterion for sepsis (for both sepsis-3 and sepsis-2 predictors). Second, we intended to determine the most prevalent sepsis scenario in the ICU among 4 possible scenarios for qSOFA and 11 possible scenarios for SIRS. Third, we investigated the multicollinearity or dichotomy among qSOFA and SIRS predictors.MethodsThis observational study was conducted according to the most recent update of Medical Information Mart for Intensive Care (MIMIC-III, Version 1.4), the critical care database developed by MIT. The qSOFA (sepsis-3) and SIRS (sepsis-2) parameters were analyzed for patients admitted to critical care units from 2001 to 2012 in Beth Israel Deaconess Medical Center (Boston, MA, USA) to determine the prevalence and underlying relation between these parameters among patients undergoing sepsis screening. We adopted a multiblind Delphi method to seek a rationale for decisions in several stages of the research design regarding handling missing data and outlier values, statistical imputations and biases, and generalizability of the study.ResultsAltered mental status in the Glasgow Coma Scale (59.28%, 38,854/65,545 observations) was the most prevalent sepsis-3 (qSOFA) criterion and the white blood cell count (53.12%, 17,163/32,311 observations) was the most prevalent sepsis-2 (SIRS) criterion confronted in the ICU. In addition, the two-factored sepsis criterion of high respiratory rate (≥22 breaths/minute) and altered mental status (28.19%, among four possible qSOFA scenarios besides no sepsis) was the most prevalent sepsis-3 (qSOFA) scenario, and the three-factored sepsis criterion of tachypnea, high heart rate, and high white blood cell count (12.32%, among 11 possible scenarios besides no sepsis) was the most prevalent sepsis-2 (SIRS) scenario in the ICU. Moreover, the absolute Pearson correlation coefficients were not significant, thereby nullifying the likelihood of any linear correlation among the critical parameters and assuring the lack of multicollinearity between the parameters. Although this further bolsters evidence for their dichotomy, the absence of multicollinearity cannot guarantee that two random variables are statistically independent.ConclusionsQuantifying the prevalence of the qSOFA criteria of sepsis-3 in comparison with the SIRS criteria of sepsis-2, and understanding the underlying dichotomy among these parameters provides significant inferences for sepsis treatment initiatives in the ICU and informing hospital resource allocation. These data-driven results further offer design implications for multiparameter intelligent sepsis prediction in the ICU.

The 2019 n2c2/OHNLP Track on Clinical Semantic Textual Similarity: Overview.

BackgroundSemantic textual similarity is a common task in the general English domain to assess the degree to which the underlying semantics of 2 text segments are equivalent to each other. Clinical Semantic Textual Similarity (ClinicalSTS) is the semantic textual similarity task in the clinical domain that attempts to measure the degree of semantic equivalence between 2 snippets of clinical text. Due to the frequent use of templates in the Electronic Health Record system, a large amount of redundant text exists in clinical notes, making ClinicalSTS crucial for the secondary use of clinical text in downstream clinical natural language processing applications, such as clinical text summarization, clinical semantics extraction, and clinical information retrieval.ObjectiveOur objective was to release ClinicalSTS data sets and to motivate natural language processing and biomedical informatics communities to tackle semantic text similarity tasks in the clinical domain.MethodsWe organized the first BioCreative/OHNLP ClinicalSTS shared task in 2018 by making available a real-world ClinicalSTS data set. We continued the shared task in 2019 in collaboration with National NLP Clinical Challenges (n2c2) and the Open Health Natural Language Processing (OHNLP) consortium and organized the 2019 n2c2/OHNLP ClinicalSTS track. We released a larger ClinicalSTS data set comprising 1642 clinical sentence pairs, including 1068 pairs from the 2018 shared task and 1006 new pairs from 2 electronic health record systems, GE and Epic. We released 80% (1642/2054) of the data to participating teams to develop and fine-tune the semantic textual similarity systems and used the remaining 20% (412/2054) as blind testing to evaluate their systems. The workshop was held in conjunction with the American Medical Informatics Association 2019 Annual Symposium.ResultsOf the 78 international teams that signed on to the n2c2/OHNLP ClinicalSTS shared task, 33 produced a total of 87 valid system submissions. The top 3 systems were generated by IBM Research, the National Center for Biotechnology Information, and the University of Florida, with Pearson correlations of r=.9010, r=.8967, and r=.8864, respectively. Most top-performing systems used state-of-the-art neural language models, such as BERT and XLNet, and state-of-the-art training schemas in deep learning, such as pretraining and fine-tuning schema, and multitask learning. Overall, the participating systems performed better on the Epic sentence pairs than on the GE sentence pairs, despite a much larger portion of the training data being GE sentence pairs.ConclusionsThe 2019 n2c2/OHNLP ClinicalSTS shared task focused on computing semantic similarity for clinical text sentences generated from clinical notes in the real world. It attracted a large number of international teams. The ClinicalSTS shared task could continue to serve as a venue for researchers in natural language processing and medical informatics communities to develop and improve semantic textual similarity techniques for clinical text.

MacEmerg Podcast: A Novel Initiative to Connect a Distributed Community of Practice.

Regional knowledge dissemination and information sharing is a challenge among physically divided groups of physicians. Many staff and resident physicians do not have easy access to share clinical and medical education and research information with each other in an academic setting. Our divisions of emergency medicine could benefit from a novel approach aimed at improving overall connection and collaborative engagement. By harnessing the sociomateriality properties of podcasting, we could achieve the dual goals of better connecting our faculty as well as educating the audience on aspects of clinical practice and education that are especially relevant to our region. We sought to primarily draw on local expertise for content. We developed a standardized structure for our monthly releases, with each episode composed of a main faculty segment, a resident-focused segment, and a medical education segment. Accessibility to the podcast was maximized through its publication across multiple platforms and detailed individual show notes were made available. We applied logic model methodology with the intended goal of having much of our content consumed by local faculty and trainees. Using Web-based analytic data, we were able to ascertain the proportion and number of listens that occurred from within our local university-affiliated and/or catchment region. Episodes averaged 227.7 ± 67.2 listens with an overall 44.1% of those originating from within our defined region. Given the number of regional listeners we are consistently reaching, we have been effective in serving to connect a widely distributed group of academic physicians. As we continue to grow the podcast, we plan on collecting quantitative data to better ascertain its effect on our stated goals.

Investigation and validation of magnetic resonance white matter atlas for 0 to 2 years old infants

Objective To construct and verify a standard template of white matter based on Chinese normal 0 to 2 years old infants by using nonlinear high registration accuracy of non-rigid diffeomorphism paradigm (large deformation diffeomorphic metric mapping, LDDMM). Methods Full-term spontaneous labor children without maternal pregnancy disease (hypertension, diabetes, etc.), intrauterine hypoxia and ischemia, head trauma, intracranial infection, intracranial surgery history, family history of mental disorders were selected. Diffusion tensor imaging (DTI) data from the 120 normal Chinese infants under 2 years old were acquired after excluding the existence of neurological diseases revealed by neurologists, radiologists and Gesell Developmental Scale. All the data were divided into six groups including group A: 1 day to 1.5 months, group B: 1.5 to 4.5 months, group C: 4.5 to 9.0 months, group D: 9 to 15 months, group E: 15 to 21 months, and group F: 21 to 24 months. Data pre-processing, normalizing, tensor fitting and calculation of all the images were performed by using MRlcron, DtiStudio, DiffeoMap and SPM software package combined with LDDMM image registration method based on the selected single template of each group. And the average templates of each group were constructed by MATLAB software platform. The set of templates included fractional anisotropy figure (FA), color map, T1 weighted image, b0 image and the mean of all DWfs figures. Results The templates of FA, T1, b0, DWfs and color map for the normal brain magnetic resonance white matter development of the Chinese infants aged 0 to 2 years were successfully established with the subjective scores exceeding 2 points. The objective evaluation root mean squared error was controlled below 0.19, and the cubic chart of brain alternation trend for the children aged 0 to 2 years was consistent with previous literature. Conclusion Constructing a standard template of white matter based on Chinese normal infants, by using nonlinear high registration accuracy of non-rigid diffeomorphism paradigm provides not only a foundation of further research on brain development, mechanism and treatment of pediatric diseases associated with brain, but also objective and fair imaging information for medical education and research.

COVID-19 & an NGO and university developed interactive portal: a perspective from Iran.

On 19 February 2020, Iran reported the initial cases of novel coronavirus (2019-nCoV). As of 21 March 2020, Iran had reported 175,927 COVID-19 cases, including 8425 deaths. One of the best approaches for responding to COVID-19 is rapid detection, early isolation, and quick treatment of the disease. Studies have stated that information technology (IT) is a powerful tool for detecting, tracking, and responding to pandemic diseases. Despite the importance of IT, a lack of efficient use of information technology capacity was observed after the emergence of the new cases of COVID-19 in Iran. A web-portal can integrate different services and technologies and can support interaction between non-governmental organizations (NGO) and universities. NGOs can provide services for public health utilizing technology and its advancements. One of the important duties of these organizations is to inform and provide integrated services to the general public. An interactive portal is one of the advanced technologies that these organizations can use for health management. Medical sciences of universities play a vital surveillance role for enhancing the performance quality of NGOs. A web-portal can be a collaboration tool between health-related NGOs and medical sciences of universities. In this study, an interactive portal was developed by NGOs and a university. NGOs under the supervision and participation of Tabriz University of Medical Sciences’ Center for Social Factors Research in COVID-19 management division of this portal separated classified information into two sections, informatics and services. This portal is accessible to the general public, patients, service providers, and, importantly, policymakers and presents educational and medical research information to all users. For patients and the general public in high-risk environments, increasing information security, reducing confusion regarding finding needed information, and facilitating communication are only part of the portal’s benefit. It seems that web-portal capacity is needed to control COVID-19 in the digital age. The collaboration of academic and university bodies in the context of health portals can play key roles for coverage of the COVID-19 pandemic.

The Current State of Research, Challenges, and Future Research Directions of Blockchain Technology in Patient Care: Systematic Review.

BackgroundBlockchain offers a promising new distributed technology to address the challenges of data standardization, system interoperability, security, privacy, and accessibility of medical records.ObjectiveThe purpose of this review is to assess the research on the use of blockchain technology for patient care and the associated challenges and to provide a research agenda for future research.MethodsThis review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. We queried the Cumulative Index of Nursing and Allied Health Literature (CINAHL), PubMed, Excerpta Medica dataBASE (EMBASE), and Web of Science databases for peer-reviewed research articles published up to December 2019 that examined the implementation of blockchain technology in health care settings. We identified 800 articles from which we selected 70 empirical research articles for a detailed review.ResultsBlockchain-based patient care applications include medical information systems, personal health records, mobile health and telemedicine, data preservation systems and social networks, health information exchanges and remote monitoring systems, and medical research systems. These blockchain-based health care applications may improve patient engagement and empowerment, improve health care provider access to information, and enhance the use of health care information for medical research.ConclusionsBlockchain health information technology (HIT) provides benefits such as ensuring data privacy and security of health data, facilitating interoperability of heterogeneous HIT systems, and improving the quality of health care outcomes. However, barriers to using blockchain technology to build HIT include security and privacy vulnerabilities, user resistance, high computing power requirements and implementation costs, inefficient consensus algorithms, and challenges of integrating blockchain with existing HIT. With 51% of the research focused on medical information systems such as electronic health record and electronic medical record, and 53% of the research focused on data security and privacy issues, this review shows that HIT research is primarily focused on the use of blockchain technologies to address the current challenges HIT faces. Although Blockchain presents significant potential for disrupting health care, most ideas are in their infancy.

The handling and using of medical research data

The results of scientific research can be trusted when initial data is processed properly. To watch out quality of data is the most important in those research studies that provide their results to Russian health care officials for management decision-making that effect medical care support on national scale. The financial support of science within the framework of public contracts turns out less efficient when deductions are based on unreliably collected, altered or omitted information. When electronic systems are applied in data collection, they are to meet definite requirements. There are several international documents defining requirements to electronic systems collecting and processing information in medical scientific research. In the Russian Federation, such requirements are absent. The solution of this problem is in gradual implementation of common international standards into practice of collecting research data initially through researchers' training and then through Russian regulatory documentation amendment.