Chairman’s remarks and recap of Day 1
Keynote Interview – Healthcare & Data: Implementing the NHS patient “data lake”
- How do you engage people? What innovative approaches can be used to engage the public? Social media? Lessons learnt from social services and education outreach programmes.
- How do diverse communities feel about sharing their data?
- What is realistic in terms of patients’ knowledge of how their medical data is being used in research?
- Discussions around ‘consent’ or ‘opt-out’
- Ethical, privacy protection and governance requirements for electronic health record architectures and systems
- What challenges to this privacy protection does research use pose?
- How can clinical research make use of interoperable electronic health records in a trustworthy way?
- Gain insight into the work being done to create a standard computational method for HPC at a time when genomics data is growing
- What hurdles were met during development?
- What opportunities does this standard present to the biodata sector?
- What translational bioinformatics is in an academic and multidisciplinary environment within the field of oncology
- What are some of the strategies, issues and bottlenecks encountered during recent studies?
- Review a platform developed for NGS automated data management
- Gain an understanding of how biodata is assisting epidemiology studies within drug development and equally drug failures
- What are some of the challenges around availability of data when looking at real world evidence and disease outcomes?
- How have population centric phenotypic data helped within this approach?
- Review challenges faced by pharma companies in data privacy
- How do you continue to push scientific development and innovation whilst maintaining adequate data protection measures?
- How does data privacy sit with the pharma business strategy as a whole?
- Gain an understanding of how medical research is seeing rapid transformation driven by an explosion in data and advances in AI
- How is BenevolentAI’s approach, of identifying and developing novel products and novel uses of existing products, taking advantage of existing data sets, and the need to access and re-evaluate pharmaceutical databases?
- What are the main benefits of this AI-based approach to a drug discovery pipeline: time, attrition, financial, and for patients?
- Is big data an opportunity or challenge for research into brain tumours?
- How can those affected by brain tumours lead us on use of their data?
- How can biobanking help advance developments within this space?
- What can you learn from linking population-level EHRs and genetic data?
- How can you use other technologies to better understand a disease, such as wearables and other data systems, to track and manage people over time?
- How do you overcome the challenge of finding the longitudinal phenotypic data required for these studies?
- Gain a better understanding of the challenges around target identification and validation
- Review a semi-supervised classification approach to explore whether gene – disease association data is sufficient to predict therapeutic targets
- Investigate how a neural network is able to predict therapeutic targets with over 70% accuracy demonstrating that disease association is predictive of the ability of a gene or a protein to work as a drug target
- Hear an overview of the biocuration-based activities being undertaken by the SIB Swiss Institute of Bioinformatics in the field of cancer
- Best practices for transforming big data into smart data within this research and development field
- Gain insights into biota analytics for drug repurposing in immunotherapy
- Gain an understanding of advancements in software development and data analysis within the field of microbial bioinformatics
- How is biodata specifically working towards combatting anti-microbial resistance?
- What new methods and technologies are being developed within the microbial bioinformatics space?
- Best practices in modelling to incorporate different data sets
- Hear a case study of this application within diabetes
- Examine how to create an Integrated Data
- Insights into big data analytics on the causation of a rare childhood cancer
- How is this enabling drug discovery for this particular patient cohort?
- Investigate methods used to identify drug responses from each patient within that same cohort
- How do we work with hospitals to embed genomic testing in clinical pathways where appropriate?
- What tools are needed by healthcare to adopt?
- What is the clinical impact of this change, including impact on cost and value?
- Hear how bioinformatics tools are being used for pathogen identification and typing, as well as pathogenicity and virulence within infectious diseases
- How is this research helping to identify and combat antimicrobial resistance?
- Gain an understanding of applications for bioinformatics within infectious diseases: drug resistance testing, pathogen-host interaction, infection and treatment outcomes
- How do you make information architecture a priority in corporate environments and deliver immediate impact?
- Insights into a structured approach to overcome the core challenges of large enterprises to build key data assets
- Best practices in applying this to a pharmaceutical R&D business
- Gain insights into machine learning as applied to the intelligent mining of clinical trials data and examine predictive analytics to refine the operational execution of drug development
- What are some of the challenges & opportunities presented by the burgeoning role of technology in clinical research?
- Consider the future: How AI might be used in years to come?
- Explore how this project unites health data and technology to improve health services in Northern England
- Review how applications of advanced data analytics have provided more efficient processes and improved public health to patients
- What are some of the lessons learnt that can be applied elsewhere in the UK and the rest of the world?
- Learn about how the Data for Good Foundation is collecting patient data for research on top of personal data storage
- What is the dynamic around data ownership and governance?
- How are the foundation interacting with both health and behaviour data?
- Lessons learnt from the pharmaceutical industry
- Focus on data and metadata management
- Gain an understanding of what’s next for bioinformatics R&D
- Hear how machine learning techniques are being applied to the automatic interpretation of medical imaging
- What are the benefits of using these techniques within the radiology setting?
- Where else could these algorithms be applied?
- Hear an overview of how AI is helping review patient records and medical literature to generate a series of ranked diagnoses
- What have been some of the lessons learnt and challenges overcome?
- Does working with rare diseases alter data protocols and practices at all?
- Explore how population-based studies are enabling strategies for prevention
- Investigate methodologies that can be applied to biobanks and population-based cohorts when studying disease prevention
- Best practices in overcoming challenges with global coverage and international collaborations
- Update into Roche’s data management and workflow management strategy
- What have been some of the challenges rolling it out?
- How has been accepted by the scientists using the platform?
- Gain an understanding of how WuXi NextCODE’s robust AI method is helping advance personalised medicine
- How do their various advanced deepCODE AI tools expand the understanding of the underlying molecular determinants of human cancer?
- What are these support systems and research tools enabling for the research communities working in this space?
- Where are we going with biodata? What is the sense of direction?
- What will be the dynamic between biodata and clinical data?
- What role will we see alternative sources of data (such as behavioural or environmental data) and new technologies (such as nanotechnology and wearable) play in their interaction with biodata?
- What is the role of AI and machine learning in all of this?
- Forward looking perspectives on population-based health studies within biodata. What will the interface be between real world data/ real world evidence and genomics? Will this help to better inform precision medicine moving forward?
last published: 15/Jun/17 16:25 GMT