6c skrøvseth data-driven analytics for decision support ehin 2014
DESCRIPTION
Stein Olav Skrøvseth Senior researcher, Norwegian Centre for Integrated Care and Telemedicine (NST) Data-driven analytics for decision support EHiN 2014, IKT-Norge og HODTRANSCRIPT
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Data-driven analytics for
decision support
Stein Olav Skrøvseth
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A rapid learning health care service
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Clinicalpractice
Knowledge
Hypotheses
Clinicaltrials
Reviews
17 Years*
Data
Synthesizedknowledge
Immediate
Reuse of data from clinical practice will enable continuous learning and translation of research results back into practice!
*Morris et al., J R Soc Med (2011)
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© 2014 International Business Machines Corporation© 2014 International Business Machines Corporation 4
Natural Language Processing
Question & Answer Technology
MachineLearning
High PerformanceComputing
UnstructuredInformationManagement
KnowledgeRepresentation
& Reasoning
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5Skrøvseth et al, Diabetes Techn. Ther. (2012)Årsand et al., J Diabetes Sci Technol (2012)
http://snow.telemed.noSkrøvseth et al., PLOS ONE (2012)
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Readmissions after index surgery
Augestad, Skrøvseth, et al, Am. Coll. Surgeons (2014)
Gastrointestinal surgery
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Challenge: data analysis
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0 100 200 300 400
Days since first entry
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Word
s / d
ay
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8000
Uniq
ue
word
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9.0
9.1
9.2
9.3
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9.6
S
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Words / day
Unique words
Entropy
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Large p, small N
“Big” data may be big in only one direction
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0
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1
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1
1
p
N
It is very easy to fit this model perfectly!
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Causality?
Correlation is not causality, but it can be a very good hint.
10Year
1999 2001 2003 2005 2007 2009
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40
60
80
10
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50
60
70
80
Fatal collisions betweencars and trains (US)
Oil exports Norway to US
Bradford Hill criteria*
Temporality, strength, consistency, specificity, gradient, plausibility, coherence, analogy, experiment.
Pearl causality†
Directed acyclic graphs (DAGs) and causal calculus.
*Lucas & McMichael, Bull WHO (2005)†Pearl, Causality (2009)
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Analytics solutions
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Statistical learning
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ClassificationClusteringRegressionDimensionality reductionCross-validation
Hastie et al., The Elements of Statistical Learning (2012)
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Anastomosis leakage
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At surgery Surgery + 4 days
Sensitivity 94% 100%
Specificity 66% 77%
Soguero-Ruiz et al., IEEE J Biomed Health Inform, Oct 2014
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Test utility
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Lots of tests are taken in healthcare.Many are unnecessary, or taken at wrong time.
Quantify the expected information content of a test at a given time in the patient’s trajectory.
Tests have different costs.Utility = information content/cost
Skrøvseth et al., AMIA Annual Symposium 2014
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17Skrøvseth et al., Visual analytics in healthcare, AMIA (2014)
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Challenge: Access to data
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Snow – Health Research Infrastructure
• Anonymized, aggregated data (N > 5)
• Access to identifiable datasets after legal and organizational clearance• Ethics committee• Data inspectorate / privacy ombudsman
• System owner committee• Patient consent
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Primary Care Hospitals
Snow
Patients
Researchers
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Goals
Improve patient treatment and safety through secondary use of patient data.
New clinical knowledge is possible through use of analytics solutions.
Immediate transfer of knowledge back to clinical practice possible through decision support. 20
Challenges
Access to data
Random correlations
Variable and unknown data quality
Sparse data
Overfitting models
Unknown confounders
Dynamic systems
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