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Resilient machine learning systems for health analytics

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Tech companies are shifting more towards machine learning or AI-first strategy. What does that mean to them? What does that mean to us? Growing number of aging population and shrinking funding are putting enormous pressure to the overall healthcare system for keeping up with the desired quality of care with limited resources. Thus, there is an increasing focus on machine learning capabilities for just-in-time alerts to predict various future events so that undesirable incidents can be reduced by giving attention to the right people at the right time. This talk explores the underlying framework behind such capabilities, various strategies for a resilient system, and the role of a machine learning platform.

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Resilient machine learning systems for health analytics

  1. 1. Resilient Machine Learning Systems for Health Analytics Mahfuzul Haque @mahfuzul2012 24/12/2016
  2. 2. Challenges Government Care Giver Care Receiver Funding Cost of service Number of care receiver Quality of care 24/12/2016 2
  3. 3. http://www.zdnet.com/article/samsung-deakin-uni-to-trial-in-home-a-i-aged-care-technology 24/12/2016 3
  4. 4. Just-in-time Alerts! 17 Number of falls in last 7 days 23 Likelihood of falls 24/12/2016 4
  5. 5. www.electronicsweekly.com/news/research-news/sensors-predict-elderly-falls-without-wearables-2016-08/ 24/12/2016 5
  6. 6. http://www.mobihealthnews.com/39851/wearable-tech-could-more-accurately-predict-falls-assess-ms-disease-state 24/12/2016 6
  7. 7. Fall prediction based on clinical data Morse Fall Scale (1985) – History of falling – Secondary diagnosis – Use of ambulatory aid – Intravenous therapy – Gait – Mental status Hendrich II Fall Risk Model (2003) Johns Hopkins Fall Risk Assessment Tool (2005) http://www.griswoldhomecare.com/blog/fall-risk-assessment-tools-whats-your-risk/ 24/12/2016 7
  8. 8. Machine Learning Pipeline Get the Data Explore, clean, and refine the data Build and evaluate the prediction model Publish the model API Keep updating the model 24/12/2016 8
  9. 9. Model Building: Strategy 1 Data (org 1) Data (org 2) Data (org 3) Combine data Clean and refine data Build and evaluate model Model API for all organizations 24/12/2016 9
  10. 10. Model Building: Strategy 2 Data (org 1) Clean and refine data Build and evaluate model Model API for org 1 Data (org 2) Clean and refine data Build and evaluate model Model API for org 2 Data (org 3) Clean and refine data Build and evaluate model Model API for org 3 24/12/2016 10
  11. 11. Things to consider • Same model for all organizations • Different model for each organization • Model for new organization • Different product versions • Scaling – storage, processing, API end-points • Where will the pipeline execute? • Predicting multiple variables 24/12/2016 11
  12. 12. Machine Learning Platforms http://www.ibm.com/cloud-computing/bluemix https://aws.amazon.com/machine-learning https://azure.microsoft.com/ml 24/12/2016 12
  13. 13. Let’s dive into Azure Machine Learning 24/12/2016 13
  14. 14. Let’s dive into Azure Machine Learning 24/12/2016 14
  15. 15. Forming a cross-functional team • Problem solving • Domain experts / SMEs • Machine learning • Cloud / architecture • DevOps 24/12/2016 15
  16. 16. Key Lessons • Bring in expertise by forming the right cross-functional team. • Choose a suitable cloud-based machine learning platform (e.g., Azure ML). • Keep experimenting. • Keep improving. 24/12/2016 16
  17. 17. Thank You! 24/12/2016 17

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