@article{liang_emergence_2021,
	title = {Emergence and evolution of big data science in {HIV} research: {Bibliometric} analysis of federally sponsored studies 2000–2019},
	volume = {154},
	issn = {1386-5056},
	shorttitle = {Emergence and evolution of big data science in {HIV} research},
	url = {https://www.sciencedirect.com/science/article/pii/S1386505621001842},
	doi = {10.1016/j.ijmedinf.2021.104558},
	abstract = {Background
The rapid growth of inherently complex and heterogeneous data in HIV/AIDS research underscores the importance of Big Data Science. Recently, there have been increasing uptakes of Big Data techniques in basic, clinical, and public health fields of HIV/AIDS research. However, no studies have systematically elaborated on the evolving applications of Big Data in HIV/AIDS research. We sought to explore the emergence and evolution of Big Data Science in HIV/AIDS-related publications that were funded by the US federal agencies.
Methods
We identified HIV/AIDS and Big Data related publications that were funded by seven federal agencies from 2000 to 2019 by integrating data from National Institutes of Health (NIH) ExPORTER, MEDLINE, and MeSH. Building on bibliometrics and Natural Language Processing (NLP) methods, we constructed co-occurrence networks using bibliographic metadata (e.g., countries, institutes, MeSH terms, and keywords) of the retrieved publications. We then detected clusters among the networks as well as the temporal dynamics of clusters, followed by expert evaluation and clinical implications.
Results
We harnessed nearly 600 thousand publications related to HIV/AIDS, of which 19,528 publications relating to Big Data were included in bibliometric analysis. Results showed that (1) the number of Big Data publications has been increasing since 2000, (2) US institutes have been in close collaborations with China, Canada, and Germany, (3) some institutes (e.g., University of California system, MD Anderson Cancer Center, and Harvard Medical School) are among the most productive institutes and started using Big Data in HIV/AIDS research early, (4) Big Data research was not active in public health disciplines until 2015, (5) research topics such as genomics, HIV comorbidities, population-based studies, Electronic Health Records (EHR), social media, precision medicine, and methodologies such as machine learning, Deep Learning, radiomics, and data mining emerge quickly in recent years.
Conclusions
We identified a rapid growth in the cross-disciplinary research of HIV/AIDS and Big Data over the past two decades. Our findings demonstrated patterns and trends of prevailing research topics and Big Data applications in HIV/AIDS research and suggested a number of fast-evolving areas of Big Data Science in HIV/AIDS research including secondary analysis of EHR, machine learning, Deep Learning, predictive analysis, and NLP.},
	language = {en},
	urldate = {2021-09-20},
	journal = {International Journal of Medical Informatics},
	author = {Liang, Chen and Qiao, Shan and Olatosi, Bankole and Lyu, Tianchu and Li, Xiaoming},
	month = oct,
	year = {2021},
	keywords = {AIDS, Bibliometrics, Big data, Data mining, Electronic health records, HIV, PLWH},
	pages = {104558},
}

{"_id":"jNb8P4sDs2rpEMqzT","bibbaseid":"liang-qiao-olatosi-lyu-li-emergenceandevolutionofbigdatascienceinhivresearchbibliometricanalysisoffederallysponsoredstudies20002019-2021","author_short":["Liang, C.","Qiao, S.","Olatosi, B.","Lyu, T.","Li, X."],"bibdata":{"bibtype":"article","type":"article","title":"Emergence and evolution of big data science in HIV research: Bibliometric analysis of federally sponsored studies 2000–2019","volume":"154","issn":"1386-5056","shorttitle":"Emergence and evolution of big data science in HIV research","url":"https://www.sciencedirect.com/science/article/pii/S1386505621001842","doi":"10.1016/j.ijmedinf.2021.104558","abstract":"Background The rapid growth of inherently complex and heterogeneous data in HIV/AIDS research underscores the importance of Big Data Science. Recently, there have been increasing uptakes of Big Data techniques in basic, clinical, and public health fields of HIV/AIDS research. However, no studies have systematically elaborated on the evolving applications of Big Data in HIV/AIDS research. We sought to explore the emergence and evolution of Big Data Science in HIV/AIDS-related publications that were funded by the US federal agencies. Methods We identified HIV/AIDS and Big Data related publications that were funded by seven federal agencies from 2000 to 2019 by integrating data from National Institutes of Health (NIH) ExPORTER, MEDLINE, and MeSH. Building on bibliometrics and Natural Language Processing (NLP) methods, we constructed co-occurrence networks using bibliographic metadata (e.g., countries, institutes, MeSH terms, and keywords) of the retrieved publications. We then detected clusters among the networks as well as the temporal dynamics of clusters, followed by expert evaluation and clinical implications. Results We harnessed nearly 600 thousand publications related to HIV/AIDS, of which 19,528 publications relating to Big Data were included in bibliometric analysis. Results showed that (1) the number of Big Data publications has been increasing since 2000, (2) US institutes have been in close collaborations with China, Canada, and Germany, (3) some institutes (e.g., University of California system, MD Anderson Cancer Center, and Harvard Medical School) are among the most productive institutes and started using Big Data in HIV/AIDS research early, (4) Big Data research was not active in public health disciplines until 2015, (5) research topics such as genomics, HIV comorbidities, population-based studies, Electronic Health Records (EHR), social media, precision medicine, and methodologies such as machine learning, Deep Learning, radiomics, and data mining emerge quickly in recent years. Conclusions We identified a rapid growth in the cross-disciplinary research of HIV/AIDS and Big Data over the past two decades. Our findings demonstrated patterns and trends of prevailing research topics and Big Data applications in HIV/AIDS research and suggested a number of fast-evolving areas of Big Data Science in HIV/AIDS research including secondary analysis of EHR, machine learning, Deep Learning, predictive analysis, and NLP.","language":"en","urldate":"2021-09-20","journal":"International Journal of Medical Informatics","author":[{"propositions":[],"lastnames":["Liang"],"firstnames":["Chen"],"suffixes":[]},{"propositions":[],"lastnames":["Qiao"],"firstnames":["Shan"],"suffixes":[]},{"propositions":[],"lastnames":["Olatosi"],"firstnames":["Bankole"],"suffixes":[]},{"propositions":[],"lastnames":["Lyu"],"firstnames":["Tianchu"],"suffixes":[]},{"propositions":[],"lastnames":["Li"],"firstnames":["Xiaoming"],"suffixes":[]}],"month":"October","year":"2021","keywords":"AIDS, Bibliometrics, Big data, Data mining, Electronic health records, HIV, PLWH","pages":"104558","bibtex":"@article{liang_emergence_2021,\n\ttitle = {Emergence and evolution of big data science in {HIV} research: {Bibliometric} analysis of federally sponsored studies 2000–2019},\n\tvolume = {154},\n\tissn = {1386-5056},\n\tshorttitle = {Emergence and evolution of big data science in {HIV} research},\n\turl = {https://www.sciencedirect.com/science/article/pii/S1386505621001842},\n\tdoi = {10.1016/j.ijmedinf.2021.104558},\n\tabstract = {Background\nThe rapid growth of inherently complex and heterogeneous data in HIV/AIDS research underscores the importance of Big Data Science. Recently, there have been increasing uptakes of Big Data techniques in basic, clinical, and public health fields of HIV/AIDS research. However, no studies have systematically elaborated on the evolving applications of Big Data in HIV/AIDS research. We sought to explore the emergence and evolution of Big Data Science in HIV/AIDS-related publications that were funded by the US federal agencies.\nMethods\nWe identified HIV/AIDS and Big Data related publications that were funded by seven federal agencies from 2000 to 2019 by integrating data from National Institutes of Health (NIH) ExPORTER, MEDLINE, and MeSH. Building on bibliometrics and Natural Language Processing (NLP) methods, we constructed co-occurrence networks using bibliographic metadata (e.g., countries, institutes, MeSH terms, and keywords) of the retrieved publications. We then detected clusters among the networks as well as the temporal dynamics of clusters, followed by expert evaluation and clinical implications.\nResults\nWe harnessed nearly 600 thousand publications related to HIV/AIDS, of which 19,528 publications relating to Big Data were included in bibliometric analysis. Results showed that (1) the number of Big Data publications has been increasing since 2000, (2) US institutes have been in close collaborations with China, Canada, and Germany, (3) some institutes (e.g., University of California system, MD Anderson Cancer Center, and Harvard Medical School) are among the most productive institutes and started using Big Data in HIV/AIDS research early, (4) Big Data research was not active in public health disciplines until 2015, (5) research topics such as genomics, HIV comorbidities, population-based studies, Electronic Health Records (EHR), social media, precision medicine, and methodologies such as machine learning, Deep Learning, radiomics, and data mining emerge quickly in recent years.\nConclusions\nWe identified a rapid growth in the cross-disciplinary research of HIV/AIDS and Big Data over the past two decades. Our findings demonstrated patterns and trends of prevailing research topics and Big Data applications in HIV/AIDS research and suggested a number of fast-evolving areas of Big Data Science in HIV/AIDS research including secondary analysis of EHR, machine learning, Deep Learning, predictive analysis, and NLP.},\n\tlanguage = {en},\n\turldate = {2021-09-20},\n\tjournal = {International Journal of Medical Informatics},\n\tauthor = {Liang, Chen and Qiao, Shan and Olatosi, Bankole and Lyu, Tianchu and Li, Xiaoming},\n\tmonth = oct,\n\tyear = {2021},\n\tkeywords = {AIDS, Bibliometrics, Big data, Data mining, Electronic health records, HIV, PLWH},\n\tpages = {104558},\n}\n\n","author_short":["Liang, C.","Qiao, S.","Olatosi, B.","Lyu, T.","Li, X."],"key":"liang_emergence_2021","id":"liang_emergence_2021","bibbaseid":"liang-qiao-olatosi-lyu-li-emergenceandevolutionofbigdatascienceinhivresearchbibliometricanalysisoffederallysponsoredstudies20002019-2021","role":"author","urls":{"Paper":"https://www.sciencedirect.com/science/article/pii/S1386505621001842"},"keyword":["AIDS","Bibliometrics","Big data","Data mining","Electronic health records","HIV","PLWH"],"metadata":{"authorlinks":{}},"html":""},"bibtype":"article","biburl":"https://bibbase.org/zotero/iagogv","dataSources":["ovn29uG6Mbp3JWCRR"],"keywords":["aids","bibliometrics","big data","data mining","electronic health records","hiv","plwh"],"search_terms":["emergence","evolution","big","data","science","hiv","research","bibliometric","analysis","federally","sponsored","studies","2000","2019","liang","qiao","olatosi","lyu","li"],"title":"Emergence and evolution of big data science in HIV research: Bibliometric analysis of federally sponsored studies 2000–2019","year":2021}