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Book ; Online: Stochastic Variable Metric Proximal Gradient with variance reduction for non-convex composite optimization

Fort, Gersende / Moulines, Eric

2023

Abstract: This paper introduces a novel algorithm, the Perturbed Proximal Preconditioned SPIDER algorithm (3P-SPIDER), designed to solve finite sum non-convex composite optimization. It is a stochastic Variable Metric Forward-Backward algorithm, which allows ... ...

Abstract	This paper introduces a novel algorithm, the Perturbed Proximal Preconditioned SPIDER algorithm (3P-SPIDER), designed to solve finite sum non-convex composite optimization. It is a stochastic Variable Metric Forward-Backward algorithm, which allows approximate preconditioned forward operator and uses a variable metric proximity operator as the backward operator; it also proposes a mini-batch strategy with variance reduction to address the finite sum setting. We show that 3P-SPIDER extends some Stochastic preconditioned Gradient Descent-based algorithms and some Incremental Expectation Maximization algorithms to composite optimization and to the case the forward operator can not be computed in closed form. We also provide an explicit control of convergence in expectation of 3P-SPIDER, and study its complexity in order to satisfy the epsilon-approximate stationary condition. Our results are the first to combine the composite non-convex optimization setting, a variance reduction technique to tackle the finite sum setting by using a minibatch strategy and, to allow deterministic or random approximations of the preconditioned forward operator. Finally, through an application to inference in a logistic regression model with random effects, we numerically compare 3P-SPIDER to other stochastic forward-backward algorithms and discuss the role of some design parameters of 3P-SPIDER. Comment: Statistics and Computing, In press
Keywords	Computer Science - Machine Learning ; Mathematics - Optimization and Control
Subject code	510
Publishing date	2023-01-02
Publishing country	us
Document type	Book ; Online
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Article ; Online: Temporal evolution of the Covid19 pandemic reproduction number: Estimations from proximal optimization to Monte Carlo sampling.

Abry, Patrice / Fort, Gersende / Pascal, Barbara / Pustelnik, Nelly

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

2022 Volume 2022, Page(s) 167–170

Abstract: Monitoring the evolution of the Covid19 pandemic constitutes a critical step in sanitary policy design. Yet, the assessment of the pandemic intensity within the pandemic period remains a challenging task because of the limited quality of data made ... ...

Abstract	Monitoring the evolution of the Covid19 pandemic constitutes a critical step in sanitary policy design. Yet, the assessment of the pandemic intensity within the pandemic period remains a challenging task because of the limited quality of data made available by public health authorities (missing data, outliers and pseudoseasonalities, notably), that calls for cumbersome and ad-hoc preprocessing (denoising) prior to estimation. Recently, the estimation of the reproduction number, a measure of the pandemic intensity, was formulated as an inverse problem, combining data-model fidelity and space-time regularity constraints, solved by nonsmooth convex proximal minimizations. Though promising, that formulation lacks robustness against the limited quality of the Covid19 data and confidence assessment. The present work aims to address both limitations: First, it discusses solutions to produce a robust assessment of the pandemic intensity by accounting for the low quality of the data directly within the inverse problem formulation. Second, exploiting a Bayesian interpretation of the inverse problem formulation, it devises a Monte Carlo sampling strategy, tailored to a nonsmooth log-concave a posteriori distribution, to produce relevant credibility interval-based estimates for the Covid19 reproduction number. Clinical relevance Applied to daily counts of new infections made publicly available by the Health Authorities for around 200 countries, the proposed procedures permit robust assessments of the time evolution of the Covid19 pandemic intensity, updated automatically and on a daily basis.
MeSH term(s)	Bayes Theorem ; COVID-19/epidemiology ; Humans ; Monte Carlo Method ; Pandemics ; Reproduction
Language	English
Publishing date	2022-09-09
Publishing country	United States
Document type	Journal Article
ISSN	2694-0604
ISSN (online)	2694-0604
DOI	10.1109/EMBC48229.2022.9871805
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Book ; Online: Covid19 Reproduction Number

Fort, Gersende / Pascal, Barbara / Abry, Patrice / Pustelnik, Nelly

Credibility Intervals by Blockwise Proximal Monte Carlo Samplers

2022

Abstract: Monitoring the Covid19 pandemic constitutes a critical societal stake that received considerable research efforts. The intensity of the pandemic on a given territory is efficiently measured by the reproduction number, quantifying the rate of growth of ... ...

Abstract	Monitoring the Covid19 pandemic constitutes a critical societal stake that received considerable research efforts. The intensity of the pandemic on a given territory is efficiently measured by the reproduction number, quantifying the rate of growth of daily new infections. Recently, estimates for the time evolution of the reproduction number were produced using an inverse problem formulation with a nonsmooth functional minimization. While it was designed to be robust to the limited quality of the Covid19 data (outliers, missing counts), the procedure lacks the ability to output credibility interval based estimates. This remains a severe limitation for practical use in actual pandemic monitoring by epidemiologists that the present work aims to overcome by use of Monte Carlo sampling. After interpretation of the nonsmooth functional into a Bayesian framework, several sampling schemes are tailored to adjust the nonsmooth nature of the resulting posterior distribution. The originality of the devised algorithms stems from combining a Langevin Monte Carlo sampling scheme with Proximal operators. Performance of the new algorithms in producing relevant credibility intervals for the reproduction number estimates and denoised counts are compared. Assessment is conducted on real daily new infection counts made available by the Johns Hopkins University. The interest of the devised monitoring tools are illustrated on Covid19 data from several different countries.
Keywords	Computer Science - Machine Learning ; Electrical Engineering and Systems Science - Signal Processing ; Statistics - Applications ; Statistics - Methodology ; Statistics - Machine Learning
Subject code	310
Publishing date	2022-03-17
Publishing country	us
Document type	Book ; Online
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Book ; Online: Federated Expectation Maximization with heterogeneity mitigation and variance reduction

Dieuleveut, Aymeric / Fort, Gersende / Moulines, Eric / Robin, Geneviève

2021

Abstract: The Expectation Maximization (EM) algorithm is the default algorithm for inference in latent variable models. As in any other field of machine learning, applications of latent variable models to very large datasets make the use of advanced parallel and ... ...

Abstract	The Expectation Maximization (EM) algorithm is the default algorithm for inference in latent variable models. As in any other field of machine learning, applications of latent variable models to very large datasets make the use of advanced parallel and distributed architectures mandatory. This paper introduces FedEM, which is the first extension of the EM algorithm to the federated learning context. FedEM is a new communication efficient method, which handles partial participation of local devices, and is robust to heterogeneous distributions of the datasets. To alleviate the communication bottleneck, FedEM compresses appropriately defined complete data sufficient statistics. We also develop and analyze an extension of FedEM to further incorporate a variance reduction scheme. In all cases, we derive finite-time complexity bounds for smooth non-convex problems. Numerical results are presented to support our theoretical findings, as well as an application to federated missing values imputation for biodiversity monitoring.
Keywords	Mathematics - Optimization and Control ; Computer Science - Machine Learning
Subject code	006
Publishing date	2021-11-03
Publishing country	us
Document type	Book ; Online
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Book ; Online: A Stochastic Path-Integrated Differential EstimatoR Expectation Maximization Algorithm

Fort, Gersende / Moulines, Eric / Wai, Hoi-To

2020

Abstract: The Expectation Maximization (EM) algorithm is of key importance for inference in latent variable models including mixture of regressors and experts, missing observations. This paper introduces a novel EM algorithm, called \texttt{SPIDER-EM}, for ... ...

Abstract	The Expectation Maximization (EM) algorithm is of key importance for inference in latent variable models including mixture of regressors and experts, missing observations. This paper introduces a novel EM algorithm, called \texttt{SPIDER-EM}, for inference from a training set of size $n$, $n \gg 1$. At the core of our algorithm is an estimator of the full conditional expectation in the {\sf E}-step, adapted from the stochastic path-integrated differential estimator ({\tt SPIDER}) technique. We derive finite-time complexity bounds for smooth non-convex likelihood: we show that for convergence to an $\epsilon$-approximate stationary point, the complexity scales as $K_{\operatorname{Opt}} (n,\epsilon )={\cal O}(\epsilon^{-1})$ and $K_{\operatorname{CE}}( n,\epsilon ) = n+ \sqrt{n} {\cal O}(\epsilon^{-1} )$, where $K_{\operatorname{Opt}}( n,\epsilon )$ and $K_{\operatorname{CE}}(n, \epsilon )$ are respectively the number of {\sf M}-steps and the number of per-sample conditional expectations evaluations. This improves over the state-of-the-art algorithms. Numerical results support our findings.
Keywords	Computer Science - Machine Learning ; Computer Science - Artificial Intelligence ; Mathematics - Statistics Theory ; Statistics - Machine Learning
Subject code	518
Publishing date	2020-11-30
Publishing country	us
Document type	Book ; Online
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Book ; Online: Fast Incremental Expectation Maximization for finite-sum optimization

Fort, Gersende / Gach, P. / Moulines, E.

nonasymptotic convergence

2020

Abstract: Fast Incremental Expectation Maximization (FIEM) is a version of the EM framework for large datasets. In this paper, we first recast FIEM and other incremental EM type algorithms in the {\em Stochastic Approximation within EM} framework. Then, we provide ...

Abstract	Fast Incremental Expectation Maximization (FIEM) is a version of the EM framework for large datasets. In this paper, we first recast FIEM and other incremental EM type algorithms in the {\em Stochastic Approximation within EM} framework. Then, we provide nonasymptotic bounds for the convergence in expectation as a function of the number of examples $n$ and of the maximal number of iterations $\kmax$. We propose two strategies for achieving an $\epsilon$-approximate stationary point, respectively with $\kmax = O(n^{2/3}/\epsilon)$ and $\kmax = O(\sqrt{n}/\epsilon^{3/2})$, both strategies relying on a random termination rule before $\kmax$ and on a constant step size in the Stochastic Approximation step. Our bounds provide some improvements on the literature. First, they allow $\kmax$ to scale as $\sqrt{n}$ which is better than $n^{2/3}$ which was the best rate obtained so far; it is at the cost of a larger dependence upon the tolerance $\epsilon$, thus making this control relevant for small to medium accuracy with respect to the number of examples $n$. Second, for the $n^{2/3}$-rate, the numerical illustrations show that thanks to an optimized choice of the step size and of the bounds in terms of quantities characterizing the optimization problem at hand, our results desig a less conservative choice of the step size and provide a better control of the convergence in expectation.
Keywords	Computer Science - Machine Learning ; Computer Science - Artificial Intelligence ; Statistics - Machine Learning
Subject code	518
Publishing date	2020-12-29
Publishing country	us
Document type	Book ; Online
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Article: Risk Assessment of Infectious Endogenous Banana Streak Viruses in Guadeloupe.

Umber, Marie / Pressat, Gersende / Fort, Guillaume / Plaisir Pineau, Kaïssa / Guiougiou, Chantal / Lambert, Frédéric / Farinas, Benoît / Pichaut, Jean-Philippe / Janzac, Bérenger / Delos, Jean-Marie / Salmon, Frédéric / Dubois, Cécile / Teycheney, Pierre-Yves

Frontiers in plant science

2022 Volume 13, Page(s) 951285

Abstract: Infectious alleles of endogenous banana streak viruses (eBSVs) are present in the genome of all banana interspecific cultivars, including plantains and cooking types. Activation of these infectious eBSV alleles by biotic and abiotic stresses leads to ... ...

Abstract	Infectious alleles of endogenous banana streak viruses (eBSVs) are present in the genome of all banana interspecific cultivars, including plantains and cooking types. Activation of these infectious eBSV alleles by biotic and abiotic stresses leads to spontaneous infections by cognate viruses and raises concerns about their ability to promote outbreaks of banana streak viruses under field cultivation conditions. We undertook a comprehensive risk assessment study of infectious eBSV alleles of species BSOLV, BSGFV and BSIMV in banana interspecific cultivars in Guadeloupe, a tropical island of the Caribbean where bananas are grown for export and local markets. We carried out a prevalence survey of BSOLV, BSGFV and BSIMV species in a range of cultivars grown in Guadeloupe. Our results suggest that BSOLV and BSGFV infections arise from the activation of infectious eBSVs rather than vector-borne transmission and point to a correlation between altitude and infection rates in interspecific hybrids with AAB genotypes. We studied the dynamics of activation of infectious eBSOLV and eBSGFV alleles by tissue culture and field cultivation in a range of cultivars. We showed that tissue culture and field cultivation trigger distinct activation pathways, resulting in distinct activation patterns. We also showed that activation decreased over time during cell culture and field cultivation and that BSV infections arising from the activation of infectious eBSV alleles cause symptomless infections in the most cultivated plantain in Guadeloupe, French Clair. Overall, our study shows that the risk of BSV outbreaks resulting from the activation of infectious eBSVs in plantain originating from vegetative multiplication is negligible in Guadeloupe.
Language	English
Publishing date	2022-07-11
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2613694-6
ISSN	1664-462X
ISSN	1664-462X
DOI	10.3389/fpls.2022.951285
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Article: Combining Monte Carlo and mean-field-like methods for inference in hidden Markov random fields.

Forbes, Florence / Fort, Gersende

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society

2006 Volume 16, Issue 3, Page(s) 824–837

Abstract: Issues involving missing data are typical settings where exact inference is not tractable as soon as nontrivial interactions occur between the missing variables. Approximations are required, and most of them are based either on simulation methods or on ... ...

Abstract	Issues involving missing data are typical settings where exact inference is not tractable as soon as nontrivial interactions occur between the missing variables. Approximations are required, and most of them are based either on simulation methods or on deterministic variational methods. While variational methods provide fast and reasonable approximate estimates in many scenarios, simulation methods offer more consideration of important theoretical issues such as accuracy of the approximation and convergence of the algorithms but at a much higher computational cost. In this work, we propose a new class of algorithms that combine the main features and advantages of both simulation and deterministic methods and consider applications to inference in hidden Markov random fields (HMRFs). These algorithms can be viewed as stochastic perturbations of variational expectation maximization (VEM) algorithms, which are not tractable for HMRF. We focus more specifically on one of these perturbations and we prove their (almost sure) convergence to the same limit set as the limit set of VEM. In addition, experiments on synthetic and real-world images show that the algorithm performance is very close and sometimes better than that of other existing simulation-based and variational EM-like algorithms.
MeSH term(s)	Algorithms ; Artificial Intelligence ; Computer Simulation ; Data Interpretation, Statistical ; Image Enhancement/methods ; Image Interpretation, Computer-Assisted/methods ; Markov Chains ; Models, Statistical ; Monte Carlo Method ; Pattern Recognition, Automated/methods
Language	English
Publishing date	2006-12-15
Publishing country	United States
Document type	Evaluation Studies ; Journal Article ; Research Support, Non-U.S. Gov't
ISSN	1057-7149
ISSN	1057-7149
DOI	10.1109/tip.2006.891045
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Article: Classification using partial least squares with penalized logistic regression.

Fort, Gersende / Lambert-Lacroix, Sophie

Bioinformatics (Oxford, England)

2005 Volume 21, Issue 7, Page(s) 1104–1111

Abstract: Motivation: One important aspect of data-mining of microarray data is to discover the molecular variation among cancers. In microarray studies, the number n of samples is relatively small compared to the number p of genes per sample (usually in ... ...

Abstract	Motivation: One important aspect of data-mining of microarray data is to discover the molecular variation among cancers. In microarray studies, the number n of samples is relatively small compared to the number p of genes per sample (usually in thousands). It is known that standard statistical methods in classification are efficient (i.e. in the present case, yield successful classifiers) particularly when n is (far) larger than p. This naturally calls for the use of a dimension reduction procedure together with the classification one. Results: In this paper, the question of classification in such a high-dimensional setting is addressed. We view the classification problem as a regression one with few observations and many predictor variables. We propose a new method combining partial least squares (PLS) and Ridge penalized logistic regression. We review the existing methods based on PLS and/or penalized likelihood techniques, outline their interest in some cases and theoretically explain their sometimes poor behavior. Our procedure is compared with these other classifiers. The predictive performance of the resulting classification rule is illustrated on three data sets: Leukemia, Colon and Prostate.
MeSH term(s)	Algorithms ; Biomarkers, Tumor/genetics ; Biomarkers, Tumor/metabolism ; Diagnosis, Computer-Assisted/methods ; Gene Expression Profiling/methods ; Humans ; Least-Squares Analysis ; Models, Genetic ; Models, Statistical ; Neoplasm Proteins/genetics ; Neoplasm Proteins/metabolism ; Neoplasms/diagnosis ; Neoplasms/genetics ; Neoplasms/metabolism ; Oligonucleotide Array Sequence Analysis/methods ; Pattern Recognition, Automated/methods ; Regression Analysis ; Reproducibility of Results ; Sensitivity and Specificity
Chemical Substances	Biomarkers, Tumor ; Neoplasm Proteins
Language	English
Publishing date	2005-04-01
Publishing country	England
Document type	Comparative Study ; Evaluation Studies ; Journal Article ; Research Support, Non-U.S. Gov't ; Validation Studies
ZDB-ID	1422668-6
ISSN	1367-4803
ISSN	1367-4803
DOI	10.1093/bioinformatics/bti114
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Article ; Online: Women and health professionals’ perspectives on a conditional cash transfer programme to improve pregnancy follow-up

Celine Chauleur / Jacob Hannigsberg / Philippe Merviel / Marc Bardou / Franck Perrotin / Thomas Schmitz / Olivier Picone / Jeanne Sibiude / Karine Chemin / Dominique Dallay / Frédéric Coatleven / Loïc Sentilhes / Céline Brochot / Astrid Eckman-Lacroix / Elise Thellier / Frédérique Falchier / Philippe Deruelle / Muriel Doret / Xavier Carcopino-Tusoli /

Nicolas Meunier-Beillard / Hervé Fernandez / Vincent Villefranque / Caroline Diguisto / Damien Subtil / Clémence Houssin / Philippe Gillard / Laurent Mandelbrot / Aurelie Godard-Marceau / Nathalie Lesavre / Claude Virtos / Elodie Debras / Aude Bourtembourg / Claire Toubin / Danièle Addes / Véronique Uguen / Cleo Tourbot / Caroline Lelievre / Christophe Tremouilhac / Anne-Hélène Saliou / Aurelie Derrieu / Stephanie Auget / Anne Legourrierec / Anne Leroux / Julie Fort-Jacquier / Marion Serclerat / Nathalie Laurenceau / Audrey Renouleau / Eliane Catteau / Julie Blanc / Candice Ronin / Laurence Piechon / Séverine Puppo / Fanny Greco / Sandrine Pettazzoni / Muriel Athlani / Amina Desvignes / Annie Petiteau / Amina El Yaakoubi / Valérie Bechadergue / Valérie Vaugirard / Marie-Emmanuelle Neveu / Caroline Geyl / Marie-Victoire Senat / Claire Colmant / Marie Houllier / Myriam Virlouet / Marion Mir / Yasmina Bejaoui / Hélène Le Cornu / Lauriane Nikel / Elodie Gustave / Amandine Stadler / Ahmad Mehdi / Tiphaine Barjat / Suzanne Lima / Thomas Corsini / Anne Genod / Charlotte Vermesch / Cécile Fanget / Marianne Perrot / Manuela Munoz / Sylvie Pitaval / Fanny Magand / Françoise Baldi / Stephanie Bret / Anne-Lise Verdier / Christelle Denis / Carine Arlicot / Jérôme Potin / Stéphanie Chretien / Julie Paternotte / Nathalie Trignol / Élisabeth Blin / Camille Mathieu / Anne Dubreuil / Anne Viallon Pelletier / Catherine Guerin / Chloé Arthuis / Christophe Vayssieres / Olivier Parant / Marion Groussolles / Maria Denis / M Mathieu Morin / Marie-Thérèse Bavoux / Juliette Pelloux / Anne-Claire Jambon / Madeleine Santraine / Veronique Lebuffe / Pascale Broux / Thierry Dzukou / Magloire Gnansounou / Didier Hubert / Claire Djazet / Ludivine Destoop / Marine Derue / Pierrick Theret / Dominique Delzenne / Stéphanie Daussin / Alice Fraissinet / Mélanie Vannerum / Cyril Faraguet / Laurence Landais / Mariana Radu / Anne Rouget / Sena Al Sudani / Bernard Guillon / Estelle Wucher / Véronique Selva / Sandrine Reviron / Francis Schwetterlé / Cécile Chassande / Véronique Grandin / Eliane Krtoliza / Patrick Becher / Marie Sarrau / Claire Lecoq / Elsa Lutringer / Denis Roux / Noémie Berge / Clémentine Barbier / Anne Heron / Audrey Farina-Bracquart / Marie-Paule Curtet / Evelyne Lefebure / Marie-Hélène Le Douarin / Hassan Al Rayes / Émilie Magne / Nathalie Destampes / Émilie Ricard / Pascale Ghezzi / Catherine Guillen / Fanny Alazard / Marie-Thé Campanaro / Florence Mojard / Magalie David-Reynard / Patricia Fuma / Remy De Montgolfier / Capucine Neel / Guillaume Legendre / Isabelle Andre / Sylvie Nordstrom / Brigitte Guionnet / Catherine Crenn Hebert / Chloé Dussaux / Karine Achaintre / Anne Wagner / Martine Werveake / Eloïse De Gouville / George Theresin / Marie Pierre Couetoux / Lydia Caillaud / Marie-Pierre Fernandez / Sabrina Bottet / M Alain Almodovar / Elisa Etienne / Véronique Guiteras / Angélique Torres / N. Roche / Myriam Nassef / Christine Abel-Faure / Marie Louvet / Carole Ettori / Guillaume Ducarme / Valérie Bonnenfant-Mezeray / Laurence Szezot-Renaudeau / Marie-Pierre Berte / Elodie Netier-Herault / Stéphanie Manson-Gallone / Franck Mauviel / Nathalie Agostini / Marine Mazeaud / Jean-Claude Dausset / Isabelle De Murcia / Emilie Alliot / Anne-Marie Bes / Magali Biferi Magali / Hélène Heckenroth / Sophie Morange / Gersende Chiuot / Audrey Gnisci / Annie Allegre / Laetitia Lecq / Eva Balenbois / Claire Tourette / Aude Figarella / Dio Andriamanjay / Pauline Vignoles / Catherine Cazelles / Véronique Lejeune Saada / Benafsheh Kashani / Isabelle Chevalier / Muriel Terrieres / Audrey Cointement / Valérie Benhaïm / Najat Lindoune / Anne-Sophie Maisonneuve / M Frédéric Daubercy / Guilia Mencattini / Vanessa Combaud / Isabelle Moya / Xavier-Côme Donato / Raoul Desbriere / Marie Lafon / Véronique Baudet

BMJ Open, Vol 13, Iss

a qualitative analysis of the NAITRE randomised controlled study

2023 Volume 3

Abstract: Objectives Women of low socioeconomic status have been described as having suboptimal prenatal care, which in turn has been associated with poor pregnancy outcomes. Many types of conditional cash transfer (CCT) programmes have been developed, including ... ...

Abstract	Objectives Women of low socioeconomic status have been described as having suboptimal prenatal care, which in turn has been associated with poor pregnancy outcomes. Many types of conditional cash transfer (CCT) programmes have been developed, including programmes to improve prenatal care or smoking cessation during pregnancy, and their effects demonstrated. However, ethical critiques have included paternalism and lack of informed choice. Our objective was to determine if women and healthcare professionals (HPs) shared these concerns.Design Prospective qualitative research.Setting We included economically disadvantaged women, as defined by health insurance data, who participated in the French NAITRE randomised trial assessing a CCT programme during prenatal follow-up to improve pregnancy outcomes. The HP worked in some maternities participating in this trial.Participants 26 women, 14 who received CCT and 12 who did not, mostly unemployed (20/26), and - 7 HPs.Interventions We conducted a multicentre cross-sectional qualitative study among women and HPs who participated in the NAITRE Study to assess their views on CCT. The women were interviewed after childbirth.Results Women did not perceive CCT negatively. They did not mention feeling stigmatised. They described CCT as a significant source of aid for women with limited financial resources. HP described the CCT in less positive terms, for example, expressing concern about discussing cash transfer at their first medical consultation with women. Though they emphasised ethical concerns about the basis of the trial, they recognised the importance of evaluating CCT.Conclusions In France, a high-income country where prenatal follow-up is free, HPs were concerned that the CCT programme would change their relationship with patients and wondered if it was the best use of funding. However, women who received a cash incentive said they did not feel stigmatised and indicated that these payments helped them prepare for their baby’s birth.Trial registration number NCT02402855
Keywords	Medicine ; R
Subject code	300
Language	English
Publishing date	2023-03-01T00:00:00Z
Publisher	BMJ Publishing Group
Document type	Article ; Online
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