Big Data challenges for Real-time Personalized Medicine

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<ul><li> 1. Big Data challenges for Real-time Personalized Medicine @tweetsinha </li></ul> <p> 2. 1GB 3D CT Scan 150MB 3D MRI 30MB X-ray 120MB Mammograms 300 TB+ 200 Cancer Genomes 200 TB+ All Known Variants 15 PB+ Broad &amp; Sanger DB 800 MB Per Genome 20-40% annual increase in medical image archives Explosion of Biological Health Information Has Surpassed Human Cognitive Capacity BIGDATA 1990 Decisions by Clinical Phenotype Structural Genetics FactsperDecision 2000 2010 2020 5 10 100 1000 Functional Genetics Proteomics and other effector molecules The Strategic Application of Information Technology in Health Care Organizations (Third Edition 2011) by John P. Glaser and Claudia Salzberg 3. 2013 SAP AG. All rights reserved. 3 What Researchers Desire Identify Causal Variants or Mutations in Cohorts Suffering from Diseases of Interest 4. 2013 SAP AG. All rights reserved. 4 What Clinicians Desire Identify Clinically Actionable Genetic Variants in Order to Deliver Personalized Medical Treatment 5. 2013 SAP AG. All rights reserved. 5 Vendors Care Circles Patients Clinical Research Payers Co-Innovation for Real-Time Experience Technical Feasibility Economical Viability Human Desirability 6. 2013 SAP AG. All rights reserved. 6 Technical Feasibility Genomics Pipeline: Dramatically Accelerated Up to 600X Faster Patient Samples Raw DNA Reads Mapped Genome Discovered Variants Follow-up &amp; Validation Real Genome Data 70x Coverage of Human Genome 17Xfaster 84hrs Industry Standard (BWA-SW) vs. 5hrs SAP HANA Report SNPs (Single Nucleotide Polymorphisms) Falling Quality Control 82Xfaster 102.47sec UCSC vs. 1.25sec SAP HANA Compute the Number of Missing Genotypes for Each Individual 270X faster 548secs VCF Tools vs. 2 sec SAP HANA Compute the Alternative Allele Frequency for Each Variant in a Genomic Region (Chromosome 1, Positions 100,000 200,000) 600Xfaster 259sec VCF Tools vs. 0.43sec SAP HANA Sequencing Alignment Variant Calling Annotation &amp; Analysis Computationally Intensive Genomics Pipeline Promising Early Results 7. 2013 SAP AG. All rights reserved. 7 Our Vision: Enabling Real-Time Personalized Medicine Lifestyle DataBiological dataClinical Data (EMRs) Real-time Big Data Convergence SAP HANA Real-time Big Data Platform Interpret all patient data during a patients visit 8. Thank you 9. 2013 SAP AG. All rights reserved. 9 Mitsui Knowledge Industry Healthcare Industry Cancer cell genomic analysis Reduce the time to detect variant DNA Support personalized patient therapeutics DNA results 216x faster in 20 minutes or less Streamline process of providing individualized cancer drug recommendation 10. 2013 SAP AG. All rights reserved. 10 Charit Berlin Healthcare industry Personalized healthcare for cancer patients Improve cancer treatment with new patient therapies 1,000x faster tumor data analysis (in seconds) Real-time analysis of 300M patient entries across departments and geographies Reduced time in staff shift changes Personalized healthcare for cancer patients 11. 2013 SAP AG. All rights reserved. 11 60x faster processing queries from 3 hours to 3 minutes 10x data compression from 1.5 TB to 150 GB 250x better long text handling from 60 to 15,000 characters Medtronic, Inc. Life Sciences Industry Global complaint handling benefitting 6M patients/year </p>