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Conference paperWang K, Saputra RP, Foster JP, et al., 2021,
Improved energy efficiency via parallel elastic elements for the straight-legged vertically-compliant robot SLIDER
, Japan, 24th International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines, Publisher: Springer, Pages: 129-140Most state-of-the-art bipedal robots are designed to be anthropomorphic, and therefore possess articulated legs with knees. Whilstthis facilitates smoother, human-like locomotion, there are implementation issues that make walking with straight legs difficult. Many robotshave to move with a constant bend in the legs to avoid a singularityoccurring at the knee joints. The actuators must constantly work tomaintain this stance, which can result in the negation of energy-savingtechniques employed. Furthermore, vertical compliance disappears whenthe leg is straight and the robot undergoes high-energy loss events such asimpacts from running and jumping, as the impact force travels throughthe fully extended joints to the hips. In this paper, we attempt to improve energy efficiency in a simple yet effective way: attaching bungeecords as elastic elements in parallel to the legs of a novel, knee-less bipedrobot SLIDER, and show that the robot’s prismatic hip joints preservevertical compliance despite the legs being constantly straight. Due tothe nonlinear dynamics of the bungee cords and various sources of friction, Bayesian Optimization is utilized to find the optimals configurationof bungee cords that achieves the largest reduction in energy consumption. The optimal solution found saves 15% of the energy consumptioncompared to the robot configuration without parallel elastic elements.Additional Video: https://youtu.be/ZTaG9−Dz8A
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Journal articleAlbini E, Baroni P, Rago A, et al., 2021,
Interpreting and explaining pagerank through argumentation semantics
, Intelligenza Artificiale, Vol: 15, Pages: 17-34, ISSN: 1724-8035In this paper we show how re-interpreting PageRank as an argumentation semantics for a bipolar argumentation framework empowers its explainability. After showing that PageRank, naively re-interpreted as an argumentation semantics for support frameworks, fails to satisfy some generally desirable properties, we propose a novel approach able to reconstruct PageRank as a gradual semantics of a suitably defined bipolar argumentation framework, while satisfying these properties. We then show how the theoretical advantages afforded by this approach also enjoy an enhanced explanatory power: we propose several types of argument-based explanations for PageRank, each of which focuses on different aspects of the algorithm and uncovers information useful for the comprehension of its results.
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ReportPaulino-Passos G, Toni F, 2021,
Monotonicity and Noise-Tolerance in Case-Based Reasoning with Abstract Argumentation (with Appendix)
Recently, abstract argumentation-based models of case-based reasoning($AA{\text -} CBR$ in short) have been proposed, originally inspired by thelegal domain, but also applicable as classifiers in different scenarios.However, the formal properties of $AA{\text -} CBR$ as a reasoning systemremain largely unexplored. In this paper, we focus on analysing thenon-monotonicity properties of a regular version of $AA{\text -} CBR$ (that wecall $AA{\text -} CBR_{\succeq}$). Specifically, we prove that $AA{\text -}CBR_{\succeq}$ is not cautiously monotonic, a property frequently considereddesirable in the literature. We then define a variation of $AA{\text -}CBR_{\succeq}$ which is cautiously monotonic. Further, we prove that suchvariation is equivalent to using $AA{\text -} CBR_{\succeq}$ with a restrictedcasebase consisting of all "surprising" and "sufficient" cases in the originalcasebase. As a by-product, we prove that this variation of $AA{\text -}CBR_{\succeq}$ is cumulative, rationally monotonic, and empowers a principledtreatment of noise in "incoherent" casebases. Finally, we illustrate $AA{\text-} CBR$ and cautious monotonicity questions on a case study on the U.S. TradeSecrets domain, a legal casebase.
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Journal articleCabral C, Curtis K, Curcin V, et al., 2021,
Challenges to implementing electronic trial data collection in primary care: a qualitative study
, BMC Family Practice, Vol: 22, ISSN: 1471-2296BackgroundWithin-consultation recruitment to primary care trials is challenging. Ensuring procedures are efficient and self-explanatory is the key to optimising recruitment. Trial recruitment software that integrates with the electronic health record to support and partially automate procedures is becoming more common. If it works well, such software can support greater participation and more efficient trial designs. An innovative electronic trial recruitment and outcomes software was designed to support recruitment to the Runny Ear randomised controlled trial, comparing topical, oral and delayed antibiotic treatment for acute otitis media with discharge in children. A qualitative evaluation investigated the views and experiences of primary care staff using this trial software.MethodsStaff were purposively sampled in relation to site, role and whether the practice successfully recruited patients. In-depth interviews were conducted using a flexible topic guide, audio recorded and transcribed. Data were analysed thematically.ResultsSixteen staff were interviewed, including GPs, practice managers, information technology (IT) leads and research staff. GPs wanted trial software that automatically captures patient data. However, the experience of getting the software to work within the limited and complex IT infrastructure of primary care was frustrating and time consuming. Installation was reliant on practice level IT expertise, which varied between practices. Although most had external IT support, this rarely included supported for research IT. Arrangements for approving new software varied across practices and often, but not always, required authorisation from Clinical Commissioning Groups.ConclusionsPrimary care IT systems are not solely under the control of individual practices or CCGs or the National Health Service. Rather they are part of a complex system that spans all three and is influenced by semi-autonomous stakeholders operating at different levels. This led
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Journal articleMersmann S, Stromich L, Song F, et al., 2021,
ProteinLens: a web-based application for the analysis of allosteric signalling on atomistic graphs of biomolecules
, Nucleic Acids Research, Vol: 49, Pages: W551-W558, ISSN: 0305-1048The investigation of allosteric effects in biomolecular structures is of great current interest in diverse areas, from fundamental biological enquiry to drug discovery. Here we present ProteinLens, a user-friendly and interactive web application for the investigation of allosteric signalling based on atomistic graph-theoretical methods. Starting from the PDB file of a biomolecule (or a biomolecular complex) ProteinLens obtains an atomistic, energy-weighted graph description of the structure of the biomolecule, and subsequently provides a systematic analysis of allosteric signalling and communication across the structure using two computationally efficient methods: Markov Transients and bond-to-bond propensities. ProteinLens scores and ranks every bond and residue according to the speed and magnitude of the propagation of fluctuations emanating from any site of choice (e.g. the active site). The results are presented through statistical quantile scores visualised with interactive plots and adjustable 3D structure viewers, which can also be downloaded. ProteinLens thus allows the investigation of signalling in biomolecular structures of interest to aid the detection of allosteric sites and pathways. ProteinLens is implemented in Python/SQL and freely available to use at: www.proteinlens.io.
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Journal articleRago A, Cocarascu O, Bechlivanidis C, et al., 2021,
Argumentative explanations for interactive recommendations
, Artificial Intelligence, Vol: 296, Pages: 1-22, ISSN: 0004-3702A significant challenge for recommender systems (RSs), and in fact for AI systems in general, is the systematic definition of explanations for outputs in such a way that both the explanations and the systems themselves are able to adapt to their human users' needs. In this paper we propose an RS hosting a vast repertoire of explanations, which are customisable to users in their content and format, and thus able to adapt to users' explanatory requirements, while being reasonably effective (proven empirically). Our RS is built on a graphical chassis, allowing the extraction of argumentation scaffolding, from which diverse and varied argumentative explanations for recommendations can be obtained. These recommendations are interactive because they can be questioned by users and they support adaptive feedback mechanisms designed to allow the RS to self-improve (proven theoretically). Finally, we undertake user studies in which we vary the characteristics of the argumentative explanations, showing users' general preferences for more information, but also that their tastes are diverse, thus highlighting the need for our adaptable RS.
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Conference paperLaumann F, von Kuegelgen J, Barahona M, 2021,
Kernel two-sample and independence tests for non-stationary random processes
, ITISE 2021 (7th International conference on Time Series and Forecasting), Publisher: https://www.mdpi.com/2673-4591/5/1/31, Pages: 1-13Two-sample and independence tests with the kernel-based MMD and HSIC haveshown remarkable results on i.i.d. data and stationary random processes.However, these statistics are not directly applicable to non-stationary randomprocesses, a prevalent form of data in many scientific disciplines. In thiswork, we extend the application of MMD and HSIC to non-stationary settings byassuming access to independent realisations of the underlying random process.These realisations - in the form of non-stationary time-series measured on thesame temporal grid - can then be viewed as i.i.d. samples from a multivariateprobability distribution, to which MMD and HSIC can be applied. We further showhow to choose suitable kernels over these high-dimensional spaces by maximisingthe estimated test power with respect to the kernel hyper-parameters. Inexperiments on synthetic data, we demonstrate superior performance of ourproposed approaches in terms of test power when compared to currentstate-of-the-art functional or multivariate two-sample and independence tests.Finally, we employ our methods on a real socio-economic dataset as an exampleapplication.
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Conference paperCully A, 2021,
Multi-Emitter MAP-Elites: Improving quality, diversity and convergence speed with heterogeneous sets of emitters
, Genetic and Evolutionary Computation Conference (GECCO), Publisher: ACM, Pages: 84-92Quality-Diversity (QD) optimisation is a new family of learning algorithmsthat aims at generating collections of diverse and high-performing solutions.Among those algorithms, MAP-Elites is a simple yet powerful approach that hasshown promising results in numerous applications. In this paper, we introduce anovel algorithm named Multi-Emitter MAP-Elites (ME-MAP-Elites) that improvesthe quality, diversity and convergence speed of MAP-Elites. It is based on therecently introduced concept of emitters, which are used to drive thealgorithm's exploration according to predefined heuristics. ME-MAP-Elitesleverages the diversity of a heterogeneous set of emitters, in which eachemitter type is designed to improve differently the optimisation process.Moreover, a bandit algorithm is used to dynamically find the best emitter setdepending on the current situation. We evaluate the performance ofME-MAP-Elites on six tasks, ranging from standard optimisation problems (in 100dimensions) to complex locomotion tasks in robotics. Our comparisons againstMAP-Elites and existing approaches using emitters show that ME-MAP-Elites isfaster at providing collections of solutions that are significantly morediverse and higher performing. Moreover, in the rare cases where no fruitfulsynergy can be found between the different emitters, ME-MAP-Elites isequivalent to the best of the compared algorithms.
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Conference paperRakicevic N, Cully A, Kormushev P, 2021,
Policy manifold search: exploring the manifold hypothesis for diversity-based neuroevolution
, Genetic and Evolutionary Computation Conference (GECCO '21), Pages: 901-909Neuroevolution is an alternative to gradient-based optimisation that has the potential to avoid local minima and allows parallelisation. The main limiting factor is that usually it does not scale well with parameter space dimensionality. Inspired by recent work examining neural network intrinsic dimension and loss landscapes, we hypothesise that there exists a low-dimensional manifold, embedded in the policy network parameter space, around which a high-density of diverse and useful policies are located. This paper proposes a novel method for diversity-based policy search via Neuroevolution, that leverages learned representations of the policy network parameters, by performing policy search in this learned representation space. Our method relies on the Quality-Diversity (QD) framework which provides a principled approach to policy search, and maintains a collection of diverse policies, used as a dataset for learning policy representations. Further, we use the Jacobian of the inverse-mapping function to guide the search in the representation space. This ensures that the generated samples remain in the high-density regions, after mapping back to the original space. Finally, we evaluate our contributions on four continuous-control tasks in simulated environments, and compare to diversity-based baselines.
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Journal articleSaputra RP, Rakicevic N, Kuder I, et al., 2021,
ResQbot 2.0: an improved design of a mobile rescue robot with an inflatable neck securing device for safe casualty extraction
, Applied Sciences, Vol: 11, Pages: 1-18, ISSN: 2076-3417Despite the fact that a large number of research studies have been conducted in the field of searchand rescue robotics, significantly little attention has been given to the development of rescue robotscapable of performing physical rescue interventions, including loading and transporting victims toa safe zone—i.e. casualty extraction tasks. The aim of this study is to develop a mobile rescue robotthat could assist first responders when saving casualties from a danger area by performing a casualty extraction procedure, whilst ensuring that no additional injury is caused by the operation andno additional lives are put at risk. In this paper, we present a novel design of ResQbot 2.0—a mobilerescue robot designed for performing the casualty extraction task. This robot is a stretcher-type casualty extraction robot, which is a significantly improved version of the initial proof-of-concept prototype, ResQbot (retrospectively referred to as ResQbot 1.0), that has been developed in our previous work. The proposed designs and development of the mechanical system of ResQbot 2.0, as wellas the method for safely loading a full body casualty onto the robot’s ‘stretcher bed’, are describedin detail based on the conducted literature review, evaluation of our previous work and feedbackprovided by medical professionals. To verify the proposed design and the casualty extraction procedure, we perform simulation experiments in Gazebo physics engine simulator. The simulationresults demonstrate the capability of ResQbot 2.0 to successfully carry out safe casualty extractions
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Journal articleCyras K, Oliveira T, Karamlou M, et al., 2021,
Assumption-based argumentation with preferences and goals for patient-centric reasoning with interacting clinical guidelines
, Argument and Computation, Vol: 12, Pages: 149-189, ISSN: 1946-2166A paramount, yet unresolved issue in personalised medicine is that of automated reasoning with clinical guidelines in multimorbidity settings. This entails enabling machines to use computerised generic clinical guideline recommendations and patient-specific information to yield patient-tailored recommendations where interactions arising due to multimorbidities are resolved. This problem is further complicated by patient management desiderata, in particular the need to account for patient-centric goals as well as preferences of various parties involved. We propose to solve this problem of automated reasoning with interacting guideline recommendations in the context of a given patient by means of computational argumentation. In particular, we advance a structured argumentation formalism ABA+G (short for Assumption-Based Argumentation with Preferences (ABA+) and Goals) for integrating and reasoning with information about recommendations, interactions, patient’s state, preferences and prioritised goals. ABA+G combines assumption-based reasoning with preferences and goal-driven selection among reasoning outcomes. Specifically, we assume defeasible applicability of guideline recommendations with the general goal of patient well-being, resolve interactions (conflicts and otherwise undesirable situations) among recommendations based on the state and preferences of the patient, and employ patient-centered goals to suggest interaction-resolving, goal-importance maximising and preference-adhering recommendations. We use a well-established Transition-based Medical Recommendation model for representing guideline recommendations and identifying interactions thereof, and map the components in question, together with the given patient’s state, prioritised goals, and preferences over actions, to ABA+G for automated reasoning. In this, we follow principles of patient management and establish corresponding theoretical properties as well as illustrate our approach in realis
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Conference paperFrazelle C, Walker I, AlAttar A, et al., 2021,
Kinematic-model-free control for space operations with continuum Manipulators
, USA, IEEE Conference on Aerospace, Publisher: IEEE, Pages: 1-11, ISSN: 1095-323XContinuum robots have strong potential for application in Space environments. However, their modeling is challenging in comparison with traditional rigid-link robots. The Kinematic-Model-Free (KMF) robot control method has been shown to be extremely effective in permitting a rigid-link robot to learn approximations of local kinematics and dynamics (“kinodynamics”) at various points in the robot's task space. These approximations enable the robot to follow various trajectories and even adapt to changes in the robot's kinematic structure. In this paper, we present the adaptation of the KMF method to a three-section, nine degrees-of-freedom continuum manipulator for both planar and spatial task spaces. Using only an external 3D camera, we show that the KMF method allows the continuum robot to converge to various desired set points in the robot's task space, avoiding the complexities inherent in solving this problem using traditional inverse kinematics. The success of the method shows that a continuum robot can “learn” enough information from an external camera to reach and track desired points and trajectories, without needing knowledge of exact shape or position of the robot. We similarly apply the method in a simulated example of a continuum robot performing an inspection task on board the ISS.
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Journal articleAlAttar A, Cursi F, Kormushev P, 2021,
Kinematic-model-free redundancy resolution using multi-point tracking and control for robot manipulation
, Applied Sciences, Vol: 11, Pages: 1-15, ISSN: 2076-3417Abstract: Robots have been predominantly controlled using conventional control methods that require prior knowledge of the robots’ kinematic and dynamic models. These controllers can be challenging to tune and cannot directly adapt to changes in kinematic structure or dynamic properties. On the other hand, model-learning controllers can overcome such challenges.Our recently proposed model-learning orientation controller has shown promising ability to simul6 taneously control a three-degrees-of-freedom robot manipulator’s end-effector pose. However, this controller does not perform optimally with robots of higher degrees-of-freedom nor does it resolve redundancies. The research presented in this paper extends the state-of-the-art kinematic9 model-free controller to perform pose control of hyper-redundant robot manipulators and resolve redundancies by tracking and controlling multiple points along the robot’s serial chain. The results show that with more control points, the controller is able to reach desired poses in fewer steps, yielding an improvement of up to 66%, and capable of achieving complex configurations. The algorithm was validated by running the simulation 100 times and it was found that 82% of the times the robot successfully reached the desired target pose within 150 steps.
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Conference paperDejl A, He P, Mangal P, et al., 2021,
Argflow: a toolkit for deep argumentative explanations for neural networks
, Proc. of the 20th International Conference on Autonomous Agents and Multiagent Systems, Pages: 1761-1763, ISSN: 1558-2914In recent years, machine learning (ML) models have been successfully applied in a variety of real-world applications. However, theyare often complex and incomprehensible to human users. This candecrease trust in their outputs and render their usage in criticalsettings ethically problematic. As a result, several methods for explaining such ML models have been proposed recently, in particularfor black-box models such as deep neural networks (NNs). Nevertheless, these methods predominantly explain outputs in termsof inputs, disregarding the inner workings of the ML model computing those outputs. We present Argflow, a toolkit enabling thegeneration of a variety of ‘deep’ argumentative explanations (DAXs)for outputs of NNs on classification tasks.
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Journal articleMyall AC, Peach RL, Weiße AY, et al., 2021,
Network memory in the movement of hospital patients carrying drug-resistant bacteria
, Applied Network Science, Vol: 6, ISSN: 2364-8228Hospitals constitute highly interconnected systems that bring into contact anabundance of infectious pathogens and susceptible individuals, thus makinginfection outbreaks both common and challenging. In recent years, there hasbeen a sharp incidence of antimicrobial-resistance amongsthealthcare-associated infections, a situation now considered endemic in manycountries. Here we present network-based analyses of a data set capturing themovement of patients harbouring drug-resistant bacteria across three largeLondon hospitals. We show that there are substantial memory effects in themovement of hospital patients colonised with drug-resistant bacteria. Suchmemory effects break first-order Markovian transitive assumptions andsubstantially alter the conclusions from the analysis, specifically on noderankings and the evolution of diffusive processes. We capture variable lengthmemory effects by constructing a lumped-state memory network, which we then useto identify overlapping communities of wards. We find that these communities ofwards display a quasi-hierarchical structure at different levels of granularitywhich is consistent with different aspects of patient flows related to hospitallocations and medical specialties.
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Conference paperTavakoli A, Fatemi M, Kormushev P, 2021,
Learning to represent action values as a hypergraph on the action vertices
, Vienna, Austria, International Conference on Learning RepresentationsAction-value estimation is a critical component of many reinforcement learning(RL) methods whereby sample complexity relies heavily on how fast a good estimator for action value can be learned. By viewing this problem through the lens ofrepresentation learning, good representations of both state and action can facilitateaction-value estimation. While advances in deep learning have seamlessly drivenprogress in learning state representations, given the specificity of the notion ofagency to RL, little attention has been paid to learning action representations. Weconjecture that leveraging the combinatorial structure of multi-dimensional actionspaces is a key ingredient for learning good representations of action. To test this,we set forth the action hypergraph networks framework—a class of functions forlearning action representations in multi-dimensional discrete action spaces with astructural inductive bias. Using this framework we realise an agent class basedon a combination with deep Q-networks, which we dub hypergraph Q-networks.We show the effectiveness of our approach on a myriad of domains: illustrativeprediction problems under minimal confounding effects, Atari 2600 games, anddiscretised physical control benchmarks.
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Journal articleEspinosa-Gonzalez AB, Neves AL, Fiorentino F, et al., 2021,
Predicting Risk of Hospital Admission in Patients With Suspected COVID-19 in a Community Setting: Protocol for Development and Validation of a Multivariate Risk Prediction Tool
, JMIR RESEARCH PROTOCOLS, Vol: 10, ISSN: 1929-0748- Author Web Link
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- Citations: 8
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Journal articlePeach RL, Arnaudon A, Schmidt JA, et al., 2021,
HCGA: Highly comparative graph analysis for network phenotyping
, Patterns, Vol: 2, Pages: 100227-100227, ISSN: 2666-3899<jats:title>A<jats:sc>bstract</jats:sc></jats:title><jats:p>Networks are widely used as mathematical models of complex systems across many scientific disciplines, not only in biology and medicine but also in the social sciences, physics, computing and engineering. Decades of work have produced a vast corpus of research characterising the topological, combinatorial, statistical and spectral properties of graphs. Each graph property can be thought of as a feature that captures important (and some times overlapping) characteristics of a network. In the analysis of real-world graphs, it is crucial to integrate systematically a large number of diverse graph features in order to characterise and classify networks, as well as to aid network-based scientific discovery. In this paper, we introduce HCGA, a framework for highly comparative analysis of graph data sets that computes several thousands of graph features from any given network. HCGA also offers a suite of statistical learning and data analysis tools for automated identification and selection of important and interpretable features underpinning the characterisation of graph data sets. We show that HCGA outperforms other methodologies on supervised classification tasks on benchmark data sets whilst retaining the interpretability of network features. We also illustrate how HCGA can be used for network-based discovery through two examples where data is naturally represented as graphs: the clustering of a data set of images of neuronal morphologies, and a regression problem to predict charge transfer in organic semiconductors based on their structure. HCGA is an open platform that can be expanded to include further graph properties and statistical learning tools to allow researchers to leverage the wide breadth of graph-theoretical research to quantitatively analyse and draw insights from network data.</jats:p>
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Journal articleTajnafoi G, Arcucci R, Mottet L, et al., 2021,
Variational Gaussian process for optimal sensor placement
, Applications of Mathematics, Vol: 66, Pages: 287-317, ISSN: 0373-6725Sensor placement is an optimisation problem that has recently gained great relevance. In order to achieve accurate online updates of a predictive model, sensors are used to provide observations. When sensor location is optimally selected, the predictive model can greatly reduce its internal errors. A greedy-selection algorithm is used for locating these optimal spatial locations from a numerical embedded space. A novel architecture for solving this big data problem is proposed, relying on a variational Gaussian process. The generalisation of the model is further improved via the preconditioning of its inputs: Masked Autoregressive Flows are implemented to learn nonlinear, invertible transformations of the conditionally modelled spatial features. Finally, a global optimisation strategy extending the Mutual Information-based optimisation and fine-tuning of the selected optimal location is proposed. The methodology is parallelised to speed up the computational time, making these tools very fast despite the high complexity associated with both spatial modelling and placement tasks. The model is applied to a real three-dimensional test case considering a room within the Clarence Centre building located in Elephant and Castle, London, UK.
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Journal articleSivan M, Rayner C, Delaney B, 2021,
Fresh evidence of the scale and scope of long covid
, BMJ-BRITISH MEDICAL JOURNAL, Vol: 373, ISSN: 0959-535X- Author Web Link
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- Citations: 34
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