Biosurveillance: Machine Learning And Disease Surveillance by Kass-Hout Di Tada

Taha Kass-Hout, MD, MS Nicolás di Tada October 2008 MACHINE LEARNING AND DISEASE SURVEILLANCE

Image source: http://www.birds.cornell.edu/crows/images/deadcrow.jpg Image source: http://farm3.static.flickr.com/2029/2239605500_6ef2fd2295.jpg?v=0

LATE DETECTION – RESPONSE DAY CASES Opportunity for control

EARLY DETECTION AND RESPONSE DAY CASES Opportunity for control

INFORMATION SOURCES

Event-based – ad-hoc unstructured reports issued by formal or informal sources

Indicator-based – (number of cases, rates, proportion of strains…)

PUBLIC HEALTH MEASURES

Representativeness

Completeness

Predictive Value

Timeliness

PUBLIC HEALTH MEASURES 1000 Malaria infections (100%) 50 Malaria notifications (5%) Specificity / Reliability Sensitivity / Timeliness

Main attributes

Representativeness

Completeness

Predictive value positive

Get as close to the bottom of the pyramid as possible Urge frequent reporting: Weekly  daily  immediately

PUBLIC HEALTH MEASURES Analyze and interpret Automated analysis/ thresholds Time

Main attributes

Timeliness

Health care hotline Signal as early as possible

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

PUBLIC HEALTH – TWO PERSPECTIVES

Case management

Individual cases of notifiable diseases

Relationship networks (contact tracing)

Population surveillance

Larger risk patterns

CASE MANAGEMENT

Questions/problems:

Is a case due to recent transmission?

If so, does the case share any feature with other, recent cases?

Ways it's being done:

Investigations/interviews

Meeting with other investigators

POPULATION SURVEILLANCE

Questions/problems:

Are more cases happening than expected?

Does an excess suggest ongoing transmission in a specific region?

Way it's being done:

Semi-automated routine temporal and space-time statistical analysis

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 as this..

Than by looking at this..

WHY LOCATION MATTERS – POP SURVEILLANCE

If you are studying the spatial distribution of a set of disease clusters

This would seem more difficult..

Than this..

MODERN DISEASE SURVEILLANCE

In the past two decades, much disease surveillance research has focused on developing analytical methods for automatically detecting anomalous patterns in data

Modern methods can achieve timely detection of anomalies by incorporating temporal , spatial , and multivariate information

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 Detection algorithm “ What are we supposed to do with this?” Too many alerts

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

ADVANTAGES OF MACHINE LEARNING P(malaria) = 22% P(influenza) = 13% P(other ILI) = 33%

MACHINE LEARNING TECHNIQUES

Classifiers

Clustering

Bayesian Statistics

Neural Networks

Genetic Algorithms

HOW TO REPRESENT A DOCUMENT? “ This morning I woke up with fever, I might have a flu.” “ I had a flu last month. […] I had a flu early this week.” flu fever

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

CLASSIFIERS - SVM

SVM – MARGIN MAXIMIZATION

Support vectors define the separator

SVM – NON LINEAR? Φ : x -> φ ( x ) Map to higher-dimension space

SVM – FILTERING OR CLASSIFYING Document 1 Document 2 Document 3 Positives Negatives Training Document Training Document Classifier

CLUSTERING – PROBLEM DEFINITION

Map items to vectors (Feature extraction)

Normalization

Agglomerative and Partitional

CLUSTERING - AGGLOMERATIVE

CLUSTERING - PARTITIONAL

BAYESIAN STATISTICS Probability of disease A (flu) once symptoms B (fever) are observed Probability of fever once flu is confirmed Probability of flu (prior or marginal) Probability of fever (prior or marginal)

NEURAL NETWORKS

Given a set of stimulus, train a system to produce a given output

NEURAL NETWORKS - STRUCTURE Hidden Layer Output Layer Input Layer […] […] {I 0 ,I 1 ,……I n } {O 0 ,O 1 ,……O n } Weight

NEURAL NETWORK - APPLICATION Event?

GENETIC ALGORITHM - BASICS

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

GENETIC ALGORITHM – MODEL

Model the transmission process using a set of parameters:

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)

GENETIC ALGORITHM – MODEL FITNESS Fitness = 1/Area

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

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 )

RESULTS – IMPROVED SURVEILLANCE

Q&A

THANK YOU!

Taha Kass-Hout, MD, MS

http://www.instedd.org

[email_address]

http://taha.instedd.org

Nicolás di Tada

http://www.manas.com.ar

[email_address]

http://weblogs.manas.com.ar/ndt/

BACKUP SLIDES

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

REFERENCES

Algorithms

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)

REFERENCES

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)

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)

RELATED PROJECTS

InSTEDD RNA (or Event Evolution): 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

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/

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/