Health data science最新文献

{"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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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}

{"title":"Advances in Deep Learning-Based Medical Image Analysis.","authors":"Xiaoqing Liu, Kunlun Gao, Bo Liu, Chengwei Pan, Kongming Liang, Lifeng Yan, Jiechao Ma, Fujin He, Shu Zhang, Siyuan Pan, Yizhou Yu","doi":"10.34133/2021/8786793","DOIUrl":"10.34133/2021/8786793","url":null,"abstract":"<p><p><i>Importance</i>. With the booming growth of artificial intelligence (AI), especially the recent advancements of deep learning, utilizing advanced deep learning-based methods for medical image analysis has become an active research area both in medical industry and academia. This paper reviewed the recent progress of deep learning research in medical image analysis and clinical applications. It also discussed the existing problems in the field and provided possible solutions and future directions.<i>Highlights</i>. This paper reviewed the advancement of convolutional neural network-based techniques in clinical applications. More specifically, state-of-the-art clinical applications include four major human body systems: the nervous system, the cardiovascular system, the digestive system, and the skeletal system. Overall, according to the best available evidence, deep learning models performed well in medical image analysis, but what cannot be ignored are the algorithms derived from small-scale medical datasets impeding the clinical applicability. Future direction could include federated learning, benchmark dataset collection, and utilizing domain subject knowledge as priors.<i>Conclusion</i>. Recent advanced deep learning technologies have achieved great success in medical image analysis with high accuracy, efficiency, stability, and scalability. Technological advancements that can alleviate the high demands on high-quality large-scale datasets could be one of the future developments in this area.</p>","PeriodicalId":73207,"journal":{"name":"Health data science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10880179/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47962962","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":"Implications of Delayed Reopening in Controlling the COVID-19 Surge in Southern and West-Central USA","authors":"Raj Dandekar, Emma Wang, G. Barbastathis, Chris Rackauckas","doi":"10.1101/2020.12.01.20242172","DOIUrl":"https://doi.org/10.1101/2020.12.01.20242172","url":null,"abstract":"In the wake of the rapid surge in the Covid-19 infected cases seen in Southern and West-Central USA in the period of June-July 2020, there is an urgent need to develop robust, data-driven models to quantify the effect which early reopening had on the infected case count increase. In particular, it is imperative to address the question: How many infected cases could have been prevented, had the worst affected states not reopened early? To address this question, we have developed a novel Covid-19 model by augmenting the classical SIR epidemiological model with a neural network module. The model decomposes the contribution of quarantine strength to the infection timeseries, allowing us to quantify the role of quarantine control and the associated reopening policies in the US states which showed a major surge in infections. We show that the upsurge in the infected cases seen in these states is strongly co-related with a drop in the quarantine/lockdown strength diagnosed by our model. Further, our results demonstrate that in the event of a stricter lockdown without early reopening, the number of active infected cases recorded on 14 July could have been reduced by more than 40% in all states considered, with the actual number of infections reduced being more than 100,000 for the states of Florida and Texas. As we continue our fight against Covid-19, our proposed model can be used as a valuable asset to simulate the effect of several reopening strategies on the infected count evolution; for any region under consideration.","PeriodicalId":73207,"journal":{"name":"Health data science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45768128","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}