4.1 - WANDA: An End-to-End Remote Health Monitoring and Analytics System for Heart Failure Patients.


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Wednesday, October 24, 2012
Technical Session #4

Rahav Dor (Washington University in St. Louis, US), Chenyang Lu (Washington University in St. Louis, US), Gregory Hackmann (Washington University in St. Louis, US)

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4.1 - WANDA: An End-to-End Remote Health Monitoring and Analytics System for Heart Failure Patients.

  1. 1. Mars Lan, Lauren Samy, Nabil Alshurafa, Myung-kyung Suh,Hassan Ghasemzadeh, Aurelia Macabasco-OConnell and Majid Sarrafzadeh Wireless Health Institute, UCLA, USA School of Nursing, UCLA, USA
  2. 2. Outline• Background• Related Work• WANDA system architecture• Clinical Trials• HF Symptom prediction & results• Conclusion Copyright: UCLA Wireless Health Institute 2
  3. 3. Background • Heart Failure (HF) is one of the leading causes of death in US and around the world (Lloyd-Jones, 2010) • 25% of patients treated for HF are re-hospitalized within 30 days. 50% readmitted within 6 months (Ross, 2010) • $17b/year in US. 20% of total Medicare expenditure in acute hospital care • Remote health monitoring offers a promising solution • Must be low cost, robust, and scalable • More than just collecting, storing, and viewing sensor data – need to have the “smart” to analyze data automaticallyD. Lloyd-Jones et al., "Executive summary: heart disease and stroke statistics--2010 update: a report from the American Heart Association,"Circulation, vol. 121, p. 948, 2010.J. S. Ross et al., "Recent national trends in readmission rates after heart failure hospitalization," Circulation: Heart Failure, vol. 3, pp. 97-103, 2010. Copyright: UCLA Wireless Health Institute 3
  4. 4. Related Work• Chaudhry’s telemonitoring study (Chaudhry, 2007) • Daily phone call to automated telemonitoring system over 6 months • Manually enter weight & responses to question using phone’s keypad • Unsuccessful in reducing risk of readmission or death for HF patients• Soran’s HF Study (Soran, 2010) • Weight + daily questions • Manual data entry via phone • Failed to show improvement due to poor adherence• Home or Hospital in Heart Failure study (Mortara, 2009) • Weight, heart rate, blood pressure, questionnaire, ECG • Manual data entry via phone and transmit only once every week • Higher compliance (81%) but still show no improvementS. I. Chaudhry et al., "Randomized trial of telemonitoring to improve heart failure outcomes (Tele-HF)," J of cardiac failure, 13, 709-714, 2007.O. Z. Soran et al., "Cost of medical services in older patients with heart failure: those receiving enhanced monitoring using a computer-basedtelephonic monitoring system compared with those in usual care" Journal of cardiac failure, vol. 16, pp. 859-866, 2010.A. Mortara, et al., "Home telemonitoring in heart failure patients: the HHH study (Home or Hospital in Heart Failure)," European journal ofheart failure, vol. 11, pp. 312-318, 2009. Copyright: UCLA Wireless Health Institute 4
  5. 5. Related Work• Problems with previous studies (Desai, 2010) • Poor adherence: asking patients to perform too many tasks • Slow and unreliable transmission: phone line • Unreliable measurement: manual data entry • Not capturing the important markers: weight & questionnaire alone may not be enough • Lack of advanced algorithm to predict adverse events• How does WANDA address these issues? • Automate the monitoring process as much as possible, from measurement, transmission, to notification • Support multiple sensors and gathering of a wide range of bodily measurements • Provide data analytics engine for developing event prediction algorithmsA. S. Desai and L. W. Stevenson, "Connecting the circle from home to heart-failure disease management," New England Journal ofMedicine, vol. 363, pp. 2364-2367, 2010. Copyright: UCLA Wireless Health Institute 5
  6. 6. WANDA System Architecture• Data collection • Collect data from heterogeneous wireless sensors • Smartphone-based gateway & tablet-based portal• Data storage & access • Scalable database & indexing • Easy-to-use access interface• Analytics engine • Wealth of analytics algorithms • Data preprocessing, classification, clustering, rule/pattern mining • Support offline & online analysis• Supporting tools • Administrative portal • Questionnaire • Social network app Copyright: UCLA Wireless Health Institute 6
  7. 7. WANDA System Architecture Copyright: UCLA Wireless Health Institute 7
  8. 8. Smartphone-based Data Collection• Runs on most Android phones• Collect data from Bluetooth- enabled sensors• Transmit data to central repository via WiFi or cellular connections• Built-in activity monitoring, fall detection, and pedometer• Immediate feedback and data visualization• Questionnaire and reminder Copyright: UCLA Wireless Health Institute 8
  9. 9. Caregiver Portal Tablet Copyright: UCLA Wireless Health Institute 9
  10. 10. Data Analytics• Core strength of WANDA over other remote monitoring systems• Provide a wealth of machine learning and data mining algorithms• Offline analysis • Data downloaded and analyzed • Statistical models generated and validated• Online analysis • Based on generated models to predict events from incoming data • Use temporary storage and PubSub interface• Fully integrated with Weka framework Copyright: UCLA Wireless Health Institute 10
  11. 11. Storage and Access• Amazon S3 for raw data storage • Reliable, secure, and scalable• Amazon SimpleDB for data indexing • Schemaless: extensible and ideal for incomplete data (e.g. medical record) • High performance and scalable• RESTful web interface • Decouple backend database technology from developers • Simple pseud object oriented access • HTTP lib widely available on most platforms• Publisher-Subscriber (PubSub) interface • Real-time notification via email, SMS, or HTTP • Clients can subscribe to fine- or coarse-grained changes • Supports multiple publishers & subscribers • Ideal for events, alarms, and real-time analytics Copyright: UCLA Wireless Health Institute 11
  12. 12. Supporting Tools• Administrative portal • Web-based tool for study & patient information management • Labeling, annotation, adverse event tracking • Rich data visualization, graphing, and searching• Questionnaire system • Questions specified by clinicians and pushed to patient’s phone • Patients are reminded on daily basis • Reponses automatically stored in the database• Social network • Facebook application • Encourage healthy behavior and compliances • Personal goals & competition Copyright: UCLA Wireless Health Institute 12
  13. 13. Current & Completed Clinical TrialsType # of Subjects Institutes StatusParolee study 600 UCLA Nursing School Completed(questionnaires, remotecoaching, SMS)Male contraceptive 300 Harbor Medical Current(questionnaires,compliance via SMS)Congestive Heart Failure 20 (pilot) UCLA, UCSF, UCI, CompletedMonitoring 20 (extension) UC Davis Completed(weight, BP, activity, 1500 Currentquestionnaires) Copyright: UCLA Wireless Health Institute 13
  14. 14. Current & Completed Clinical TrialsType # of Subjects Institutes StatusDiabetes 50 UCLA Medical Center Current(blood glucose, activity)Weight loss 20 UCLA Medical Center Completed(activity, questionnaires,social networking)Cardiovascular Disease 60 UCLA Nursing School Current(activity, weight, BP,questionnaires) Copyright: UCLA Wireless Health Institute 14
  15. 15. HF Symptom Prediction• Daily weight change (DWC) is a commonly used predictor for worsening of HF symptoms • Recommended by American College of Cardiology/American Health Association (Hunt, 2001)• Several studies have raised doubt about DWC’s poor correlation with worsening of HF symptoms (Chaudhry, 2007; Lewin, 2005; Zhang, 2009)• Experienced many false alarms based on DWC > 2lb during our clinical trials• False-Positive (added load to the nurses)• False-Negative (may eventually lead to readmission) Copyright: UCLA Wireless Health Institute 15
  16. 16. HF Symptom Prediction• Example for “normal” patient • DWC > 2lb (false-positive) • Stable BP over 7-day period 160 250 SBP DBP 140 Weight 245 120 Blood Pressure (mmHg) 100 240 Weight (lb) 80 235 60 40 230 20 0 225 6/13/11 6/14/11 6/15/11 6/16/11 6/17/11 6/18/11 6/19/11 6/20/11 6/21/11 Copyright: UCLA Wireless Health Institute 16
  17. 17. Experiment Setup• Real data from clinical trial over 3 months • 16 patients with daily weight, BP, questionnaires• Patient self-reported worsening of HF symptom cross- checked with nurse’s phone log• Total of 34 positive instances• Nine features extracted • Daily change in weight, systolic & diastolic BP • 3-day standard deviation of weight, systolic & diastolic BP • 7-day standard deviation of weight, systolic & diastolic BP• Ensuring fairness • Equal number of positive & negative instances • 10-fold validation to prevent overfitting • Average result over 10 runs to reduce effect of outliers Copyright: UCLA Wireless Health Institute 17
  18. 18. WANDA Algorithms• Naïve Bayes (NBC)• Nearest Neighbor (NN)• Logistic Regression (LR)• Voting Feature Interval (VFI)• Ripple-Down Rule Learner (RIDOR)• C4.5 Decision Tree (C4.5)VS.• Daily Weight Change thresholding (> 2lb) Copyright: UCLA Wireless Health Institute 18
  19. 19. Accuracy, Sensitivity, and Specificity• Poor results for DWC • Accuracy: 51.9% cf. random guess: 50% • Sensitivity: 15.9% (missed many positives) • Specificity: 71.4% (mis- classified many negatives)• WANA algorithms • Accuracy: 70-74% • Sensitivity: At least 45% better than DWC • Specificity: 70-87% • NBC, LR, and RIDOR performs best overall Copyright: UCLA Wireless Health Institute 19
  20. 20. ROC Curve 1 0.9 0.8 0.7 0.6 TPR 0.5 NBC 0.4 kNN LR 0.3 VFI RIDOR 0.2 C4.5 0.1 DWC 0 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 FPR• DWC is only slightly better than random guess with Area Under Curve (AUC) = 0.573• All algorithms from WANDA performs significantly better with LR achieves largest AUC (0.817)• NBC can be tuned to FPR = 0 with a respectable TPR = 0.652 Copyright: UCLA Wireless Health Institute 20
  21. 21. Error Bound Analysis• Is 74% accuracy good/bad? What’s the “optimal” accuracy?• The error rate of any binary classifier is lower-bounded by the Bayes Error Rate: • R1 & R2 are the regions where x is misclassified • Class distributions w1 & w2 can only be estimated• 2 x Bayes Error Rate ≥ 1-NN Error Rate • 1-NN Error Rate = 0.348 (based on empirical results), Bayes Error ≥ 0.174• Theoretical max accuracy = 82.6% • Within 10% of WANDA’s results Copyright: UCLA Wireless Health Institute 21
  22. 22. Conclusion• Design and implementation of WANDA • Smartphone-based gateway for data collection & transmission • Scalable data storage with RESTful web interface & PubSub real-time notification • Analytics engine capable of prognostic prediction using machine learning & data mining algorithms• Prediction of worsening HF symptom using WANDA data analytics engine • Based on real data gathered from clinical trials • Identified 3-day & 7-day BP fluctuation as better predictors than DWC • Improved prediction accuracy by 20% & sensitivity by 45% over thresholding algorithm based on DWC • 9% lower than the estimated upper bound Copyright: UCLA Wireless Health Institute 22