1. 2014 A Statistical Clinical Decision Support Tool in Telemonitoring Using Predictive Analytics © Celeste Fralick, Ph.D. Principal Engineer, Chief Data Scientist Analytic Products & Technologies Internet of Things Group Intel Corporation #GHC14 2014

2. Agenda  Challenge and Approach  Segregating Clinical & Statistical Thresholds  Classification Model  Predicting Future Episodes  Summary & Future Studies

3. Challenge & Approach Provide clinician with robust statistical thresholds using Statistical Process Control (SPC) & Western Electric Rules Patient’s own data will drive thresholds; normal variation seen Use predictive analytics to forecast the probable classification Predict next day’s vital sign reading and whether it’s statistically normal for that patient Provide opportunity for clinicians to intervene earlier, lower costs & hospital readmissions, and improve patient outcomes. Elder with COPD or CHFClinicians monitoring vitals Broadband & Private Cloud Clinician derived thresholds colors

4. Clinician Derived Threshold Forced Expiratory Volume in 1 sec in L (FEV1, P#18) Date in Time Series (7/09-7/10) Peak Flow with Two Thresholds (Clinician & Statistical) InterventionTestHypState Statistically Derived State Clinician Derived Combined State Result IntervenedTPH0H0 1 Threshold violation1 1,14 Did not intervene but should have (statistically)FNH1H1 1 Threshold violation0 No threshold violation1,04 Intervened but shouldn't have (statistically)FPH1H1 0 No threshold violation1 Threshold violation0,14 No intervention necessaryTNH0H0 0 No threshold violation0 0,047 Rule Point Location Detection 1 One point beyond Zone A Detects a shift in the mean, an increase in the standard deviation, or a single aberration in the process. 2 Nine points in a row in a single (upper or lower) side of Zone C or beyond Detects a shift in the process mean. Confusion matrix & ROC available FP FN TPTP

5. ParameterResult Disease SeveritySevere Mean / N 0.884 / 59 UCL / LCL 1.343 / 0.425 Std Dev0.883525 Variance0.83 Skewness / Kurtosis 0.03458/ 0.593806 DistributionNormal 2 Mixture Training Gen R Square 0.875022 Validation Gen R Square 0.938169 (Polynomial Shift) Date in Time Series (7/09-7/10)

6. σ edge misclassifications Deeper analytics provide deeper insight: Pre- & post predictive algorithm of SPC zones generally agree Severe & Very Severe COPD patients provide better predictive models Mean FEV1 FP → FN % negligible increase for moderate COPD

7. DiseaseWeightBlood pressure FEV1PEFSpO2Pulse CHFIncreases Decreases Increases/ arrhythmia COPDIncreases in severe cases IncreasesDecreases Increases/ arrhythmia

8. 2014 Summary  Provided Clinical Decision Support using elder’s own vitals rather than generalizing  By applying a unique neural net algorithm, able to classify successfully  Personalized prediction of vital sign may impact disease outcome, re-hospitalization

9. 2014 Future Studies  Additional vital signs for specific disease  Additive algorithms to increase fit and accuracy  Reduce sources of errors via tightly controlled clinical study  Consider better neural net penalty to address overfitting 4/16/2013crf9

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11. 2014 Backup

12. Investigated a random K-fold (5) for predicting values & classification – Cross-validation technique for small sample sizes in neural networks 4/16/2013crf12 Random K-Fold Data TR VAL TP FN FP TN TR X- bar xval 5x VAL X- bar Act Func Act Func Nodes Transformation Responses Act Func Input