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Biosurveillance 2.0


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Seamlessly integrating various early disease indicators with experts' opinion for better event warning and response...

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Biosurveillance 2.0

  1. 1. Biosurveillance 2.0 Collaboration and Web 2.0/3.0 Semantic Technologies for Better Early Disease Warning and Effective Response Taha Kass-Hout Nicolás di Tada
  2. 2. Background
  3. 3. Late Detection and Response DAY CASES Opportunity for control Background
  4. 4. Early Detection and Response DAY CASES Opportunity for control Background
  5. 5. Public Health Measures <ul><li>Representativeness </li></ul><ul><li>Completeness </li></ul><ul><li>Predictive Value </li></ul><ul><li>Timeliness </li></ul>Background
  6. 6. Public Health Measures 1000 Malaria infections (100%) 50 Malaria notifications (5%) Specificity / Reliability Sensitivity / Timeliness <ul><ul><li>Main attributes </li></ul></ul><ul><ul><ul><li>Representativeness </li></ul></ul></ul><ul><ul><ul><li>Completeness </li></ul></ul></ul><ul><ul><ul><li>Predictive value positive </li></ul></ul></ul>Background Get as close to the bottom of the pyramid as possible Urge frequent reporting: Weekly  daily  immediately
  7. 7. Public Health Measures Analyze and interpret Automated analysis/ thresholds Time <ul><ul><li>Main attributes </li></ul></ul><ul><ul><ul><li>Timeliness </li></ul></ul></ul>Health care hotline Background Signal as early as possible
  8. 8. The Problem Space <ul><li>Current systems design, analysis and evaluation has been geared towards specific data sources and detection algorithms – not humans </li></ul><ul><li>We have systems in place for those threats we have been faced with before </li></ul>The Problem
  9. 9. Traditional DISEASE SURVEILLANCE <ul><li>In the past two decades focus was on </li></ul><ul><ul><li>automatically detecting anomalous patterns in data (often a single stream) </li></ul></ul><ul><li>Modern methods </li></ul><ul><ul><li>rely on human input and judgment </li></ul></ul><ul><ul><li>incorporate temporal , spatial , and multivariate information </li></ul></ul>The Problem
  10. 10. Traditional DISEASE SURVEILLANCE 9/20, 15213, cough/cold, … 9/21, 15207, antifever, … 9/22, 15213, CC = cough, ... 1,000,000 more records… Huge mass of data Detection algorithm “ What are we supposed to do with this?” Too many alerts The Problem
  11. 11. Our Approach <ul><li>Human-based </li></ul><ul><li>Collaborative and cross-disciplinary </li></ul><ul><li>Web 2.0/3.0 platform </li></ul>Our Approach
  12. 12. Information Sources <ul><li>Event-based - ad-hoc unstructured reports issued by formal or informal sources </li></ul><ul><li>Indicator-based - (number of cases, rates, proportion of strains…) </li></ul>Timeliness, Representativeness, Completeness, Predictive Value, Quality, … Our Approach
  13. 13. MODERN DISEASE SURVEILLANCE 9/20, 15213, cough/cold, … 9/21, 15207, antifever, … 9/22, 15213, CC = cough, ... 1,000,000 more records… Huge mass of data Feedback loop Our Approach Fewer and more actionable alerts Effective and coordinated response
  14. 14. Evolve: Main Components Feature extraction, reference and baseline information Tags Multiple Data Streams User-Generated and Machine Learning Metadata Comments Spatio-temporal Flags/Alerts/Bookmarks Evolve Bot Event Classification, Characterization and Detection Previous Event Training Data Previous Event Control Data Metadata extraction Machine learning Social network Professional feedback Anomaly detection Collaborative Spaces Hypotheses generation esting Our Solution
  15. 15. Evolve: Main Components Our Solution
  16. 16. Evolve: Process Item Hypothesis Field Actions and Verifications Feedback / Confirmation Our Solution Item Item Item Item Item Item Item Item
  17. 17. Advantages of Machine Learning P(malaria) = 22% P(influenza) = 13% P(other ILI) = 33% Our Solution
  18. 18. Machine Learning Techniques <ul><li>Classifiers </li></ul><ul><li>Clustering </li></ul><ul><li>Bayesian Statistics </li></ul><ul><li>Neural Networks </li></ul><ul><li>Genetic Algorithms </li></ul>Our Solution
  19. 19. How to represent a document: cold fever Our Solution
  20. 20. (1) Classifiers: Problem Definition <ul><li>Map items to vectors (Feature extraction) </li></ul><ul><li>Normalize those vectors </li></ul><ul><li>Train the classifier </li></ul><ul><li>Measure the results with new information </li></ul><ul><li>Feedback the classifier </li></ul><ul><li>Separate classes in feature space </li></ul>Our Solution
  21. 21. Classifiers: Support Vector Machines (SVM) Our Solution
  22. 22. SVM – Margin Maximization <ul><li>Support vectors define the separator </li></ul>Our Solution
  23. 23. SVM – Non-linear? Φ : x -> φ ( x ) Map to higher-dimension space Our Solution
  24. 24. SVM – Filtering or classifying Classifier Document 1 Document 2 Document 3 Positives Negatives Training Document Training Document Our Solution
  25. 25. (2) Clustering: Problem Definition <ul><li>Map items to vectors (Feature extraction) </li></ul><ul><li>Normalization </li></ul><ul><li>Agglomerative or Partitional </li></ul>Our Solution
  26. 26. Clustering: AGGLOMERATIVE Our Solution
  27. 27. Clustering: PARTITIONAL Our Solution
  28. 28. (3) Bayesian Statistics Probability of disease A (flu) once symptom B (fever) is observed Probability of fever once flu is confirmed Probability of flu (prior or marginal) Probability of fever (prior or marginal) Our Solution
  29. 29. (4) Neural Networks <ul><li>Given a set of stimuli, train a system to produce a given output… </li></ul>Our Solution
  30. 30. Neural Network: Structure Hidden Layer Output Layer Input Layer […] […] {I 0 ,I 1 ,……I n } {O 0 ,O 1 ,……O n } Weight Our Solution
  31. 31. Neural Network: Application Event? Our Solution
  32. 32. (5) Genetic Algorithm: Basic <ul><li>Define the model that you want to optimize </li></ul><ul><li>Create the fitness function </li></ul><ul><li>Evolve the gene pool testing against the fitness function. </li></ul><ul><li>Select the best individual </li></ul>Our Solution
  33. 33. Genetic Algorithm: Model <ul><li>Model the transmission process using a set of parameters ( e.g., an infectious disease ): </li></ul><ul><ul><li>Onset time between an infection and illness </li></ul></ul><ul><ul><li>Latency period </li></ul></ul><ul><ul><li>Incubation period </li></ul></ul><ul><ul><li>Symptomatic period </li></ul></ul><ul><ul><li>Infectious period </li></ul></ul>(Onset, Latency, Incubation, Symptomatic , Infectious) ( 2 days, 3 days, 1 day, 4 days, 3 days) Our Solution
  34. 34. Genetic Algorithm: Model Fitness Fitness = 1/Area Our Solution
  35. 35. Genetic Algorithm: Process <ul><li>Create an initial population of candidates </li></ul><ul><li>Use operators to generate new candidates (mating and mutation) </li></ul><ul><li>Discard worst individuals or select best individuals in generation </li></ul><ul><li>Repeat from 2 until you find a candidate that satisfies the solution searched </li></ul>Our Solution
  36. 36. Genetic Algorithm: Process (4, 5 ,6, 3 ,5) (4,3,6,2,5) (5,3,4,6,2) (2,4,6,3,5) (4,3,6,5,2) (2,3,4,6,5) (3,4,5,2,6) (3,5,4,6,2) (4,5,3,6,2) (5,4,2,3,6) (4,6,3,2,5) (3,4,2,6,5) (3,6,5,1,4) ( 5,3 , 2,6,5 ) ( 3,4 , 4,6,2 ) ( 5,3 , 2,6,5 ) ( 3,4 , 4,6,2 ) Our Solution
  37. 37. Result of incorporating all 5 techniques: Improved Surveillance Our Solution
  38. 38. Our Solution InSTEDD Evolve Related items (e.g., News articles) are grouped into a thread. Threads are later associated with events (hypothesized or confirmed). InSTEDD Evolve : ( ) Tag cloud and semantic heatmap
  39. 39. Our Solution InSTEDD Evolve InSTEDD Evolve : ( ) Filter feature which automatically filters for related items, updates the map and associated tags
  40. 40. Our Solution InSTEDD Evolve InSTEDD Evolve : ( ) Auto-generated (machine-learning) tags. These tags are semantically ranked (a statistical probability match). Users can further train the classifier by accepting or rejecting a suggestion. Users can similarly train the geo-locator by simply accepting or rejecting and updating a location.
  41. 41. Our Solution InSTEDD Evolve InSTEDD Evolve : ( ) Tracking the recent Avian Influenza Outbreak in Egypt (reports started to appear late January 2009). Notice the pattern of reported incidents along the Nile river.
  42. 42. Acknowledgements
  43. 43. Through funding from:
  44. 44. Thank You! <ul><li>Taha Kass-Hout </li></ul>Nicolás di Tada
  46. 46. Index <ul><li>Disease surveillance References </li></ul><ul><ul><li>Computing </li></ul></ul><ul><ul><li>Automating Laboratory Reporting </li></ul></ul><ul><ul><li>Using EMR data for disease surveillance </li></ul></ul><ul><ul><li>Related Projects </li></ul></ul><ul><ul><li>Misc Readings </li></ul></ul><ul><li>Open Source Software (OSS) References </li></ul><ul><ul><li>Open Source License References </li></ul></ul><ul><ul><li>Open Source References </li></ul></ul><ul><ul><li>Open Source and Public Health References </li></ul></ul><ul><li>Architectural Matters </li></ul><ul><ul><li>Service Oriented Architecture (or SOA) </li></ul></ul><ul><ul><li>Synchronization Architecture </li></ul></ul><ul><ul><li>Cloud Architecture </li></ul></ul>
  47. 47. DISEASE SURVEILLANCE <ul><li>References and Related-Efforts </li></ul>
  48. 48. REFERENCES <ul><li>Izadi, M. and Buckeridge, D., Decision Theoretic Analysis of Improving Epidemic Detection, AMIA 2007, Symposium Proceedings 2007 </li></ul><ul><li>EpiNorth-Based material ( ): </li></ul><ul><ul><li>Mereckiene, J., Outbreak Investigation Operational Aspects. Jurmala, Latvia, 2006 </li></ul></ul><ul><ul><li>Bagdonaite, J., and Mereckiene, J., Outbreak Investigation Methodological aspects. Jurmala, Latvia, 2006 </li></ul></ul><ul><ul><li>Epidemic Intelligence: Signals from surveillance systems, Anne Mazick, Statens Serum Institut, Denmark, EpiTrain III, Jurmala, August 2006 </li></ul></ul><ul><li>Daniel Neil, Incorporating Learning into Disease Surveillance Systems </li></ul>
  49. 49. REFERENCES <ul><li>Computing </li></ul><ul><ul><li>The Future of Statistical Computing in Wilkinson (2008) </li></ul></ul><ul><ul><li>Complex Event Processing Over Uncertain Data in Wasserkrug (2008) </li></ul></ul><ul><ul><li>Outbreak detection through automated surveillance A review of the determinants of detection in Buckeridge (2007) </li></ul></ul><ul><ul><li>Approaches to the evaluation of outbreak detection methods in Watkins (2006) </li></ul></ul><ul><ul><li>Algorithms for rapid outbreak detection a research synthesis Buckeridge (2004) </li></ul></ul><ul><ul><li>Data mining in bioinformatics using Weka in Frank (2004) </li></ul></ul><ul><ul><li>Aho-Corasick Algorithm in Kilpeläinen </li></ul></ul><ul><li>Automating Laboratory Reporting </li></ul><ul><ul><li>Automatic Electronic Laboratory-Based Reporting in Panackal (2002) </li></ul></ul><ul><ul><li>Benefits and Barriers to Electronic Laboratory Results Reporting for Notifiable Diseases in Nguyen (2007) </li></ul></ul>
  50. 50. REFERENCES <ul><li>Using EMR Data for Disease Surveillance </li></ul><ul><ul><li>Using Electronic Medical Records to Enhance Detection and Reporting of Vaccine Adverse Events in Hinrichsen (2007) </li></ul></ul><ul><ul><li>Electronic Medical Record Support for PH in Klompas (2007) </li></ul></ul><ul><ul><li>A knowledgebase to support notifiable disease surveillance in Doyle (2005) </li></ul></ul><ul><ul><li>Automated Detection of Tuberculosis Using Electronic Medical Record Data in Calderwood (2007) </li></ul></ul><ul><li>Misc Readings </li></ul><ul><ul><li>Breakthrough in modeling emerging disease hotspots in Jones (2008) </li></ul></ul><ul><ul><li>Use of data mining techniques to investigate disease risk classification as a proxy for compromised biosecurity of cattle herds in Wales in Ortiz-Pelaez (2008) </li></ul></ul><ul><ul><li>Euclidean distance: </li></ul></ul><ul><ul><li>Tags/Folksonomy: </li></ul></ul><ul><ul><ul><li>Tag Decay: A View Over Aging Folksonomy in Russell (2007) </li></ul></ul></ul><ul><ul><ul><li>Cloudalicious: Folksonomy Over Time in Russell (2006) </li></ul></ul></ul>
  51. 51. RELATED PROJECTS <ul><li>InSTEDD Evolve : ( ) </li></ul><ul><ul><li>Collaborative Analytics and Environment for Linking Early Health-Related Event Detection to an Effective Response ( ) </li></ul></ul><ul><li>ALPACA &quot;ALPACA Light Parsing And Classifying Application (ALPACA) is a classifying tool designed for use in community-oriented software as well as in Academia. The application consists of two parts: a parsing tool for transforming raw documents into readable data, and a classifying tool for categorizing documents into user-provided classes. The application provides a user-friendly interface and a Plug-in functionality to provide a simple way to add more parsers/classifiers to the application.&quot; </li></ul><ul><li>Weka An open source &quot;...collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes.&quot; </li></ul>
  52. 52. RELATED PROJECTS <ul><li>The R Project for statistical computing: </li></ul><ul><ul><li>Surveillance Project: An Open Source R-package disease surveillance framework for &quot;...the development and the evaluation of outbreak detection algorithms in univariate and multivariate routine collected public health surveillance data.&quot; </li></ul></ul><ul><ul><ul><li>The R package surveillance in Höhle (multiple articles) </li></ul></ul></ul><ul><li>Google's Research Publications: MapReduce Simplified Data Processing on Large Clusters ( ) </li></ul><ul><ul><li>Hadoop : a software platform that lets one easily write and run applications that process vast amounts of data ( ) </li></ul></ul>
  53. 53. OPEN SOURCE SOFTWARE <ul><li>References and Related-Efforts </li></ul>
  54. 54. REFERENCES <ul><li>Open Source License References </li></ul><ul><ul><li> </li></ul></ul><ul><ul><li> </li></ul></ul><ul><li>Open Source References </li></ul><ul><ul><li> </li></ul></ul><ul><ul><li> </li></ul></ul><ul><ul><li>   </li></ul></ul><ul><li>Open Source and Public Health References </li></ul><ul><ul><li> </li></ul></ul><ul><ul><li> </li></ul></ul><ul><ul><li> </li></ul></ul><ul><ul><li>Open Source Development for Public Health: A Primer with Examples of Existing Enterprise Ready Open Source Applications in Turner (2006) </li></ul></ul><ul><ul><li>A Quick Survey of Open Source Software for Public Health Organizations in Mirabito and Kass-Hout (2007) </li></ul></ul>
  55. 55. ARCHITECTURAL MATTERS <ul><li>References and Related-Efforts </li></ul>
  56. 56. REFERENCES <ul><li>Service Oriented Architecture (or SOA) </li></ul><ul><ul><li>Proposal for Fulfilling Strategic Objectives of the U.S. Roadmap for National Action on Decision Support through a Service—oriented Architecture Leveraging HL7 Services in Kawamoto (2007) </li></ul></ul><ul><ul><li>Service-oriented Architecture in Medical Software: Promises and Perils in Nadkarni (2007) </li></ul></ul><ul><ul><li>Wiki sources: </li></ul></ul><ul><ul><ul><li>SOA: </li></ul></ul></ul><ul><ul><ul><li>Semantic service oriented architecture: </li></ul></ul></ul><ul><li>Synchronization Architecture </li></ul><ul><ul><li>InSTEDD’s Mesh4x: </li></ul></ul><ul><li>Cloud Architecture </li></ul><ul><ul><li>Google App Engine: Google App Engine Goes Up Against Amazon Web Services in Gartner Report (2008) </li></ul></ul>