| 词条 | Danielle Belgrave |
| 释义 |
| birth_name = Danielle Charlotte Belgrave | name = Danielle Belgrave | fields = Statistics Machine learning[1] | alma_mater = London School of Economics (BSc) University College London (MSc) University of Manchester (PhD) | thesis_title = Probabilistic causal models for asthma and allergies developing in childhood | thesis_year = 2014 | thesis_url = https://www.librarysearch.manchester.ac.uk/discovery/fulldisplay?docid=alma9960190960001631&context=L&vid=44MAN_INST:MU_NUI&search_scope=MyInst_and_CI&tab=Everything&lang=en | doctoral_advisor = Iain Buchan Christopher Bishop Adnan Custovic[2][3] | workplaces = Microsoft Research Imperial College London GlaxoSmithKline | website = {{URL|microsoft.com/en-us/research/people/dabelgra}} }}Danielle Charlotte Belgrave is a British computer scientist based at Microsoft Research, who uses statistics and machine learning to understand the progression of diseases.[1][2][6] Early life and educationBelgrave grew up in Trinidad, where her high school mathematics teacher inspired her to work as a data scientist.[3] She studied statistics and business at the London School of Economics (LSE).[4][5] She was a graduate student at University College London (UCL), where she earned a master's degree in statistics.[4] In 2010 Belgrave moved to the University of Manchester, where she earned a PhD for research supervised by {{ill|Iain Buchan|wd=Q47503095|reasonator=1}}, Christopher Bishop and {{ill|Adnan Custovic (scientist)|wd=Q37615657|reasonator=1}}[2][6][4] supported by a Microsoft Research scholarship. She was awarded a Dorothy Hodgkin postgraduate award by Microsoft and the Barry Kay Award by the British Society of Allergy and Clinical Immunology (BSACI).[7] Research and careerAfter graduating, Belgrave worked at GlaxoSmithKline (GSK), where she was awarded the Exceptional Scientist Award.[4] Belgrave joined Imperial College London as a Medical Research Council (MRC) statistician in 2015.[4][8][7] She develops statistical machine learning models to look at disease progression in an effort to design new management strategies and understand heterogeneity.[9][10] Statistical learning methods can inform the management of medical conditions by providing a framework for endotype discovery using probabilistic modelling.[3][11] She uses statistical models to identify the underlying endotypes of a condition from a set of phenotypes.[12] She studied whether atopic march, the progression of allergic diseases from early life, adequately describes atopic diseases like eczema in early life.[13] Belgrave used a latent disease profile model to study atopic march in over 9,000 children, where machine learning was used to identify groups of children with similar eczema onset patterns.[13] She is part of the study team for early life asthma research consortium.[14] Belgrave is interested in using big data for meaningful clinical interpretation, to inform personalised prevention strategies.[14] Her research focuses on Bayesian and statistical machine learning within the healthcare to develop personalised medicine.[2] {{As of|2019}} Belgrave is developing and implementing methods which incorporate domain knowledge with data-driven models. Her research interests include latent variable models, longitudinal studies, survival analysis, ‘omics, dimensionality reduction, Bayesian graphical models and cluster analysis.[2][1] Belgrave is part of the regulatory algorithms project, which evaluates how healthcare algorithms should be regulated.[15] In particular, Belgrave is interested in what scheme of liability should be imposed on artificial intelligence for healthcare.[15] She serves on the 2019 organising committee of the Conference on Neural Information Processing Systems[16] and as an advisor for DeepAfricAI.[17] References1. ^1 2 {{Google scholar id}} {{Authority control}}{{DEFAULTSORT:Belgrave, Danielle}}2. ^1 2 3 4 {{cite web|archiveurl=https://web.archive.org/web/20190313221432/https://www.imperial.ac.uk/people/d.belgrave/cv/Danielle%20Belgrave%20CV%202018.pdf|archivedate=2019-03-13|url=https://www.imperial.ac.uk/people/d.belgrave/cv/Danielle%20Belgrave%20CV%202018.pdf|first=Danielle|last=Belgrave|year=2016|title=Danielle Belgrave CV|website=imperial.ac.uk|publisher=Imperial College London}} 3. ^1 {{Cite web|url=http://www.deeplearningindaba.com/danielle-belgrave.html|title=Danielle Belgrave|website=deeplearningindaba.com|publisher=Deep Learning Indaba|language=en|access-date=2019-03-16}} 4. ^1 2 3 4 {{Cite web|url=https://www.imperial.ac.uk/people/d.belgrave|title=Dr Danielle Belgrave|website=imperial.ac.uk|access-date=2019-03-16|archiveurl=https://web.archive.org/web/20180105050233/https://www.imperial.ac.uk/people/d.belgrave|archivedate=2018-01-05|publisher=Imperial College London}} 5. ^{{Cite web|url=http://www.datascience.manchester.ac.uk/events-1/events/advances-and-challenges-in-machine-learning-for-healthcare-seminar/|title=Advances and Challenges in Machine Learning for healthcare Seminar|author=Anon|year=2019|publisher=University of Manchester|website=datascience.manchester.ac.uk|language=en|access-date=2019-03-16}} 6. ^1 {{cite thesis|degree=PhD|first=Danielle Charlotte|last=Belgrave|year=2014|url=https://www.librarysearch.manchester.ac.uk/discovery/fulldisplay?docid=alma9960190960001631&context=L&vid=44MAN_INST:MU_NUI&search_scope=MyInst_and_CI&tab=Everything&lang=en|website=manchester.ac.uk|publisher=University of Manchester|title=Probabilistic causal models for asthma and allergies developing in childhood |oclc=}} 7. ^1 {{Cite web|url=http://www.cipp-meeting.org/en/faculty-members/id-65-danielle-belgrave|title=Danielle Belgrave|website=cipp-meeting.org|publisher=CIPP XV|language=en|access-date=2019-03-16}} 8. ^{{Cite web|url=https://gtr.ukri.org/projects?ref=MR%2FM015181%2F1|title=Unified probabilistic latent variable modelling strategies to accelerate endotype discovery in longitudinal studies|last=|first=|date=|website=ukri.org|publisher=United Kingdom Research and Innovation|archive-url=|archive-date=|dead-url=|access-date=2019-03-16}} 9. ^1 {{Cite web|url=https://www.re-work.co/events/deep-learning-in-healthcare-summit-london-2018/speakers/danielle-belgrave|title=Danielle Belgrave|publisher=RE•WORK|website=re-work.co|language=en|access-date=2019-03-16}} 10. ^{{Cite web|url=https://www.microsoft.com/en-us/research/people/dabelgra/|title=Danielle Belgrave at Microsoft Research|website=microsoft.com|publisher=Microsoft Research|language=en-US|access-date=2019-03-16|archiveurl=https://web.archive.org/web/20190317213130/https://www.microsoft.com/en-us/research/people/dabelgra/|archivedate=2019-03-17}} 11. ^{{Citation|author=Anon|publisher=Deep Learning Indaba|title=12 Applications of Machine Learning in Healthcare by Danielle Belgrave|date=2017-09-15|url=https://www.youtube.com/watch?v=G06sG58E850|access-date=2019-03-16|website=youtube.com}} 12. ^{{Cite web|url=http://www.robotethics.co.uk/ethical-ai/|title=Ethical AI|author=Anon|date=2019-03-07|website=robotethics.co.uk|publisher=AI and Robot Ethics|language=en-GB|access-date=2019-03-16}} 13. ^1 {{Cite journal|last=Custovic|first=Adnan|last2=Henderson|first2=A. John|last3=Buchan|first3=Iain|last4=Bishop|first4=Christopher|last5=Guiver|first5=John|last6=Simpson|first6=Angela|last7=Granell|first7=Raquel|last8=Belgrave|first8=Danielle C. M.|date=2014|title=Developmental Profiles of Eczema, Wheeze, and Rhinitis: Two Population-Based Birth Cohort Studies|journal=PLOS Medicine|language=en|volume=11|issue=10|pages=e1001748|doi=10.1371/journal.pmed.1001748|issn=1549-1676|pmc=4204810|pmid=25335105}} 14. ^1 {{cite journal|last1=Bønnelykke|first1=Klaus|last2=Sleiman|first2=Patrick|last3=Nielsen|first3=Kasper|last4=Kreiner-Møller|first4=Eskil|last5=Mercader|first5=Josep M|last6=Belgrave|first6=Danielle|last7=den Dekker|first7=Herman T|last8=Husby|first8=Anders|last9=Sevelsted|first9=Astrid|last10=Faura-Tellez|first10=Grissel|last11=Mortensen|first11=Li Juel|last12=Paternoster|first12=Lavinia|last13=Flaaten|first13=Richard|last14=Mølgaard|first14=Anne|last15=Smart|first15=David E|last16=Thomsen|first16=Philip F|last17=Rasmussen|first17=Morten A|last18=Bonàs-Guarch|first18=Silvia|last19=Holst|first19=Claus|last20=Nohr|first20=Ellen A|last21=Yadav|first21=Rachita|last22=March|first22=Michael E|last23=Blicher|first23=Thomas|last24=Lackie|first24=Peter M|last25=Jaddoe|first25=Vincent W V|last26=Simpson|first26=Angela|last27=Holloway|first27=John W|last28=Duijts|first28=Liesbeth|last29=Custovic|first29=Adnan|last30=Davies|first30=Donna E|last31=Torrents|first31=David|last32=Gupta|first32=Ramneek|last33=Hollegaard|first33=Mads V|last34=Hougaard|first34=David M|last35=Hakonarson|first35=Hakon|last36=Bisgaard|first36=Hans|display-authors=11|title=A genome-wide association study identifies CDHR3 as a susceptibility locus for early childhood asthma with severe exacerbations|journal=Nature Genetics|pmid= 24241537 |volume=46|issue=1|year=2013|pages=51–55|issn=1061-4036|doi=10.1038/ng.2830|oclc=885448463}} {{closed access}} 15. ^1 {{Cite web|url=http://www.phgfoundation.org/blog/regulating-algorithims-ip-liability|title=Regulating algorithms in healthcare: IP and liability |publisher= PHG Foundation|website=phgfoundation.org|access-date=2019-03-16}} 16. ^{{Cite web|url=https://nips.cc/Conferences/2019/Committees|title=2019 Organizing Committee|website=nips.cc|access-date=2019-03-16}} 17. ^{{Cite web|url=http://deepafricai.com/team.php|title=DeepAfricAI|website=deepafricai.com|access-date=2019-03-16}} 9 : Year of birth missing (living people)|Living people|Trinidad and Tobago people|British computer scientists|Women computer scientists|Academics of Imperial College London|Alumni of the University of Manchester|Alumni of University College London|Alumni of the London School of Economics |
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