Big Data Trends A Basis for Personalized Medicine

Big Data Trends A Basis for Personalized Medicine Dr. Hellmuth Broda, Principal Technology Architect emedikation: Verordnung, Support Prozesse & Logistik 5. Juni, 2013, Inselspital Bern

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4 We Are in the Midst of an Information Tsunami Mankind is generating more data in two days than we did from the dawn of man until 2003 (Eric Schmidt, Google) Information is doubling every 18 months But this report did not include genome sequencing: Source: IDC White Paper: The Diverse and Exploding Digital Universe Source: National Human Genome Research Institute

5 What is Big Data The global information explosion does not exclude the medical community In the past we used Data Warehousing to make data available for new quests But Data Warehouses are limited to one compute center and (often) one server Data Warehouses need structured tabular information But most of the information is free text Big Data approaches allow to tap the gold nuggets hidden in the haystacks of free text information Some believe medicine can become more of a science, rather than a practice, by relying more on technology By combining data from many current and future devices and from different records, healthcare providers can have greater insight into medicine This could generate thousands of data points about a person s health and lead to faster and cheaper correct diagnoses

6 Promises of Big Data More and more data can be gathered to identify patterns and physiological interactions Machine learning software can point to abnormalities and predict health issues Smart phones and ianythings will empower the patient to monitor his health Big Data analysis can help to move from corrective to preventive medicine Doctors will increasingly rely on technology and Big Data for triage, diagnosis and decision-making Consequently, doctors will perform better, health care costs can decrease, and patient care can improve Pharmaceutical companies when screening large clouds of Big Data can find and corroborate seemingly weak correlations and target medicines to subgroups of patients with similar genetic backgrounds

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8 Challenges Faced by Managing Big Data Explosion in Volume of Data Available for Research Expansion into genomics, proteomics and metabolomics research has resulted in an explosion of data across the research organizations Advancements into Biomarkers and Imaging biomarkers have added to the existing data volume and need for high throughput computing Availability of data from the public domains and collaborators for research have added to the current data management issues Existence of Data Silos A large amount of this data resides in data silos across the global sites. In addition, translation and personalized medicine based drug development requires an integration with clinical and real-world patient and physician data which are spread across disparate silos across the globe Infrastructure challenges to meet the high throughput computing needs With the increase in computing and collaboration needs, IT infrastructure strategy needs to adapt to keep up

9 Leveraging Value from Big Data Integrate data siloes across the research organization to allow scientists easy access to the wealth of information available Also the ability to capture and ingest data from multiple structured and unstructured data sources Develop and place flexible and broad high throughput analytical layers on the integrated data sets to mine the information Continue to allow the researchers the flexibility of choosing a broad array of analytical tools that fit there research needs Advanced near real time analytics over Big Data combining structured and unstructured data Ability to process data on cloud architecture and cloud platforms Develop scalable and flexible infrastructure to support the increasing computing needs. Bioinformatics is increasingly needing Tera-Scale computing in the areas of drug discovery and drug repurposing

10 Big Data in Life Sciences* Sequencing and gene expression data Drug data (pathways, structure etc.) Clinical Trial & Hospital Electronic Record data In vitro diagnostics & X-ray results Patient self-reported and social media data Telemedicine data Big Data Analytics Drug repurposing New biomarkers New drug targets Personalized healthcare Adverse event detection Etc. *This and the following nine slides contributed by Edward Currie, AVP Life Sciences, Infosys

11 Big Data in Life Sciences Example Use Cases Drug Repurposing Health recordguided drug development Next Generation Sequencing Personalized Healthcare Treatment Adherence (Compliance) Adverse Event Detection Patient Pre- Profiling Influencer Profiling

12 Drug Repurposing Transcriptome signatures Compound structures Drug/protein interactions Side-effect profiles Mechanisms of action Understand which new diseases are most likely to be treatable with a drug Drug Repurposing BIG DATA ANALYSED INSIGHT DELIVERED STRATEGY ENABLED

13 Healthcare Record Guided Drug Development Drug data as for DRP + Healthcare record data Understand outcomes of patients who have been treated with drugs of similar profile to that of the drug of interest Identify patients for clinical trials Predict real-world efficacy & safety Reiterate drug design to optimize efficacy & safety BIG DATA ANALYSED INSIGHT DELIVERED STRATEGY ENABLED

14 Next Generation Sequencing Sequencing data Outcome data (clinical trial or hospital) Understand which gene mutations are associated with which diseases New drug targets New biomarkers BIG DATA ANALYSED INSIGHT DELIVERED STRATEGY ENABLED

15 Personalized Healthcare Prescription (Rx) data Diagnostic test (Dx) data Sequencing data Outcome data (clinical trial or hospital) Understand which patients respond to which drugs Personalized Healthcare BIG DATA ANALYSED INSIGHT DELIVERED STRATEGY ENABLED

16 Compliance (Treatment Adherence) Prescription (Rx) data Social media analytics Understand why patients don t take their medication correctly/fully Treatment Adherence BIG DATA ANALYSED INSIGHT DELIVERED STRATEGY ENABLED

17 Adverse Event Detection Prescription (Rx) data Social media analytics Identify otherwise unreported drug sideeffects/ interactions Drug safety BIG DATA ANALYSED INSIGHT DELIVERED STRATEGY ENABLED

18 Patient Pre-Profiling Prescription (Rx) data Social media analytics Personal genomics data Identify patients eligible and willing to take part in clinical trials, and who have elected to be genetically profiled Clinical Trial Subject Pre-Profiling BIG DATA ANALYSED INSIGHT DELIVERED STRATEGY ENABLED

19 Influencer Profiling Prescription (Rx) data Social media analytics Medical literature Identify the key influencers among different customer groups: key opinion leaders; prescribers; patients Influencer Profiling BIG DATA ANALYSED INSIGHT DELIVERED STRATEGY ENABLED

Infosys BigData Edge Enables building solutions though ETL like graphical tool accelerating time to market while reducing cost 20 Agility Actionable intelligence from new data in hours Speed Expedite your processing deployment across all data types by 10X or more Business Value Monetize your data through applied insights BigData- Edge Consumption Integration with wide variety of industry solutions Industry leading Visualization techniques for deep insights Full Featured Hub Management One-Click Cloud Deployment -Seamless Cluster Setup, Configuration Industry specific Solutions

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22 Conclusion 1. Data in our world are growing exponentially 2. There is tons of hidden and useful information in those data 3. Clever methods to mine these data are necessary 4. Big Data analysis methodologies are instrumental in detecting new information 5. Infosys BigDataEdge is one of the most mature solutions on the market 6. Big Data will help to detect correlations between people and illnesses 7. Big Data will help in Drug Repurposing 8. Big Data will allow for patient pre-profiling 9. Big Data will help in adverse reaction monitoring and detection 10.Big Data will help progress in Personalized Medicine

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