Health data science最新文献

{"title":"The COVID-19 Pandemic and Mental Health Concerns on Twitter in the United States","authors":"Senqi Zhang, Li Sun, Daiwei Zhang, Pin Li, Yue Liu, A. Anand, Zidian Xie, Dongmei Li","doi":"10.1101/2021.08.23.21262489","DOIUrl":"https://doi.org/10.1101/2021.08.23.21262489","url":null,"abstract":"Background: Mental health illness is a growing problem in recent years. During the COVID-19 pandemic, mental health concerns (such as fear and loneliness) have been actively discussed on social media. Objective: In this study, we aim to examine mental health discussions on Twitter during the COVID-19 pandemic in the United States 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 COVID-19 related keywords (e.g., \"corona\", \"covid19\", \"covid\"). By further filtering using mental health keywords (e.g., \"depress\", \"failure\", \"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. Demographic inference using deep learning algorithms (including Face++ and Ethnicolr) was performed to infer the demographic composition of Twitter users who had mental health concerns during the COVID-19 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, White) were more likely to have mental health concerns during the COVID-19 pandemic.","PeriodicalId":73207,"journal":{"name":"Health data science","volume":"2022 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47536357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Characterizing Discourse about COVID-19 Vaccines: A Reddit Version of the Pandemic Story.","authors":"Wei Wu, Hanjia Lyu, Jiebo Luo","doi":"10.34133/2021/9837856","DOIUrl":"10.34133/2021/9837856","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":73207,"journal":{"name":"Health data science","volume":"2021 ","pages":"9837856"},"PeriodicalIF":0.0,"publicationDate":"2021-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9629685/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40477813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Framework for Assessing Import Costs of Medical Supplies and Results for a Tuberculosis Program in Karakalpakstan, Uzbekistan.","authors":"Stefan Kohler, Norman Sitali, Nicolas Paul","doi":"10.34133/2021/9813732","DOIUrl":"10.34133/2021/9813732","url":null,"abstract":"<p><p><i>Background</i>. 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.<i>Methods</i>. 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.<i>Results</i>. 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).<i>Conclusion</i>. 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.</p>","PeriodicalId":73207,"journal":{"name":"Health data science","volume":"1 1","pages":"9813732"},"PeriodicalIF":0.0,"publicationDate":"2021-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10904066/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69807167","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Social and Behavioral Determinants of Health in the Era of Artificial Intelligence with Electronic Health Records: A Scoping Review.","authors":"Anusha Bompelli, Yanshan Wang, Ruyuan Wan, Esha Singh, Yuqi Zhou, Lin Xu, David Oniani, Bhavani Singh Agnikula Kshatriya, Joyce Joy E Balls-Berry, Rui Zhang","doi":"10.34133/2021/9759016","DOIUrl":"10.34133/2021/9759016","url":null,"abstract":"<p><p><i>Background</i>. There is growing evidence that social and behavioral determinants of health (SBDH) play a substantial effect in a wide range of health outcomes. Electronic health records (EHRs) have been widely employed to conduct observational studies in the age of artificial intelligence (AI). However, there has been limited review into how to make the most of SBDH information from EHRs using AI approaches.<i>Methods</i>. A systematic search was conducted in six databases to find relevant peer-reviewed publications that had recently been published. Relevance was determined by screening and evaluating the articles. Based on selected relevant studies, a methodological analysis of AI algorithms leveraging SBDH information in EHR data was provided.<i>Results</i>. Our synthesis was driven by an analysis of SBDH categories, the relationship between SBDH and healthcare-related statuses, natural language processing (NLP) approaches for extracting SBDH from clinical notes, and predictive models using SBDH for health outcomes.<i>Discussion</i>. The associations between SBDH and health outcomes are complicated and diverse; several pathways may be involved. Using NLP technology to support the extraction of SBDH and other clinical ideas simplifies the identification and extraction of essential concepts from clinical data, efficiently unlocks unstructured data, and aids in the resolution of unstructured data-related issues.<i>Conclusion</i>. Despite known associations between SBDH and diseases, SBDH factors are rarely investigated as interventions to improve patient outcomes. Gaining knowledge about SBDH and how SBDH data can be collected from EHRs using NLP approaches and predictive models improves the chances of influencing health policy change for patient wellness, ultimately promoting health and health equity.</p>","PeriodicalId":73207,"journal":{"name":"Health data science","volume":" ","pages":"9759016"},"PeriodicalIF":0.0,"publicationDate":"2021-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10880156/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46471378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis of COVID-19 Guideline Quality and Change of Recommendations: A Systematic Review.","authors":"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 L Mathew, Hyeong Sik Ahn, Myeong Soo Lee, Xiaohui Wang, Chenyan Zhou, Yaolong Chen","doi":"10.34133/2021/9806173","DOIUrl":"10.34133/2021/9806173","url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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 lopinavir-ritonavir 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.</p><p><strong>Conclusions: </strong>Both the methodological and repor","PeriodicalId":73207,"journal":{"name":"Health data science","volume":"2021 ","pages":"9806173"},"PeriodicalIF":0.0,"publicationDate":"2021-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9629660/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40477812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cognitive Computing-Based CDSS in Medical Practice.","authors":"Jun Chen, Chao Lu, Haifeng Huang, Dongwei Zhu, Qing Yang, Junwei Liu, Yan Huang, Aijun Deng, Xiaoxu Han","doi":"10.34133/2021/9819851","DOIUrl":"10.34133/2021/9819851","url":null,"abstract":"<p><p><i>Importance</i>. 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.<i>Highlights</i>. (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.<i>Conclusion</i>. 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.</p>","PeriodicalId":73207,"journal":{"name":"Health data science","volume":" ","pages":"9819851"},"PeriodicalIF":0.0,"publicationDate":"2021-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10880153/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49223838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Urban-Rural Disparities for COVID-19: Evidence from 10 Countries and Areas in the Western Pacific.","authors":"Minah Park,&nbsp;Jue Tao Lim,&nbsp;Lin Wang,&nbsp;Alex R Cook,&nbsp;Borame L Dickens","doi":"10.34133/2021/9790275","DOIUrl":"https://doi.org/10.34133/2021/9790275","url":null,"abstract":"<p><strong>Background: </strong>Limited evidence on the effectiveness of various types of social distancing measures, from voluntary physical distancing to a community-wide quarantine, exists for the Western Pacific Region (WPR) which has large urban and rural populations.</p><p><strong>Methods: </strong>We estimated the time-varying reproduction number (<i>R</i> _<i>t</i> ) in a Bayesian framework using district-level mobility data provided by Facebook (i) to assess how various social distancing policies have contributed to the reduction in transmissibility of SARS-COV-2 and (ii) to examine within-country variations in behavioural responses, quantified by reductions in mobility, for urban and rural areas.</p><p><strong>Results: </strong>Social distancing measures were largely effective in reducing transmissibility, with <i>R</i> _<i>t</i> estimates decreased to around the threshold of 1. Within-country analysis showed substantial variation in public compliance across regions. Reductions in mobility were significantly lower in rural and remote areas than in urban areas and metropolitan cities (<i>p</i> < 0.001) which had the same scale of social distancing orders in place.</p><p><strong>Conclusions: </strong>Our findings provide empirical evidence that public compliance and consequent intervention effectiveness differ between urban and rural areas in the WPR. Further work is required to ascertain the factors affecting these differing behavioural responses, which can assist in policy-making efforts and increase public compliance in rural areas where populations are older and have poorer access to healthcare.</p>","PeriodicalId":73207,"journal":{"name":"Health data science","volume":"2021 ","pages":"9790275"},"PeriodicalIF":0.0,"publicationDate":"2021-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9629684/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40477808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mobile Phone-Based Population Flow Data for the COVID-19 Outbreak in Mainland China.","authors":"Xin Lu, Jing Tan, Ziqiang Cao, Yiquan Xiong, Shuo Qin, Tong Wang, Chunrong Liu, Shiyao Huang, Wei Zhang, Laurie B Marczak, Simon I Hay, Lehana Thabane, Gordon H Guyatt, Xin Sun","doi":"10.34133/2021/9796431","DOIUrl":"10.34133/2021/9796431","url":null,"abstract":"<p><strong>Background: </strong>Human migration is one of the driving forces for amplifying localized infectious disease outbreaks into widespread epidemics. During the outbreak of COVID-19 in China, the travels of the population from Wuhan have furthered the spread of the virus as the period coincided with the world's largest population movement to celebrate the Chinese New Year.</p><p><strong>Methods: </strong>We have collected and made public an anonymous and aggregated mobility dataset extracted from mobile phones at the national level, describing the outflows of population travel from Wuhan. We evaluated the correlation between population movements and the virus spread by the dates when the number of diagnosed cases was documented.</p><p><strong>Results: </strong>From Jan 1 to Jan 22 of 2020, a total of 20.2 million movements of at-risk population occurred from Wuhan to other regions in China. A large proportion of these movements occurred within Hubei province (84.5%), and a substantial increase of travels was observed even before the beginning of the official Chinese Spring Festival Travel. The outbound flows from Wuhan before the lockdown were found strongly correlated with the number of diagnosed cases in the destination cities (log-transformed).</p><p><strong>Conclusions: </strong>The regions with the highest volume of receiving at-risk populations were identified. The movements of the at-risk population were strongly associated with the virus spread. These results together with province-by-province reports have been provided to governmental authorities to aid policy decisions at both the state and provincial levels. We believe that the effort in making this data available is extremely important for COVID-19 modelling and prediction.</p>","PeriodicalId":73207,"journal":{"name":"Health data science","volume":"2021 ","pages":"9796431"},"PeriodicalIF":0.0,"publicationDate":"2021-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9629681/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40477809","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine Learning Highlights Downtrending of COVID-19 Patients with a Distinct Laboratory Profile.","authors":"He S Yang,&nbsp;Yu Hou,&nbsp;Hao Zhang,&nbsp;Amy Chadburn,&nbsp;Lars F Westblade,&nbsp;Richard Fedeli,&nbsp;Peter A D Steel,&nbsp;Sabrina E Racine-Brzostek,&nbsp;Priya Velu,&nbsp;Jorge L Sepulveda,&nbsp;Michael J Satlin,&nbsp;Melissa M Cushing,&nbsp;Rainu Kaushal,&nbsp;Zhen Zhao,&nbsp;Fei Wang","doi":"10.34133/2021/7574903","DOIUrl":"https://doi.org/10.34133/2021/7574903","url":null,"abstract":"<p><strong>Background: </strong>New York City (NYC) experienced an initial surge and gradual decline in the number of SARS-CoV-2-confirmed cases in 2020. A change in the pattern of laboratory test results in COVID-19 patients over this time has not been reported or correlated with patient outcome.</p><p><strong>Methods: </strong>We performed a retrospective study of routine laboratory and SARS-CoV-2 RT-PCR test results from 5,785 patients evaluated in a NYC hospital emergency department from March to June employing machine learning analysis.</p><p><strong>Results: </strong>A COVID-19 high-risk laboratory test result profile (COVID19-HRP), consisting of 21 routine blood tests, was identified to characterize the SARS-CoV-2 patients. Approximately half of the SARS-CoV-2 positive patients had the distinct COVID19-HRP that separated them from SARS-CoV-2 negative patients. SARS-CoV-2 patients with the COVID19-HRP had higher SARS-CoV-2 viral loads, determined by cycle threshold values from the RT-PCR, and poorer clinical outcome compared to other positive patients without the COVID12-HRP. Furthermore, the percentage of SARS-CoV-2 patients with the COVID19-HRP has significantly decreased from March/April to May/June. Notably, viral load in the SARS-CoV-2 patients declined, and their laboratory profile became less distinguishable from SARS-CoV-2 negative patients in the later phase.</p><p><strong>Conclusions: </strong>Our longitudinal analysis illustrates the temporal change of laboratory test result profile in SARS-CoV-2 patients and the COVID-19 evolvement in a US epicenter. This analysis could become an important tool in COVID-19 population disease severity tracking and prediction. In addition, this analysis may play an important role in prioritizing high-risk patients, assisting in patient triaging and optimizing the usage of resources.</p>","PeriodicalId":73207,"journal":{"name":"Health data science","volume":"2021 ","pages":"7574903"},"PeriodicalIF":0.0,"publicationDate":"2021-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9629663/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40477810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Health Data Science - A New Science Partner Journal Dedicated to Promoting Data for Better Health.","authors":"Qimin Zhan","doi":"10.34133/2021/9843140","DOIUrl":"10.34133/2021/9843140","url":null,"abstract":"","PeriodicalId":73207,"journal":{"name":"Health data science","volume":" ","pages":"9843140"},"PeriodicalIF":0.0,"publicationDate":"2021-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10880157/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48321304","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}