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Biosurveillance 2.0: Lecture at Emory University

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Invited lecture at Emory University Rollins School of Public Health. We presented our InSTEDD global early warning and response social platform; Evolve (http://instedd.org/evolve) with live …

Invited lecture at Emory University Rollins School of Public Health. We presented our InSTEDD global early warning and response social platform; Evolve (http://instedd.org/evolve) with live demonstration.


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    • 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 Invited by Dr. Barbara Massoudi, PhD, MPH Lecture at Emory University Rollins School of Public Health Public Health Informatics, INFO 503 Atlanta, GA, USA
    • 2.  
    • 3. Background
    • 4. Late Detection and Response DAY CASES Opportunity for control Background
    • 5. Early Detection and Response DAY CASES Opportunity for control Background
    • 6. Public Health Measures
      • Representativeness
      • Completeness
      • Predictive Value
      • Timeliness
      Background
    • 7. Public Health Measures 1000 Malaria infections (100%) 50 Malaria notifications (5%) Specificity / Reliability Sensitivity / Timeliness
        • Main attributes
          • Representativeness
          • Completeness
          • Predictive value positive
      Background Get as close to the bottom of the pyramid as possible Urge frequent reporting: Weekly  daily  immediately
    • 8. Public Health Measures Analyze and interpret Automated analysis/ thresholds Time
        • Main attributes
          • Timeliness
      Health care hotline Background Signal as early as possible
    • 9. Public Health – Two Perspectives
      • Case management
        • Individual cases of notifiable diseases
        • Relationship networks (contact tracing)
      • Population surveillance
        • Larger risk patterns
      Background
    • 10. Case Management
      • Questions and problems:
        • Is a case due to recent transmission?
        • If so, does the case share any feature with other recent cases?
      • Current methods:
        • Investigations and interviews
        • Meeting with other investigators
      Background
    • 11. Population Surveillance
      • Questions and problems:
        • Are more cases happening than expected?
        • Does an excess suggest ongoing transmission in a specific region?
      • Current methods:
        • Semi-automated routine temporal and space-time statistical analysis
      Background
    • 12. Why location matters: Case Management
      • If you are studying a case of a certain disease that was just declared
      • It is harder to picture the situation by looking at something like this...
      Background
    • 13. Why location matters: Case Management Background
    • 14. Why location matters: Case Management
      • Than by looking at this..
      Background
    • 15. Why location matters: Case Management Background
    • 16. Why location matters: Population Surveillance
      • If you are studying the spatial distribution of a set of disease clusters, this next slide seems more difficult…
      Background
    • 17. Why location matters: Population Surveillance Background
    • 18. Why location matters: Population Surveillance
      • Than this...
      Background
    • 19. Why location matters: Population Surveillance Background
    • 20. The Problem Space
      • Current systems design, analysis and evaluation has been geared towards specific data sources and detection algorithms – not humans
      • We have systems in place for those threats we have been faced with before
      The Problem
    • 21. Traditional DISEASE SURVEILLANCE
      • In the past two decades focus was on
        • automatically detecting anomalous patterns in data (often a single stream)
      • Modern methods
        • rely on human input and judgment
        • incorporate temporal , spatial , and multivariate information
      The Problem
    • 22. 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
    • 23. Our Approach
      • Human-based
      • Collaborative and cross-disciplinary
      • Web 2.0/3.0 platform
      Our Approach
    • 24. Information Sources
      • Event-based - ad-hoc unstructured reports issued by formal or informal sources
      • Indicator-based - (number of cases, rates, proportion of strains…)
      Timeliness, Representativeness, Completeness, Predictive Value, Quality, … Our Approach
    • 25. 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
    • 26. 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
    • 27. Evolve: Main Components Our Solution
    • 28. Evolve: Process Item Hypothesis Field Actions and Verifications Feedback / Confirmation Our Solution Item Item Item Item Item Item Item Item
    • 29. Advantages of Machine Learning P(malaria) = 22% P(influenza) = 13% P(other ILI) = 33% Our Solution
    • 30. Machine Learning Techniques
      • Classifiers
      • Clustering
      • Bayesian Statistics
      • Neural Networks
      • Genetic Algorithms
      Our Solution
    • 31. How to represent a document: cold fever Our Solution
    • 32. (1) Classifiers: Problem Definition
      • Map items to vectors (Feature extraction)
      • Normalize those vectors
      • Train the classifier
      • Measure the results with new information
      • Feedback the classifier
      • Separate classes in feature space
      Our Solution
    • 33. Classifiers: Support Vector Machines (SVM) Our Solution
    • 34. SVM – Margin Maximization
      • Support vectors define the separator
      Our Solution
    • 35. SVM – Non-linear? Φ : x -> φ ( x ) Map to higher-dimension space Our Solution
    • 36. SVM – Filtering or classifying Classifier Document 1 Document 2 Document 3 Positives Negatives Training Document Training Document Our Solution
    • 37. (2) Clustering: Problem Definition
      • Map items to vectors (Feature extraction)
      • Normalization
      • Agglomerative or Partitional
      Our Solution
    • 38. Clustering: AGGLOMERATIVE Our Solution
    • 39. Clustering: PARTITIONAL Our Solution
    • 40. (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
    • 41. (4) Neural Networks
      • Given a set of stimuli, train a system to produce a given output…
      Our Solution
    • 42. Neural Network: Structure Hidden Layer Output Layer Input Layer […] […] {I 0 ,I 1 ,……I n } {O 0 ,O 1 ,……O n } Weight Our Solution
    • 43. Neural Network: Application Event? Our Solution
    • 44. (5) Genetic Algorithm: Basic
      • Define the model that you want to optimize
      • Create the fitness function
      • Evolve the gene pool testing against the fitness function.
      • Select the best individual
      Our Solution
    • 45. Genetic Algorithm: Model
      • Model the transmission process using a set of parameters ( e.g., an infectious disease ):
        • Onset time between an infection and illness
        • Latency period
        • Incubation period
        • Symptomatic period
        • Infectious period
      (Onset, Latency, Incubation, Symptomatic , Infectious) ( 2 days, 3 days, 1 day, 4 days, 3 days) Our Solution
    • 46. Genetic Algorithm: Model Fitness Fitness = 1/Area Our Solution
    • 47. Genetic Algorithm: Process
      • Create an initial population of candidates
      • Use operators to generate new candidates (mating and mutation)
      • Discard worst individuals or select best individuals in generation
      • Repeat from 2 until you find a candidate that satisfies the solution searched
      Our Solution
    • 48. 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
    • 49. Result of incorporating all 5 techniques: Improved Surveillance Our Solution
    • 50. 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 : ( http://instedd.org/evolve ) Tag cloud and semantic heatmap
    • 51. Our Solution InSTEDD Evolve InSTEDD Evolve : ( http://instedd.org/evolve ) Filter feature which automatically filters for related items, updates the map and associated tags
    • 52. Our Solution InSTEDD Evolve InSTEDD Evolve : ( http://instedd.org/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.
    • 53. Our Solution InSTEDD Evolve InSTEDD Evolve : ( http://instedd.org/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.
    • 54. Acknowledgements
    • 55. Through funding from:
    • 56. Thank You!
      • Taha Kass-Hout
      Nicolás di Tada
    • 57. BACKGROUND MATERIAL
    • 58. Index
      • Disease surveillance References
        • Computing
        • Automating Laboratory Reporting
        • Using EMR data for disease surveillance
        • Related Projects
        • Misc Readings
      • Open Source Software (OSS) References
        • Open Source License References
        • Open Source References
        • Open Source and Public Health References
      • Architectural Matters
        • Service Oriented Architecture (or SOA)
        • Synchronization Architecture
        • Cloud Architecture
    • 59. DISEASE SURVEILLANCE
      • References and Related-Efforts
    • 60. REFERENCES
      • Izadi, M. and Buckeridge, D., Decision Theoretic Analysis of Improving Epidemic Detection, AMIA 2007, Symposium Proceedings 2007
      • EpiNorth-Based material ( http://www.epinorth.org ):
        • Mereckiene, J., Outbreak Investigation Operational Aspects. Jurmala, Latvia, 2006
        • Bagdonaite, J., and Mereckiene, J., Outbreak Investigation Methodological aspects. Jurmala, Latvia, 2006
        • Epidemic Intelligence: Signals from surveillance systems, Anne Mazick, Statens Serum Institut, Denmark, EpiTrain III, Jurmala, August 2006
      • Daniel Neil, Incorporating Learning into Disease Surveillance Systems
    • 61. REFERENCES
      • Computing
        • The Future of Statistical Computing in Wilkinson (2008)
        • Complex Event Processing Over Uncertain Data in Wasserkrug (2008)
        • Outbreak detection through automated surveillance A review of the determinants of detection in Buckeridge (2007)
        • Approaches to the evaluation of outbreak detection methods in Watkins (2006)
        • Algorithms for rapid outbreak detection a research synthesis Buckeridge (2004)
        • Data mining in bioinformatics using Weka in Frank (2004)
        • Aho-Corasick Algorithm in Kilpeläinen
      • Automating Laboratory Reporting
        • Automatic Electronic Laboratory-Based Reporting in Panackal (2002)
        • Benefits and Barriers to Electronic Laboratory Results Reporting for Notifiable Diseases in Nguyen (2007)
    • 62. REFERENCES
      • Using EMR Data for Disease Surveillance
        • Using Electronic Medical Records to Enhance Detection and Reporting of Vaccine Adverse Events in Hinrichsen (2007)
        • Electronic Medical Record Support for PH in Klompas (2007)
        • A knowledgebase to support notifiable disease surveillance in Doyle (2005)
        • Automated Detection of Tuberculosis Using Electronic Medical Record Data in Calderwood (2007)
      • Misc Readings
        • Breakthrough in modeling emerging disease hotspots in Jones (2008)
        • 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)
        • Euclidean distance: http://en.wikipedia.org/wiki/Euclidean_distance
        • Tags/Folksonomy:
          • Tag Decay: A View Over Aging Folksonomy in Russell (2007)
          • Cloudalicious: Folksonomy Over Time in Russell (2006)
    • 63. RELATED PROJECTS
      • InSTEDD Evolve : ( http://instedd.org/evolve )
        • Collaborative Analytics and Environment for Linking Early Health-Related Event Detection to an Effective Response ( http://taha.instedd.org/2008/09/collaborative-analytics-and-environment.html )
      • ALPACA "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." http://2008.hfoss.org/ALPACA
      • Weka An open source "...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." http://www.cs.waikato.ac.nz/~ml/weka/
    • 64. RELATED PROJECTS
      • The R Project for statistical computing: http://www.r-project.org
        • Surveillance Project: An Open Source R-package disease surveillance framework for "...the development and the evaluation of outbreak detection algorithms in univariate and multivariate routine collected public health surveillance data." http://surveillance.r-forge.r-project.org
          • The R package surveillance in Höhle (multiple articles)
      • Google's Research Publications: MapReduce Simplified Data Processing on Large Clusters ( http://labs.google.com/papers/mapreduce.html )
        • Hadoop : a software platform that lets one easily write and run applications that process vast amounts of data ( http://hadoop.apache.org/core )
    • 65. OPEN SOURCE SOFTWARE
      • References and Related-Efforts
    • 66. REFERENCES
      • Open Source License References
        • http://www.opensource.org/licenses
        • http://openacs.org/about/licensing/open-source-licensing
      • Open Source References
        • http://www.lifehack.org/articles/technology/open-source-life-how-the-open-movement-will-change-everything.html
        • http://en.wikipedia.org/wiki/Open_source
        • http://www.opensource.org/  
      • Open Source and Public Health References
        • http://www.ibiblio.org/pjones/wiki/index.php/Open_Source_Software_for_Public_Health
        • http://en.wikipedia.org/wiki/List_of_open_source_healthcare_software
        • http://www.epha.org/a/320
        • Open Source Development for Public Health: A Primer with Examples of Existing Enterprise Ready Open Source Applications in Turner (2006)
        • A Quick Survey of Open Source Software for Public Health Organizations in Mirabito and Kass-Hout (2007)
    • 67. ARCHITECTURAL MATTERS
      • References and Related-Efforts
    • 68. REFERENCES
      • Service Oriented Architecture (or SOA)
        • 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)
        • Service-oriented Architecture in Medical Software: Promises and Perils in Nadkarni (2007)
        • Wiki sources:
          • SOA: http://en.wikipedia.org/wiki/Service_Orientated_Architecture
          • Semantic service oriented architecture: http://en.wikipedia.org/wiki/Semantic_service_oriented_architecture
      • Synchronization Architecture
        • InSTEDD’s Mesh4x: http://mesh4x.org
      • Cloud Architecture
        • Google App Engine: Google App Engine Goes Up Against Amazon Web Services in Gartner Report (2008)