Gaining Time -- Real-time Analysis of Big Medical Data


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Hasso Plattner gave this presentation about how in-memory technology can support analysis of big medical data at the 2013 World Health Summit in Berlin. It consists real-world examples showing latest results of partners, such as the Hasso Plattner Institute, Stanford, Charité, and SAP. For background details, please refer to

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Gaining Time -- Real-time Analysis of Big Medical Data

  1. 1. Gaining  Time  –  Real-­‐.me  Analysis  of     Big  Medical  Data     Prof.  Dr.  Hasso  Pla,ner   Chairman  of  the  Supervisory  Board,  SAP  AG  and   Professor,  Hasso  Pla,ner  Ins?tute  
  2. 2. Growing Data Volumes in Diverse Healthcare Systems Human  genome/biological  data   800  MB  per  full  genome   15  PB+  in  databases  of  leading  ins?tutes   Human  proteome   160  Mil.  data  points  (2.4  GB)  per  sample   3.7  TB  raw  proteome  data  in  ProteomicsDB     Clinical  informa?on   management  systems   OMen  more  than  50  GB   PubMed  biomedical   ar?cle  database   Cancer  pa?ent  records   Medical  sensor  data   160,000  at     NCT  Heidelberg    Prescrip?on  data   1.5  Bil.  records  from  10,000  doctors   and  10  Mil.  Pa?ents  (100  GB)   23+  Mil.  ar?cles   Scan  of  a  single  organ   in  1s  creates  10GB  of   raw  data     Clinical  trials   Currently  more  than  30,000   recrui?ng  on   2  
  3. 3. Innovation in Medicine can be Driven Using a Design Thinking Approach Clinicians   Researchers   Human   Factors   Business   Factors   Desirability   Administra.on  &   Opera.ons  Staff     Technical   Factors   Viability   Feasibility   3  
  4. 4. Only  a  Effort  can  be   Viable  From  a  Business   Clinical   Pharma   Care  Circles   Pa.ents &   Consumers   Payers   SAP  HANA   Research   Desirability   Providers   Viability   Feasibility   4  
  5. 5. SAP  HANA  is  the     Technology  Enabler  for  This  Vision   Advances  in  Hardware   •  Mul?-­‐core  Architectures,   e.g.  16  CPUs  x  10  Cores  on   Each  Node   •  Scaling  Across  Servers,   e.g.  100  Nodes  x  160  Cores     •  64  bit  Address  Space  –   12TB  in  Current  Servers   •  25GB/s  Data  Throughput   •  Cost-­‐Performance  Ra?o   Improving   A   Advances  in  SoLware   Reduced Footprint Multi-Core Parallelization Compression Desirability   No  aggregate   tables   Viability   Federation Feasibility   Complex Algorithms 5  
  6. 6. More  Than  Just  a  Faster  Database,  SAP  HANA   is  a  Revolu.onary  PlaOorm   + Desirability   Viability   Feasibility   6  
  7. 7. Selected  SAP  HANA  Usage  Scenarios   Clinicians   Decision  Support   Researchers   Personalized     Proteome   medicine   Diagnos?cs   Medical  Knowledge  Cockpit   Medical  Explorer   Genomics  for   Personalized  Medicine   SAP   HANA   Prescrip?on   Analysis   Healthcare   Administra.on   Op?mized   Opera?ons   Pa?ent  Management   (IS-­‐H)  Analy?cs   7  
  8. 8. Research   Genome  Variant  Analysis   For  personalized/preventa?ve  medicine   §  §  Analysis  on  125   variants  in  629  people   Multi-Core in  parallel;  was  not   Parallelization possible  before   “   ”   Researchers  want  to  iden?fy  and  chart  amount   of  varia?on  in  one  gene  across  a  popula?on     §  Mul.-­‐Core   Paralleliza.on   Full  human  genome  is  3.2  billion  characters  long     With  SAP  HANA,  researchers  can  compare   gene?c  variants  of  diseased  &  healthy  cohorts     in  real-­‐?me   §  Using  SAP  HANA,  Stanford  has  seen   “spectacular”  findings:  Type  2  diabetes  disease   risk  is  very  different  across  popula?ons   "We  have  been  thrilled  to  work  with  SAP  and  HPI  on  a  collabora?on  to  accelerate  DNA  sequence  analysis.  In  our  pilot  projects,  we  are  seeing   drama?c  speedups  in  compu?ng  on  human  genome  varia?on  data  from  many  samples.  We  are  dreaming  of  what  will  soon  be  possible  as  we   8   integrate  phenotype,  genomics,  proteomics,  and  exposome  data  to  empower  complex  trait  mapping  using  millions  of  health  records.”     -­‐  Professor  Carlos  D.  Bustamante  at  the  Stanford  University  School  of  Medicine      
  9. 9. Proteome-­‐based  Cancer  Diagnos?cs      Plamorm  for  Researchers  and  Clinicians   Research   §  Diagnosis  can  be  done  by  analysing  proteome   “fingerprint”  from  just  one  drop  of  blood   §  Proteome  analysis  yields  very  large  data  sets   (160Mil  data  points/sample)     §  Fingerprint   recogni.on   on  high  resolu?on  data   now  possible  interface     for  complex  analysis   pipeline     Researchers  can  model  a  detec?on  pipeline   interac?vely  on  SAP  HANA   §  Researchers  can  manipulate  the  detec?on   pipeline  interac?vely   §  Minimally  invasive  diagnos?cs  made  possible  by   large  scale  studies   9  
  10. 10. ProteomicsDB  
  11. 11. ProteomicsDB  
  12. 12. ProteomicsDB  
  13. 13. ProteomicsDB  
  14. 14. ProteomicsDB  
  15. 15. ProteomicsDB  
  16. 16. ProteomicsDB  
  17. 17. ProteomicsDB  
  18. 18. Clinic   Medical  Explorer   Cancer  pa?ent  treatment  and  research   §  §  to  mul?ple  formerly   disjoint  data  sources   Flexible  Analy.cs   t   on  historical  data   Clinical  records  and  inclusion  criteria  are     very  complex   §  Clinical  data  from  different  sources  is   combined  in  one  SAP  HANA  system   §  Unified  access   Oncologists  need  to  find  the  best  treatment   op?on  for  pa?ents  à  Find  pa?ents  eligible   for  clinical  trials   Doctors  can  filter  pa?ent  cohorts  based  on   any  clinical  a,ribute  à  Pa?ents  eligible  for   clinical  trials  can  be  found  in  seconds   “In  the  future  we  would  like  to  use  SAP  HANA  at  every  diagnos?c  and  therapeu?c  step  in  the  fight  against  cancer  as  every  cancer  is  different   18   and  can  vary  immensely  from  one  pa?ent  to  the  next.“   -­‐  Prof.  Dr.  Christof  von  Kalle,  Head  of  Na?onal  Center  for  Tumor  Diseases  Heidelberg,  Germany  
  19. 19. Medical  Knowledge  Cockpit   Clinic   Relevant  scien?fic  findings  at  a  glance   §  Search  for  affected  genes  in  distributed  and   heterogeneous  data  sources   §  Immediate  explora?on  of  relevant   informa?on,  such  as   §  Gene  descrip?ons,   §  Molecular  impact  and  related  pathways,   §  Scien?fic  publica?ons,  and   §  Suitable  clinical  trials.   Unified  access  to   structured  and   unstructured  data  sources   Automa.c   clinical  trial   matching  using   HANA  text  analysis   features     §  No  manual  search  for  hours  or  days  –   SAP  HANA  translates  manual  searching  into   interac?ve  finding   19  
  20. 20. Pa?ent  Management  (IS-­‐H)  Analy?cs   Real-­‐?me  analysis  of  hospital  pa?ent  management  data   §  Medical  Controllers  need  to  check  occupancy   for  different  wards  frequently   §  Current  systems  too  slow  for  real-­‐?me   analysis  à  no    what-­‐if  scenarios  possible   §  HANA  made  sub-­‐second     response  ?mes  possible   §  Admin   New  analy?cal  applica?ons  can  now  help   drive  cost-­‐savings  and  more  efficient     resource  alloca?on   Flexible  analysis   –  no  need  for  materialized   aggregates   20  
  21. 21. Admin   Prescrip?on  Data  Analysis   Understanding  the  who,  where,  and  what  of  drug  prescrip?ons   §  Which  is  prescribed  e.g.  for  migraine?   §  Specialists  might  prescribe  different  drugs   than  general  prac??oners   §  SAP  HANA  cloud  system  holds  1.5  Bil.   Prescrip?on  records  for  around  10  Mil.   pa?ents  and  10,000  doctors   §  Data  can  be  explored  and  visualized   interac?vely  with  SAP  Lumira  in  seconds   Answers  in    1  sec.   instead  of  1  hour  analysis   using  data  graphics   "SAP  Health  Data  on  Demand  reduces  the  ?me  it  takes  to  analyze  our  more  than  1.5  bn  data  records  from  1  hour  to  1  second.  As  a  result,  we   21   are  able  to  offer  our  customers  new  online  services,  establish  a  new  business  model  and  generate  addi?onal  revenue.”     -­‐  Franz-­‐Xaver  Thalmeir,  Managing  Director,  Medimed  GmbH  
  22. 22. Healthcare  Projects  on  SAP  HANA   HANA  helps  gain  ?me  and  enables  completely  new  scenarios   Speedups  achieved   Pa?ent  Management  (IS-­‐H)  Analy?cs   50x  (55  seconds  à  800  milliseconds)   Virtual  Pa?ent  Plamorm   5000x  (4  hours  à  2-­‐3  seconds)   Prescrip?on  analysis   3600x  (1  hour  à  1  second)   DNA  Sequence  Alignment   17x  (85  hours  à  5  hours)   Proteome-­‐based  Cancer  Diagnos?cs   22x  (15  minutes  à  40  seconds)   New  usage  scenarios   Medical  Explorer   Genome  Analysis   Clinical  Trial  Matching   ProteomicsDB   Genome  Browser   Biological  Pathway  Analysis   Large  Pa?ent  Cohort  Analysis   HANA  Data  Scien?st   Genome  Data  Processing  and  Pipeline  Modeling   22  
  23. 23.   Demo           23  
  24. 24. The  Power  of  Mul.disciplinary  Teams   Only  Strong  Partners  Build  Strong  Co-­‐Opera?ve  Success  Stories     SAP:  Global  SoMware  Vendor  and  Expert  for  Enterprise   Technologies  World-­‐Wide   +   Hasso  PlaYner  Ins.tute:  Academic  Research  Ins?tute  for  IT   Systems  Engineering   +   Carlos  Bustamante  Lab:  Leading  Stanford  Lab  On  Human   Popula?on  Genomics  and  Global  Health   +   Charité  –  Universitätsmedizin  Berlin:  One  of  the  largest   university  hospitals  in  Europe              +   Na.onal  Center  for  Tumor  Diseases  Heidelberg  (NCT):  One  of   the  leading  ins?tu?ons  for  cancer  research  and  pa?ent  care   Design Thinking Teams You   Join Us! 24  
  25. 25. New  Ways  of  Real-­‐Time   Personal  Medicine   al