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| 1 | Active Vaccine Safety Surveillance: Global Trends and Challenges in China显示文摘Importance.The great success in vaccine-preventable diseases has been accompanied by vaccine safety concerns.This has caused vaccine hesitancy to be the top 10 in threats to global health.The comprehensive understanding of adverse events following immunization should be entirely based on clinical trials and postapproval surveillance.It has increasingly been recognized worldwide that the active surveillance of vaccine safety should be an essential part of immunization programs due to its complementary advantages to passive surveillance and clinical trials.Highlights.In the present study,the framework of vaccine safety surveillance was summarized to illustrate the importance of active surveillance and address vaccine hesitancy or safety concerns.Then,the global progress of active surveillance systems was reviewed,mainly focusing on population-based or hospital-based active surveillance.With these successful paradigms,the practical and reliable ways to create robust and similar systems in China were discussed and presented from the perspective of available databases,methodology challenges,policy supports,and ethical considerations.Conclusion.In the inevitable trend of the global vaccine safety ecosystem,the establishment of an active surveillance system for vaccine safety in China is urgent and feasible.This process can be accelerated with the consensus and cooperation of regulatory departments,research institutions,and data owners. | Zhike Liu Ruogu Meng Yu Yang Keli Li Zundong Yin Jingtian Ren Chuanyong Shen Zijian Feng Siyan Zhan | 2021 | Health Data Science2021，，1： | 2 |
| 2 | Analysis of COVID-19 Guideline Quality and Change of Recommendations:A Systematic Review显示文摘Background.Hundreds of coronavirus disease 2019(COVID-19)clinical practice guidelines(CPGs)and expert consensus statements have been developed and published since the outbreak of the epidemic.However,these CPGs are of widely variable quality.So,this review is aimed at systematically evaluating the methodological and reporting qualities of COVID-19 CPGs,exploring factors that may influence their quality,and analyzing the change of recommendations in CPGs with evidence published.Methods.We searched five electronic databases and five websites from 1 January to 31 December 2020 to retrieve all COVID-19 CPGs.The assessment of the methodological and reporting qualities of CPGs was performed using the AGREE II instrument and RIGHT checklist.Recommendations and evidence used to make recommendations in the CPGs regarding some treatments for COVID-19(remdesivir,glucocorticoids,hydroxychloroquine/chloroquine,interferon,and lopinavir-ritonavir)were also systematically assessed.And the statistical inference was performed to identify factors associated with the quality of CPGs.Results.We included a total of 92 COVID-19 CPGs developed by 19 countries.Overall,the RIGHT checklist reporting rate of COVID-19 CPGs was 33.0%,and the AGREE II domain score was 30.4%.The overall methodological and reporting qualities of COVID-19 CPGs gradually improved during the year 2020.Factors associated with high methodological and reporting qualities included the evidence-based development process,management of conflicts of interest,and use of established rating systems to assess the quality of evidence and strength of recommendations.The recommendations of only seven(7.6%)CPGs were informed by a systematic review of evidence,and these seven CPGs have relatively high methodological and reporting qualities,in which six of them fully meet the Institute of Medicine(IOM)criteria of guidelines.Besides,a rapid advice CPG developed by the World Health Organization(WHO)of the seven CPGs got the highest overall scores in methodological(72.8%)and reporting qualities(83.8%).Many CPGs covered the same clinical questions(it refers to the clinical questions on the effectiveness of treatments of remdesivir,glucocorticoids,hydroxychloroquine/chloroquine,interferon,and lopinavirritonavir in COVID-19 patients)and were published by different countries or organizations.Although randomized controlled trials and systematic reviews on the effectiveness of treatments of remdesivir,glucocorticoids,hydroxychloroquine/chloroquine,interferon,and lopinavir-ritonavir for patients with COVID-19 have been published,the recommendations on those treatments still varied greatly across COVID-19 CPGs published in different countries or regions,which may suggest that the CPGs do not make sufficient use of the latest evidence.Conclusions.Both the methodological and reporting qualities of COVID-19 CPGs increased over time,but there is still room for further improvement.The lack of effective use of available evidence and management of conflicts of interest were the main reasons for the low quality of the CPGs.The use of formal rating systems for the quality of evidence and strength of recommendations may help to improve the quality of CPGs in the context of the COVID-19 pandemic.During the pandemic,we suggest developing a living guideline of which recommendations are supported by a systematic review for it can facilitate the timely translation of the latest research findings to clinical practice.We also suggest that CPG developers should register the guidelines in a registration platform at the beginning for it can reduce duplication development of guidelines on the same clinical question,increase the transparency of the development process,and promote cooperation among guideline developers all over the world.Since the International Practice Guideline Registry Platform has been created,developers could register guidelines prospectively and internationally on this platform. | Siya Zhao Shuya Lu Shouyuan Wu Zijun Wang Qiangqiang Guo Qianling Shi Hairong Zhang Juanjuan Zhang Hui Liu Yunlan Liu Xianzhuo Zhang Ling Wang Mengjuan Ren Ping Wang Hui Lan Qi Zhou Yajia Sun Jin Cao Qinyuan Li Janne Estill Joseph LMathew Hyeong Sik Ahn Myeong Soo Lee Xiaohui Wang Chenyan Zhou Yaolong Chen | 2021 | Health Data Science2021，，1： | 1 |
| 3 | A Framework for Assessing Import Costs of Medical Supplies and Results for a Tuberculosis Program in Karakalpakstan,Uzbekistan显示文摘Background.Import of medical supplies is common,but limited knowledge about import costs and their structure introduces uncertainty to budget planning,cost management,and cost-effectiveness analysis of health programs.We aimed to estimate the import costs of a tuberculosis(TB)program in Uzbekistan,including the import costs of specific imported items.Methods.We developed a framework that applies costing and cost accounting to import costs.First,transport costs,customs-related costs,cargo weight,unit weights,and quantities ordered were gathered for a major shipment of medical supplies from the Médecins Sans Frontières(MSF)Procurement Unit in Amsterdam,the Netherlands,to a TB program in Karakalpakstan,Uzbekistan,in 2016.Second,air freight,land freight,and customs clearance cost totals were estimated.Third,total import costs were allocated to different cargos(standard,cool,and frozen),items(e.g.,TB drugs),and units(e.g.,one tablet)based on imported weight and quantity.Data sources were order invoices,waybills,the local MSF logistics department,and an MSF standard product list.Results.The shipment contained 1.8 million units of 85 medical items of standard,cool,and frozen cargo.The average import cost for the TB program was 9.0%of the shipment value.Import cost varied substantially between cargos(8.9–28%of the cargo value)and items(interquartile range 4.5–35%of the item value).The largest portion of the total import cost was caused by transport(82–99%of the cargo import cost)and allocated based on imported weight.Ten(14%)of the 69 items imported as standard cargo were associated with 85%of the standard cargo import cost.Standard cargo items could be grouped based on contributing to import costs predominantly through unit weight(e.g.,fluids),imported quantity(e.g.,tablets),or the combination of unit weight and imported quantity(e.g.,items in powder form).Conclusion.The cost of importing medical supplies to a TB program in Karakalpakstan,Uzbekistan,was sizable,variable,and driven by a subset of imported items.The framework used to measure and account import costs can be adapted to other health programs. | Stefan Kohler Norman Sitali Nicolas Paul | 2021 | Health Data Science2021，，1： | 1 |
| 4 | Cognitive Computing-Based CDSS in Medical Practice显示文摘Importance.The last decade has witnessed the advances of cognitive computing technologies that learn at scale and reason with purpose in medicine studies.From the diagnosis of diseases till the generation of treatment plans,cognitive computing encompasses both data-driven and knowledge-driven machine intelligence to assist health care roles in clinical decision-making.This review provides a comprehensive perspective from both research and industrial efforts on cognitive computing-based CDSS over the last decade.Highlights.(1)A holistic review of both research papers and industrial practice about cognitive computing-based CDSS is conducted to identify the necessity and the characteristics as well as the general framework of constructing the system.(2)Several of the typical applications of cognitive computing-based CDSS as well as the existing systems in real medical practice are introduced in detail under the general framework.(3)The limitations of the current cognitive computing-based CDSS is discussed that sheds light on the future work in this direction.Conclusion.Different from medical content providers,cognitive computing-based CDSS provides probabilistic clinical decision support by automatically learning and inferencing from medical big data.The characteristics of managing multimodal data and computerizing medical knowledge distinguish cognitive computing-based CDSS from other categories.Given the current status of primary health care like high diagnostic error rate and shortage of medical resources,it is time to introduce cognitive computing-based CDSS to the medical community which is supposed to be more open-minded and embrace the convenience and low cost but high efficiency brought by cognitive computing-based CDSS. | Jun Chen Chao Lu Haifeng Huang Dongwei Zhu Qing Yang Junwei Liu Yan Huang Aijun Deng Xiaoxu Han | 2021 | Health Data Science2021，，1： | 1 |
| 5 | Characterizing Discourse about COVID-19 Vaccines:A Reddit Version of the Pandemic Story显示文摘It has been one year since the outbreak of the COVID-19 pandemic.The good news is that vaccines developed by several manufacturers are being actively distributed worldwide.However,as more and more vaccines become available to the public,various concerns related to vaccines become the primary barriers that may hinder the public from getting vaccinated.Considering the complexities of these concerns and their potential hazards,this study is aimed at offering a clear understanding about different population groups’underlying concerns when they talk about COVID-19 vaccines—particularly those active on Reddit.The goal is achieved by applying LDA and LIWC to characterize the pertaining discourse with insights generated through a combination of quantitative and qualitative comparisons.Findings include the following:(1)during the pandemic,the proportion of Reddit comments predominated by conspiracy theories outweighed that of any other topics;(2)each subreddit has its own user bases,so information posted in one subreddit may not reach that from other subreddits;and(3)since users’concerns vary across time and subreddits,communication strategies must be adjusted according to specific needs.The results of this study manifest challenges as well as opportunities in the process of designing effective communication and immunization programs. | Wei Wu Hanjia Lyu Jiebo Luo | 2021 | Health Data Science2021，，1： | 1 |
| 6 | Space-Time-Stratified Case-Crossover Design in Environmental Epidemiology Study显示文摘We are living in a changing environment that affects human health.It is vital to use proper methods to quantify the impact of environmental exposure(e.g.,air pollutants and extreme temperatures)on human health.Case-crossover design with daily environmental exposure and health outcomes(e.g.,deaths and hospitalisations)is one of the most common study designs.It allows researchers to examine the acute health effects due to short-term environmental exposure.A case-crossover design utilizes the ID as a stratum,comparing individuals to themselves at different times.To examine whether the events are associated with a particular exposure,it compares exposure level in the day when the health event occurs(case day)with the levels in nearby days(control days).The control days represent the counterfactual exposure experience of each case,independently of the exposure on case day. | Yao Wu Shanshan Li Yuming Guo | 2021 | Health Data Science2021，，1： | 1 |
| 7 | Angiotensin-Converting Enzyme(ACE)Inhibitors May Moderate COVID-19 Hyperinflammatory Response:An Observational Study with Deep Immunophenotyping显示文摘Background:Angiotensin-converting enzyme inhibitors(ACEi)and angiotensin-II receptor blockers(ARB),the most commonly prescribed antihypertensive medications,counter renin-angiotensin-aldosterone system(RAAS)activation via induction of angiotensin-converting enzyme 2(ACE2)expression.Considering that ACE2 is the functional receptor for SARS-CoV-2 entry into host cells,the association of ACEi and ARB with COVID-19 outcomes needs thorough evaluation.Methods:We conducted retrospective analyses using both unmatched and propensity score(PS)-matched cohorts on electronic health records(EHRs)to assess the impact of RAAS inhibitors on the risk of receiving invasive mechanical ventilation(IMV)and 30-day mortality among hospitalized COVID-19 patients.Additionally,we investigated the immune cell gene expression profiles of hospitalized COVID-19 patients with prior use of antihypertensive treatments from an observational prospective cohort.Results:The retrospective analysis revealed that there was no increased risk associated with either ACEi or ARB use.In fact,the use of ACEi showed decreased risk for mortality.Survival analyses using PS-matched cohorts suggested no significant relationship between RAAS inhibitors with a hospital stay and in-hospital mortality compared to non-RAAS medications and patients not on antihypertensive medications.From the analysis of gene expression profiles,we observed a noticeable up-regulation in the expression of 1L1R2(an anti-inflammatory receptor)and RETN(an immunosuppressive marker)genes in monocytes among prior users of ACE inhibitors.Conclusion:Overall,the findings do not support the discontinuation of ACEi or ARB treatment and suggest that ACEi may moderate the COVID-19 hyperinflammatory response. | Venkata R.Duvvuri Andrew Baumgartner Sevda Molani Patricia V.Hernandez Dan Yuan Ryan T.Roper Wanessa F.Matos Max Robinson Yapeng Su Naeha Subramanian Jason D.Goldman James R.Heath Jennifer J.Hadlock | 2022 | Health Data Science2022，，1： | 1 |
| 8 | The COVID-19 Pandemic and Mental Health Concerns on Twitter in the United States显示文摘Background.During the COVID-19 pandemic,mental health concerns(such as fear and loneliness)have been actively discussed on social media.We aim to examine mental health discussions on Twitter during the COVID-19 pandemic in the US and infer the demographic composition of Twitter users who had mental health concerns.Methods.COVID-19-related tweets from March 5th,2020,to January 31st,2021,were collected through Twitter streaming API using keywords(i.e.,“corona,”“covid19,”and“covid”).By further filtering using keywords(i.e.,“depress,”“failure,”and“hopeless”),we extracted mental health-related tweets from the US.Topic modeling using the Latent Dirichlet Allocation model was conducted to monitor users’discussions surrounding mental health concerns.Deep learning algorithms were performed to infer the demographic composition of Twitter users who had mental health concerns during the pandemic.Results.We observed a positive correlation between mental health concerns on Twitter and the COVID-19 pandemic in the US.Topic modeling showed that“stay-at-home,”“death poll,”and“politics and policy”were the most popular topics in COVID-19 mental health tweets.Among Twitter users who had mental health concerns during the pandemic,Males,White,and 30-49 age group people were more likely to express mental health concerns.In addition,Twitter users from the east and west coast had more mental health concerns.Conclusions.The COVID-19 pandemic has a significant impact on mental health concerns on Twitter in the US.Certain groups of people(such as Males and White)were more likely to have mental health concerns during the COVID-19 pandemic. | Senqi Zhang Li Sun Daiwei Zhang Pin Li Yue Liu Ajay Anand Zidian Xie Dongmei Li | 2022 | Health Data Science2022，，1： | 1 |
| 9 | Health Data Sharing Platforms:Serving Researchers through Provision of Access to High-Quality Data for Reuse显示文摘1.Introduction The objective of health-related data sharing platforms or repositories is to facilitate access to datasets to either researchers or the public.Data sharing platforms provide a service to researchers worldwide through providing access to high-quality data.Oftentimes,platforms aggregate disparate data sources that are siloed.In the current spectrum of data sharing platforms,there are a variety of implementation models—open,managed access,and closed.The selected model depends on the data type and governance required.“Open access”or“open data”approaches allow the data to be freely available via download if the user agrees to sign a simple legal data use agreement or terms of use agreement.“Managed access”or“gatekeeper”models require additional elements for data access—these may involve a research proposal,agreeing to data use agreements,and undergoing a review process.Closed or private repositories typically only allow individuals of a single institution or community to access data. | Rebecca Li Nina Hill Catherine D’Arcy Amrutha Baskaran Patricia Bradford | 2022 | Health Data Science2022，，1： | 0 |
| 10 | Artificial Intelligence in Skin Diseases:Fulfilling its Potentials to Meet the Real Needs in Dermatology Practice显示文摘Artificial intelligence(AI)medical image analysis techniques based on deep learning and machine learning have developed rapidly in recent years.Since the diagnosis of skin diseases is mainly based on the morphology of lesions,dermatology is considered a promising area for AI image analysis techniques.In 2017,scientists from Stanford University published a milestone paper in Nature to show the performance of AI was comparable to dermatologists in the classification and recognition of skin cancer,using convolutional neural network(CNN)models trained on nearly 130,000 clinical images[1].Since then,many countries have been actively developing similar products.So far,the U.S.Food and Drug Administration has approved 3Drem,Google DeepMind,and SkinVision.In China,Youzhi Pifu,Voxel-Cloud DermX,and Meitueve have entered the public view.The public’s interest in AI roars,and the imagination of AI replacing dermatologists seems to be a reality in the foreseen future. | Yicen Yan Shenda Hong Wensheng Zhang Hang Li | 2022 | Health Data Science2022，，1： | 0 |
| 11 | Research Trends and Emerging Hotspots of Lung Cancer Surgery during 2012-2021:A 10-Year Bibliometric and Network Analysis显示文摘Background. Lung cancer remains the leading cause of death because of cancer globally in the past years. To inspire researcherswith new targets and path-breaking directions for lung cancer research, this study is aimed at exploring the research trends andemerging hotspots in the lung cancer surgery literature in the recent decade. Methods. This cross-sectional study combinedbibliometric and network analysis techniques to undertake a quantitative analysis of lung cancer surgery literature. Dimensionsdatabase was searched using keywords in a 10-year period (2012-2021). Publications were characterized by publication year,research countries, field citation ratio, cooperation status, research area, and emerging hotspots. Results. Overall, globalscholarly outputs of lung cancer surgery had almost doubled during the recent decade, with China, Japan, and the UnitedStates leading the way, while Denmark and Belgium predominated in terms of scientific influence. Network analysis showedthat international cooperation accounted for a relatively small portion in lung cancer surgery research, and the United States,China, and Europe were the prominent centers of international cooperation network. In the recent decade, research of lungcancer surgery majored in prevention, biomedical imaging, rehabilitation, and genetics, and the emerging research hotspotstransformed into immunotherapy. Research on immunotherapy showed a considerable increase in scientific influence in thelatest year. Conclusions. The study findings are expected to provide researchers and policymakers with interesting insights intothe changing trends of lung cancer surgery research and further generate evidence to support decision-making in improvingprognosis for patients with lung cancer. | Jingyi Wu Chenlu Bao Ganwei Liu Shushi Meng Yunwei Lu Pengfei Li Jian Zhou | 2022 | Health Data Science2022，，1： | 0 |
| 12 | Social Determinants,Data Science,and Decision Making:The 3-D Approach to Achieving Health Equity in Asia显示文摘Improving population health by creating more equitable health systems is a major focus of health policy and planning today.However,before we can achieve equity in health,we must first begin by leveraging all we have learned,and are continuing to discover,about the many social,structural,and environmental determinants of health.We must fully consider the conditions in which people are born,grow,learn,work,play,and age.The study of social determinants of health has made tremendous strides in recent decades.At the same time,we have seen huge advances in how health data are collected,analyzed,and used to inform action in the health sector.It is time to merge these two fields,to harness the best from both and to improve decision-making to accelerate evidence-based action toward greater health equity. | Luxia Zhang Sabina Faiz Rashid Gabriel Leung | 2022 | Health Data Science2022，，1： | 0 |
| 13 | Next Decade’s AI-Based Drug Development Features Tight Integration of Data and Computation显示文摘Traditional drug development heavily relies on humanderived rational and effort to detect the functional mechanisms of diseases,identify druggable targets,and design lead compounds to hit the targets.Despite our progress in understanding human diseases and the advances in biotechnology,the search for novel therapeutics remains a timeconsuming and costly process.With the recent tremendous success of artificial intelligence(AI)in various domains,AIbased drug development is poised to become a revolutionary force in the pharmaceutical sector and is expected to fundamentally change the traditional trial-and-error design process(Figure 1(a)). | Yunan Luo Jian Peng Jianzhu Ma | 2022 | Health Data Science2022，，1： | 0 |
| 14 | Communicating about Data to Achieve Change显示文摘Even with plans for collecting,managing,and analyzing high-quality data,drawing meaningful conclusions,and writing an exceptional peer-reviewed journal article,there is no guarantee that the findings will be translated into change in the organization,delivery,or financing of health care that will result in better health outcomes for a target population or society at large.The process of turning wellcollected data with high-quality analyses and well-drawn conclusions into changes in health care requires clear and careful communication.The importance of timely and clear communication continues to grow as datasets become more complex,research teams become more interdisciplinary,and the speed demanded by business and public health decision-making seems to grow faster by the day.Researchers need to know how to communicate with other researchers with expertise in different fields and with the general public.Successful communication requires action from the researcher and is intended to inspire action in stakeholders who can help bring about change.With action as both an input and an output in the change process,I have developed and utilized a model with ACTION as the acronym. | Kevin D.Frick | 2022 | Health Data Science2022，，1： | 0 |
| 15 | Impact of COVID-19 Prevention and Control on the Influenza Epidemic in China:A Time Series Study显示文摘Background. COVID-19 prevention and control measures might affect influenza epidemic in China since the nonpharmaceuticalinterventions (NPIs) and behavioral changes contain transmission of both SARS-CoV-2 and influenza virus. We aimed to explorethe impact of COVID-19 prevention and control measures on influenza using data from the National Influenza SurveillanceNetwork. Methods. The percentage of influenza-like illness (ILI%) in southern and northern China from 2010 to 2022 wascollected from the National Influenza Surveillance Network. Weekly ILI% observed value from 2010 to 2019 was used tocalculate estimated annual percentage change (EAPC) of ILI% with 95% confidence intervals (CIs). Time series analysis wasapplied to estimate weekly ILI% predicted values in 2020/2021 and 2021/2022 season. Impact index was used to explore theimpact of COVID-19 prevention and control on influenza during nonpharmaceutical intervention and vaccination stages.Results. China influenza activity was affected by the COVID-19 pandemic and different prevention and control measuresduring 2020-2022. In 2020/2021 season, weekly ILI% observed value in both southern and northern China was at a lowepidemic level, and there was no obvious epidemic peak in winter and spring. In 2021/2022 season, weekly ILI% observedvalue in southern and northern China showed a small peak in summer and epidemic peak in winter and spring. The weeklyILI% observed value was generally lower than the predicted value in southern and northern China during 2020-2022. Themedian of impact index of weekly ILI% was 15.11% in north and 22.37% in south in 2020/2021 season and decreasedsignificantly to 2.20% in north and 3.89% in south in 2021/2022 season. Conclusion. In summary, there was a significantdecrease in reported ILI in China during the 2020-2022 COVID-19 pandemic, particularly in winter and spring. Reduction ofinfluenza virus infection might relate to everyday Chinese public health COVID-19 interventions. The confirmation of thisrelationship depends on future studies. | Zirui Guo Li Zhang Jue Liu Min Liu | 2022 | Health Data Science2022，，1： | 0 |
| 16 | Mobile Sensing in the COVID-19 Era:A Review显示文摘Background. During the COVID-19 pandemic, mobile sensing and data analytics techniques have demonstrated their capabilitiesin monitoring the trajectories of the pandemic, by collecting behavioral, physiological, and mobility data on individual,neighborhood, city, and national scales. Notably, mobile sensing has become a promising way to detect individuals’ infectiousstatus, track the change in long-term health, trace the epidemics in communities, and monitor the evolution of viruses andsubspecies. Methods. We followed the PRISMA practice and reviewed 60 eligible papers on mobile sensing for monitoringCOVID-19. We proposed a taxonomy system to summarize literature by the time duration and population scale under mobilesensing studies. Results. We found that existing literature can be naturally grouped in four clusters, including remote detection,long-term tracking, contact tracing, and epidemiological study. We summarized each group and analyzed representative workswith regard to the system design, health outcomes, and limitations on techniques and societal factors. We further discussed theimplications and future directions of mobile sensing in communicable diseases from the perspectives of technology andapplications. Conclusion. Mobile sensing techniques are effective, efficient, and flexible to surveil COVID-19 in scales of timeand populations. In the post-COVID era, technical and societal issues in mobile sensing are expected to be addressed toimprove healthcare and social outcomes. | Zhiyuan Wang Haoyi Xiong Mingyue Tang Mehdi Boukhechba Tabor E.Flickinger Laura E.Barnes | 2022 | Health Data Science2022，，1： | 0 |
| 17 | Cost-Utility Analysis of Screening for Diabetic Retinopathy in China显示文摘Background.Diabetic retinopathy(DR)has been primarily indicated to cause vision impairment and blindness,while no studies have focused on the cost-utility of telemedicine-based and community screening programs for DR in China,especially in rural and urban areas,respectively.Methods.We developed a Markov model to calculate the cost-utility of screening programs for DR in DM patients in rural and urban settings from the societal perspective.The incremental cost-utility ratio(ICUR)was calculated for the assessment.Results.In the rural setting,the community screening program obtained 1 QALY with a cost of$4179(95%CI 3859 to 5343),and the telemedicine screening program had an ICUR of$2323(95%CI 1023 to 3903)compared with no screening,both of which satisfied the criterion of a significantly cost-effective health intervention.Likewise,community screening programs in urban areas generated an ICUR of$3812(95%CI 2906 to 4167)per QALY gained,with telemedicine screening at an ICUR of$2437(95%CI 1242 to 3520)compared with no screening,and both were also cost-effective.By further comparison,compared to community screening programs,telemedicine screening yielded an ICUR of 1212(95%CI 896 to 1590)per incremental QALY gained in rural setting and 1141(95%CI 859 to 1403)in urban setting,which both meet the criterion for a significantly cost-effective health intervention.Conclusions.Both telemedicine and community screening for DR in rural and urban settings were cost-effective in China,and telemedicine screening programs were more cost-effective. | Yue Zhang Weiling Bai Ruyue Li Yifan Du Runzhou Sun Tao Li Hong Kang Ziwei Yang Jianjun Tang Ningli Wang Hanruo Liu | 2022 | Health Data Science2022，，1： | 0 |
| 18 | Clinician Data Scientists—Preparing for the Future of Medicine in the Digital World显示文摘1.Introduction Clinician-scientists have a unique strength in translational research and medical advances that improve the quality of care and patient outcomes.As big data analytics and advanced technologies such as artificial intelligence are being continuously applied in the healthcare scenario,it not only transforms patient care but also creates tremendous opportunities for data-driven discoveries.In a digital health era,clinicianscientists proficient in data science knowledge——that is clinician data scientists——are central to harnessing the power of big data analytics and advanced technologies in medicine. | Fulin Wang Lin Ma Georgina Moulton Mai Wang Luxia Zhang | 2022 | Health Data Science2022，，1： | 0 |
| 19 | A Review of Three-Dimensional Medical Image Visualization显示文摘Importance. Medical images are essential for modern medicine and an important research subject in visualization. However,medical experts are often not aware of the many advanced three-dimensional (3D) medical image visualization techniques thatcould increase their capabilities in data analysis and assist the decision-making process for specific medical problems. Ourpaper provides a review of 3D visualization techniques for medical images, intending to bridge the gap between medicalexperts and visualization researchers. Highlights. Fundamental visualization techniques are revisited for various medicalimaging modalities, from computational tomography to diffusion tensor imaging, featuring techniques that enhance spatialperception, which is critical for medical practices. The state-of-the-art of medical visualization is reviewed based on aprocedure-oriented classification of medical problems for studies of individuals and populations. This paper summarizes freesoftware tools for different modalities of medical images designed for various purposes, including visualization, analysis, andsegmentation, and it provides respective Internet links. Conclusions. Visualization techniques are a useful tool for medicalexperts to tackle specific medical problems in their daily work. Our review provides a quick reference to such techniques giventhe medical problem and modalities of associated medical images. We summarize fundamental techniques and readily availablevisualization tools to help medical experts to better understand and utilize medical imaging data. This paper could contributeto the joint effort of the medical and visualization communities to advance precision medicine. | Liang Zhou Mengjie Fan Charles Hansen Chris R.Johnson Daniel Weiskopf | 2022 | Health Data Science2022，，1： | 0 |
| 20 | Knowledge Graph Applications in Medical Imaging Analysis:A Scoping Review显示文摘Background. There is an increasing trend to represent domain knowledge in structured graphs, which provide efficient knowledgerepresentations for many downstream tasks. Knowledge graphs are widely used to model prior knowledge in the form of nodesand edges to represent semantically connected knowledge entities, which several works have adopted into different medicalimaging applications. Methods. We systematically searched over five databases to find relevant articles that applied knowledgegraphs to medical imaging analysis. After screening, evaluating, and reviewing the selected articles, we performed a systematicanalysis. Results. We looked at four applications in medical imaging analysis, including disease classification, diseaselocalization and segmentation, report generation, and image retrieval. We also identified limitations of current work, such asthe limited amount of available annotated data and weak generalizability to other tasks. We further identified the potentialfuture directions according to the identified limitations, including employing semisupervised frameworks to alleviate the needfor annotated data and exploring task-agnostic models to provide better generalizability. Conclusions. We hope that our articlewill provide the readers with aggregated documentation of the state-of-the-art knowledge graph applications for medicalimaging to encourage future research. | Song Wang Mingquan Lin Tirthankar Ghosal Ying Ding Yifan Peng | 2022 | Health Data Science2022，，1： | 0 |
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