ICT in the IT Future of Medicine Project
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ICT in the IT Future of Medicine Project
Babette RegiererDaniel Jameson
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ITFoM Consortium
170 Partners from 34 countries:
21 EC Member States
Associated Countries:Switzerland, Iceland, Israel, Croatia, Turkey, Norway
Other countries:Australia, New Zealand, Canada, USA, Libanon, Korea, Japan
Number of partners in one country
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ITFoM - Project vision
• Assimilation of data about individuals (‘omic, health records).
• Incorporate these data into mathematical models of each individual’s “health”.
• Use these models to make predictions about the health of individuals and, if necessary, courses of treatment best suited to them.
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The Virtual Patient: Integration of various models
Molecules
Tissues Anatomy
Statistics
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Structure of IFoMMedical Platform (Kurt Zatloukal)
Analytical Technologies (Hans Lehrach)
Infrastructure Hard- and Software (Nora Benhabiles/Oskar Mencer)
Data Pipelines (Ewan Birney)
Computational Methodologies (Mark Girolami)
ICT Integration (Hans Westerhoff)
Coordination and Management (Hans Lehrach/Markus Pasterk)
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Challenges for ICT
Acquisition Integration Processing Utilisation
Automation
Scalability
Security
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Scale
• 12 million new cancer cases world wide / year– To address all you would need to sequence and
analyse 1 cancer every 2 seconds, that’s at least two complete sequences, at least one for the tumour and one somatic.
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Scalability
• All technology must be developed with an eye on scalability– What is appropriate now is guaranteed not to be
in 10 years– All data formats, standards and paradigms must
be flexible and extensible
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Security
• ITFoM aware that a huge amount of the data involved in the proposal was sensitive– Proposal to develop a robust, federated security
framework and policies.– Mindful of the location of data objects – certain
objects must remain within the EU.– Identity Management to build on the experience
of a variety of partners (EUDAT, UCL, EBI, IBM).
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Acquisition: Data gathered
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Acquisition: Data gathered
• For data generation we need to consider:- heterologous data produced (molecules, physiology, patient, society…?)- various technologies for data generation- different user groups (skilled vs. naïve)- different data management systems- different professional level
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Acquisition: ICT to facilitate• Easy user-oriented process from machine to knowledge:
- data analysis pipelines must be easy to handle and fast (e.g. flow computing)
- fast data transfer systems- “online” data generation in the future? - development of automated processes- standards for data formats and processes- Suitable data management systems, data storage (local or
distributed, security issues)- new database structure needed to speed up data storage,
transfer, use? (e.g. HANA system)- responsibility for data curation - where, when, how, who?
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Integration: Pipelines to models
• Complete genomes provide the framework to pull all biological data together such that each piece says something about biology as a whole
• Biology is too complex for any organisation to have a monopoly of ideas or data
• The more organisations provide data or analysis separately, the harder it becomes for anyone to make use of the results
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Integration: Pipelines to models
• The data being gathered must be marshaled into something useful
• Processing, Storage, Retrieval• It must be stored• It must be annotated• It must be auditable
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Integration: ICT to facilitate
• Federated data warehouse with standardised interfaces– Includes auditing services– Must integrate with security layer
• Processing pipelines feed into the warehouse• Compute tasks handled on HPC platform using already
established middleware (EBI).• Pipelines – several, draw on existing databases for
automation of annotation where possible.• Data specific compression algorithms
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Processing: Simulating models
• Variety of model types
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Processing: Simulating models
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Processing: ICT to facilitate
• New algorithms and techniques.• HPC platforms.• Protocols.• New hardware.• Once size will not fit all, but all must
communicate with each other.
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Utilising: Making use of models
• Closing the loop
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Utilising: Making use of models
• We need to consider:- different target groups- easy access to data/information needed- make them work in the field/on the
bedside- technology must be available at low price (e.g. computing power must be cost-
effective = green technology)
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Utilising: ICT to facilitate
• Aim is an approach that is easy-to-handle, cost-efficient and running on all systems
- automated data analysis/modeling system- elaborated human-computer interface
(visualization)- automated updating of the information (e.g. by
text mining in publications)- must be easy to plug in new systems- legal issues- results instantly
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ICT Components for Genomic MedicineHealthcare Professional
Component 4Individual query
analysis
Component 3Additional clinical
annotation
Component 2Genotype and
Phenotype relationship capture
Component 1Human sequence data
repositories
Component 5Electronic Health Record
Component 6Research on Clinical data
SHIP, GPRD, LSDBs,Research Capability Programme (RCP)
EBI: repositories(petabytes of genome sequence data)Sanger: sequencing (1000 genomes, uk10K)
Reference genome sequence
~3 gigabytes
eHR system (e.g. emis):~10 Mb Variant file as attachment per record
Add genomics:Up to 60 million variant files = 600 terabytes*
Biomedical Informatics Institute (BII)
BII, SMEs etc.cloud based, secure services
Variant file
decision supportsystem
open data
Personal Data
Anonymised Data
Summary Data
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Importance of Automation
• Mentioned frequently in ITFoM.• Pipelining and utilising data on this scale is
impossible if all steps are conducted manually.• This includes processing, annotation,
hypothesis generation and testing.• Text mining, machine learning• No one’s actually cracked this.
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Conclusions• A virtual, or digital, patient has the potential to
revolutionise healthcare, but it will rely completely on the creation of a broad, probably federated, IT infrastructure.
• An infrastructure such as this is non-trivial.• Any project as ambitious as a virtual patient requires
vastly more expertise than any one individual can hold, but all elements of the project must interact.
• Rigorous definition of data standards, interfaces and pipelines must be coupled with a broad view of the topology within which they play a part.