NIHRDataInsights/HRCSData huggingface dataset:
Title: Application of deep learning to heterogeneous open data for measuring urban environment and health
Funder: Medical Research Council

Abstract: Recent advances in deep learning methods have achieved unprecedented improvements in accuracy on multiple visual tasks such as object recognition and classification. While previously unused large-scale data is gaining importance in public health research, recent technical developments mostly focused on advanced spatial machine learning and statistics methods while integration of deep learning techniques have been largely unexplored. The overall goal of my fellowship research is to leverage these advanced methods to answer important questions in environment and health research. Specifically, convolutional neural networks will be trained using satellite and street view imagery to extract outcomes of health and its environmental/social determinants. Transfer learning and class specific saliency maps will be used for post-hoc model visualisation. A combined analysis of models trained for multiple outcomes will be conducted to study overlaps and deviations. Secondly, joint use of mobile phone and image data as predictors will be explored for improving model performance. New methods of data integration will be developed to enable the use of multiple big data sources as predictors in a unified modelling framework. Finally, transferability of deep learning models trained on data from one city to other geographies will be evaluated; adaptation techniques to facilitate transferability will be explored.


Research activity codes: 8.5 RESOURCES AND INFRASTRUCTURE (HEALTH SERVICES)
Health categories: GENERIC HEALTH RELEVANCE
Title: Semantic-based secure infrastructure for interoperable, in-depth biomedical data and analytics
Funder: Medical Research Council

Abstract: WP1: Harmonized data framework WP1 will research the development of an integrated health-data framework based on syntactic and semantic interoperability. We will investigate how current data held in various resources (EHR, LIMS) can be represented for different content models based on existing ontologies and standards. We will then employ the semantically characterised data to develop and apply symbolic AI techniques that will facilitate the creation and manipulation of comprehensible conceptualisations of information, allowing us not only to make decisions, but to truly learn from data. WP2: Computer-usable information extraction from text Current electronic healthcare records commonly contain poorly defined information in textual form. Based on our proposed framework, we will develop methodologies using advances AI techniques (Word2Vec, LSTMs) and apply them to characterise textual data and transmute them into structured formats. Initially we intend to apply our approach to a few chosen domains and then scale it up. WP3: Analysis of complex multi-dimensional images Similarly, multi-dimensional data cannot easily be used for computer-based analytical systems. To harvest the potential information hidden in unstructured data, we will develop approaches to employ modern AI techniques (CNNs, GANs) for knowledge extraction. We plan to further combine this feature learning with defined vocabularies to directly extract meaningful annotations. WP4: Anonymization approaches for distributed analysis AI techniques rely on large datasets with millions of examples. However, this creates challenges with respect to sharing data whilst respecting the wishes of patients and meeting the GDPR requirements. To address this, we will develop anonymization approaches able to learn distributions from data. These distributions can be employed to sample novel, artificial data, which can be shared without disclosing any real data and be used to boost model induction.


Research activity codes: 8.5 RESOURCES AND INFRASTRUCTURE (HEALTH SERVICES)
Health categories: GENERIC HEALTH RELEVANCE
Title: Social listening: Applying natural language processing methods to social media data to yield actionable analytics for health and care services
Funder: Medical Research Council

Abstract: Future healthcare delivery and research relies on public acceptance of information technology and health data uses as part of service delivery. Social media platforms (e.g. Twitter), blogs and online discussion forums provide an increasingly ubiquitous, yet under exploited, source of unstructured textual data for examining public attitudes and preferences relevant to health, information technology, data uses and healthcare delivery. Yet, analysing such data manually is labour intensive and can neither be done rapidly nor at scale. This project will investigate the accuracy of newer natural language processing (NLP) techniques for the rapid, automated extraction of public attitudes and preferences from large-scale, social media data. Exemplar datasets relevant to public attitudes towards the commercial use of health data and wearable technologies will be extracted from social media data, cleaned and prepared for analysis. NLP techniques (e.g. sentiment analysis, text mining, machine learning and/or rule-based methods) and qualitative analysis (e.g. framework analysis, discursive psychology) will then be: (a) applied to unstructured textual data in parallel; (b) benchmarked against each other; and (c) tested within an integrated mixed methods approach. Online open source decision support tools will be developed to guide the selection and application of NLP techniques (alone and/or as part of a mixed methods approach) to unstructured social media data, addressing distinct purposes, such as longitudinal monitoring of public opinion and informing policy development. Advanced data visualisations will be developed, evaluated and optimised for data exploration, presentation and informing decision making. This will include sentiment analysis, clustering, frequency analysis, and high-dimensional representations of big text data. Findings, tools and methods will be disseminated widely, with the aim of enabling public opinion data to inform future policy development.


Research activity codes: 4.3 INFLUENCES AND IMPACT;7.1 INDIVIDUAL CARE NEEDS;8.4 RESEARCH DESIGN AND METHODOLOGIES (HEALTH SERVICES)
Health categories: GENERIC HEALTH RELEVANCE
Title: Artificial Intelligence in Medical Treatment Decisions and Diagnosis
Funder: Medical Research Council

Abstract: The data set is made of anonymised EHRs from the Great Osmond Street Hospital. Over time, we look to engage the NIHR Health Informatics Collaborative and other hospitals that are part of HDR London and HDR UK to collaboratively expand the data set. In the diagnosis network, we exploit a deep recurrent neural network architecture. We model EHR data using multi-task Gaussian processes (MGP) - a probabilistic model used to handle missing values and irregularly spaced observation times - and employ Long-Short-Term Memory (LSTM) units to capture long-range dependencies and nonlinear dynamics. We implement a multi-level deeply supervised network by introducing companion objective functions for each hidden layer that we will combine with the final loss value. We will also explore the effect of attention mechanisms on performance. We will compare the network performance with several state-of-the-art models and consider timeliness along with other metrics that quantify discrimination and accuracy. In the network where the goal is to learn optimal treatment regimens, we exploit a deep reinforcement learning architecture. We adopt an MGP-LSTM module to impute and interpolate the discretised EHR inputs, and employ a duelling Q-network architecture to learn the Q-values. The expected loss and gradient in the MGP layer will be approximated using a fixed number of Monte Carlo sampling. A different action-reward training environment will be structured for each disease explored. To speed up learning, we will explore prioritised experience. We will compare the network and physician performance using the estimated expected total Q-values.


Research activity codes: 4.1 DISCOVERY AND PRECLINICAL TESTING OF MARKERS AND TECHNOLOGIES;7.3 MANAGEMENT AND DECISION MAKING
Health categories: GENERIC HEALTH RELEVANCE
Title: Deriving an actionable patient phenome from healthcare data
Funder: Medical Research Council

Abstract: For objective 1, at the data layer, my research will focus on a semantic phenome model that is able to detect/correct erroneous and inconsistent phenotypes, associate accurate contextual and temporal information with each phenotype mention and also support rule based reasoning to complete missing data. For objective 2, I will be devising and applying artificial intelligence models to derive unknown clinical knowledge from large scale, longitudinal and interlinked phenome data. potential use cases include predicting outcomes of septic shock treatments within intensive care units; predicting unknown adverse drug reactions in depression patients with comorbidities; subtyping atrial fibrillation to deliver tailored care. For objective 3, my research will provide actionable suggestions in clinical settings with applications of clinical trial recruitment and automated alerting for ensuring patient safety. Key challenges to be tackled here include how to make action suggestions explainable and reliable. This project aims to deliver enabling technologies for The University of Edinburgh's HDR UK focus including deriving and applying health-related phenotypes at scale; computational tools for genetic and environmental risk prediction and causal inference. It will develop national leadership, partnerships, and interdisciplinary skills and capacity through the development of semantic computation infrastructure on top of deep and accurate patient phenome data, which if successful, can be disseminated to a wide range of healthcare service providers nationally/internationally and achieve high impact in research and patient care.


Research activity codes: 4.5 RESOURCES AND INFRASTRUCTURE (DETECTION)
Health categories: GENERIC HEALTH RELEVANCE
Title: Holistic Approach To Unravel Antibacterial resistance in East Africa (HATUA)
Funder: Medical Research Council

Abstract: Researchers investigating antibacterial resistance (ABR) where there is a paucity of information are faced with challenges of data collection, linkage and resolution. We have designed our experimental workflows to utilize techniques and methods that will provide high-resolution and accurate data. A crucial element of our project is the quantification and characterisation of the clinical burden of ABR. Working with microbiology labs that form the consortium's network, we will standardize Antibiotic Sensitivity Testing (AST) methodologies across labs, so that it meets EUCAST/CLSI guidelines, generating robust and comparable data. We will implement WGS within ABR within the surveillance network, and develop bioinformatics to enable local and international comparison of data for key indicator ABR organisms. We will deploy an intuitive user-friendly bioinformatics web application (microreact.net) to enable network laboratories to view WGS data contextually (i.e. in a geographic and phylogenetic context, with accompanying meta-data). In order to gather antibiotic usage data across multiple sites we will equip researchers with mobile devices running EpiCollect software, to record quantitate and qualitative antibiotic data gathered from interviews. EpiCollect allows the generation of custom questionnaires, and uploading of data in the field, to a remote server which displays on a project website that can be used for data analysis and visualisation. The software also records geospatial information to enable geographically mapping of data. Simulated scenarios and in-depth interviews will be conducted to examine attitudes and behaviours around antibiotic prescription, administration and adherence. EpiCollect will also be utilized for this work to promote data linkage. Integrated mathematical and statistical analyses will be undertaken on our multi-disciplinary datasets to help construct and parameterise mechanistic models of the genesis and spread of ABR in East Africa.


Research activity codes: 2.5 RESEARCH DESIGN AND METHODOLOGIES (AETIOLOGY);2.6 RESOURCES AND INFRASTRUCTURE (AETIOLOGY)
Health categories: GENERIC HEALTH RELEVANCE
Title: Drivers of Resistance in Uganda and Malawi: The DRUM Consortium
Funder: Medical Research Council

Abstract: The Drivers of Resistance in Uganda and Malawi, or DRUM Consortium aims to build an agent based model of the drivers of antimicrobial resistance transmission, using data from urban, peri-urban and rural settings in Uganda and Malawi to inform the model. We will undertake five packets of work to build the model; 1. Longitudinally collect microbiological data from humans, animals and the environment of households at two levels of sampling intensity and genomically characterise the bacteria isolated; 2. Produce data on the intensity of antibiotic use across human and agricultural domains; the reasons for antibiotic use - and non-use - in different contexts; and drivers that are amenable to change; 3. As these are enteric pathogens, transmitted via the faecal oral route, we will provide a comprehensive outline of WASH behaviours and associated faecal exposure; 4. Determine the evolutionary trajectories of AMR development related to local antibiotic usage to understand if local usage and exposure patterns of antibiotics drive resistance emergence and persistence; 5. Quantify the economic burden from AMR and assess the value for money of potential interventions to tackle it. At the same time, we will describe the barriers that exist in the AMR policy community and how these can be addressed in through implementing AMR strategic/action plans. The resulting information will be fed back to the policy community. We will use the relationships we develop in this workstream to develop and, in the future, trial interventions based on the output of the model.


Research activity codes: 8.3 POLICY, ETHICS AND RESEARCH GOVERNANCE
Health categories: GENERIC HEALTH RELEVANCE
Title: Supporting the National Action Plan for Antimicrobial Resistance (SNAP-AMR) in Tanzania
Funder: Medical Research Council

Abstract: We aim to provide novel insights into biological, social and cultural drivers of AMR within and out with hospitals at individual (inherent), micro- (community), meso- (institutional) and macro- (policy) level to prioritise use of limited human and financial resources in targeting evidence-based levers of behavioural change that will reduce the risk and burden of AMR. Socio-anthropological methods to elicit information on knowledge, attitudes, practices and access to antimicrobials across tiers of the hospital system and distinct livestock keeping communities include focus group discussions, surveys, questionnaires, in depth interviews and observations to foster stakeholder discussion, elicit community views, provide quantitative data, seek detailed information on key knowledges, and cross check responses. This will provide a rich baseline understanding to inform the design of stated choice experiments, which will complement a micro-costings approach to generate evidence on (non-)pecuniary incentives to motivate intentions for behaviour change in antimicrobial use. We will examine genomic data from hospital, community and livestock associated Escherichia coli using phylogenetic and diversity analysis and source attribution methods to explore the contribution of within- and between hospital transmission and human or animal associated AMR in the community to clinical burden. This evidence will enable us to prioritise levers of change in hospitals and livestock-keeping communities based on predicted impact and cost-benefit. Using a bespoke theoretically informed approach we will co-construct, design, deliver and evaluate an AMR awareness campaign at hospital and community level to learn how best to communicate in a culturally appropriate context and manner. By involving professionals, the public and policy makers these campaigns will also contribute to capacity strengthening across all layers of society in Tanzania.


Research activity codes: 2.1 BIOLOGICAL AND ENDOGENOUS FACTORS;8.3 POLICY, ETHICS AND RESEARCH GOVERNANCE
Health categories: GENERIC HEALTH RELEVANCE
Title: Quantitative and Real-Time Image Analysis for Advanced Light Microscopy.
Funder: Medical Research Council

Abstract: In terms of microscopy analysis, great inroads have been made in terms of impartial and systematic analysis but little has been done to ensure that cells under the microscope are selected impartially. For this project DW will develop algorithms that can statistically quantify and describe cellular appearances utilizing the latest machine learning, computer vision (CV) and signal processing techniques and technologies. DW has in ongoing work investigated the use of CV algorithms in microscopy and has shown very promising results can be achieved by utilizing object detection convolutional neural networks for cellular detection. DW will extend on his use of neural networks for localizing cells (objectives 1-2) and will also develop methods to statistically describe cellular appearance using networks derived from auto-encoders (objective 3), a type of compression network. Neural networks are implemented in several ways, a popular method is to use Tensorflow and DW is an expert in this language. To effectively train neural networks, powerful GPUs are required. Fortunately the WIMM has various computational facilities and DW has access to two powerful GPU equipped servers. For developing CV and real-time analysis approaches to work with camera and detector hardware, DW will work toward developing algorithms that can be embedded in miniaturized electronics (objective 4). The Nvidia Jetson TX2 Developer kit is a resource which allows you to create and develop neural networks and distribute them onto small hardware boards. DW will develop and test this hardware technique with microscopy based hardware and algorithms. For objectives 5 and 6 GPU code will be systematically produced in CUDA and will be made as compatible and distributable as possible. Oxford University has several microscopy software development projects (e.g. in Micron) and it is intended that the software and libraries produced by this project will be made compatible with these other projects.


Research activity codes: 1.4 METHODOLOGIES AND MEASUREMENTS
Health categories: GENERIC HEALTH RELEVANCE
Title: Engineered microenvironments to harvest stem cell response to viscosity for cartilage repair
Funder: Medical Research Council

Abstract: The tissues that cells inhabit in vivo are dynamic and dissipative environments, where rapid and adaptive processes drive the interactions between cells and extracellular matrices (ECMs). However, most synthetic material systems designed up to now to promote the healing of damaged tissues employ static environments, mechanically designed to mimic solely the elastic properties of the native tissue. The viscous behaviour of the substrate, which is an intrinsic property of physiological cell surroundings, is traditionally ignored. This proposal aims at engineering advanced microenvironments that instead exploit these dissipative interactions to promote cell differentiation and tissue repair, for application in the regeneration of tissues characterised by high viscous properties, such as cartilage. In order to dissect the contributions of the different components of the ECM to its overall viscous properties and their role in driving chondrogenic differentiation, we will engineer materials with controlled mobility and hence viscosity, functionalised with relevant ligand molecules. These material platforms will be based on supported lipid bilayers, supported polymer films and hydrogels. The ligands molecules employed for their functionalisation will include peptide ligands that regulate cell adhesion (e.g. collagen II ligands), cell-cell contacts (e.g. N-cadherin ligands) and intercellular communication through soluble signalling molecules (e.g. growth factors involved in cartilage repair). The behaviour of mesenchymal stem cells on these functional interfaces will allow a paradigm for cell response to dissipative interactions in the context of chondrogenic differentiation to be derived. This will ultimately guide the incorporation of viscosity into the design of bulk hydrogel systems with a dynamic display of ligands identified as fundamental for the promotion of cartilage repair.


Research activity codes: 5.2 CELLULAR AND GENE THERAPIES
Health categories: GENERIC HEALTH RELEVANCE
Title: Novel methods for the integration of high dimensional single cell proteomic and RNA data to understand cell populations in development and disease.
Funder: Medical Research Council

Abstract: A methodology will be developed for the integration of single cell RNA-Sequencing (scRNA-Seq) and CyTOF data using novel machine learning techniques. I will be using an autoencoder, a technique that is widely used in machine learning, to extract the structure of data from populations of cells analysed with both technologies. In more detail, I will be using the python library TensorFlow, which is one of the most commonly used tools for deep learning, to analyse in parallel the scRNA-Seq and CyTOF data and use the data structure from each to disassociate cell heterogeneity from technical variability. In addition, this approach will ensure that the right normalization has been performed and will take into account batch effects arising from the different technologies, and potentially also from the experimental design. It is important to note that cell populations observed in one of the two technologies will also be reported separately to account for biological variation between transcriptomics and proteomics data. This approach is subsequently going to be used 1) to identify cell subpopulations and 2) to study cell fate in differentiation processes. The data will be acquired from collaborations in the WIMM and I will be working closely with the groups that have produced it to ensure accurate interpretation. Finally, I will be creating a visualisation tool to facilitate the interpretation of the results by both computational and bench scientists. I plan to use WebGL, which allows the use of 3D graphics in web pages with increased speed for data heavy applications. This approach will enable the visualisation of millions of cells. This will be implemented using python packages such as Plotly and Vispy, which can help create a WebGL application from python objects.


Research activity codes: 1.1 NORMAL BIOLOGICAL DEVELOPMENT AND FUNCTIONING;1.4 METHODOLOGIES AND MEASUREMENTS
Health categories: GENERIC HEALTH RELEVANCE