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Towards Personalization in Global Digital Health

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The rapid expansion of mobile phone usage in low-income and middle-income countries has created unprecedented opportunities for applying AI to improve individual and population health.

In benshi.ai, a non-profit funded by the Bill and Melinda Gates Foundation, the goal is to transform health outcomes in resource-poor countries through advanced AI applications. We aim to do so by providing personalized predictions and recommendations to support diagnosis to medical care teams and frontline workers, as well as to nudge patients through personalized incentives towards an improvement in disease treatment management and general wellness.

To this end, we have built an operational machine learning platform that provides personalized content and interventions real-time. Multiple engineering and machine learning decisions have been made to overcome different challenges and to build an experimentation engine and a centralized data and model management system for global health. Databricks served as a cornerstone upon which all our data/ML services were built. In particular, MLflow and dbx (an opensource tool from Databricks) have been crucial for the training, tracking and management of our end-to-end model pipelines. From the data science perspective, our challenges involved causal inference analysis, behavioral time series forecasting, micro-randomized trials, and contextual bandits-based experimentation at the individual level.

This talk will focus on how we overcome the technical challenges to build a state-of-the-art machine learning platform that serves to improve global health outcomes.

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Towards Personalization in Global Digital Health

  1. 1. Towards Personalization in Global Digital Health 28 May 2021 África Periáñez, PhD Data + AI Summit 2021 Founder & CEO benshi.ai ai for equitable healthcare
  2. 2. Accelerating and democratizing behavioral machine learning for low- and middle- income countries to reduce health inequalities
  3. 3. Mission ▸ Provide real-time and just in time personalized incentives and recommendations to frontline health workers and patients ▸ Utilize data-driven insights and predictions for individual behaviors towards shaping strategies for collective behaviors
  4. 4. ▸ Computationally operationalize large-scale digital traces from mobile health devices ▸ Turn behavioral logs into robust personalized scientific results and move towards causal analysis beyond correlations ▸ Leverage fine-grained health logs to advance science through a better understanding of behavior and health Challenges
  5. 5. Provide interactive & actionable data-driven insights for individual and collective behaviors Apply machine learning models and experimentations Reduce global healthcare inequities Deliver personalized recommendations and incentives to users Receive frontline worker and patient data from mHealth providers & local partners Optimize healthcare systems to reach underserved populations Combine behavioral, health & contextual data 1 2 3 4 5 6 Model of Change
  6. 6. Our Global Team Our passionate team of scientists, engineers, and creative minds works closely with our partners to push the frontiers of AI and global health
  7. 7. Our Global Team 17 Team Members 14 Nationalities
  8. 8. Our Global Team LEADERSHIP STAFF MEMBERS
  9. 9. Game data science Health apps in low income settings A machine learning journey from game design to global health impact.
  10. 10. Processing layer ▸ Model training ▸ Feature engineering Machine learning module Storage layer ▸ Database ▸ Data Lake Ingestion layer ▸ Data connectors ML service ▸ Deploy ▸ Monitor ▸ Manage Experiment service ▸ Define ▸ Track ▸ Compare Scalable computation APIs / SDKs Centralized ML services mHealth provider patients frontline workers health authority data insights Frontend Dashboards Impact analysis Interactive insights Actionable recommendations
  11. 11. ▸ More than half of all maternal and newborn deaths happening due to poor quality of care ▸ Safe Delivery App, an online learning app, to improve skilled birth attendant training and support ▸ Goal: personalized and adaptive learning journey
  12. 12. ▸ Predictions on Learning Progress Among Safe Delivery App users in Ethiopia ▸ Users that predicted to make a significant progress in the learning feature are midwives
  13. 13. ▸ Predicted progress typically increases with predicted connected time ▸ Most users predicted to progress above level 5 will spend at least 3 hours using the app
  14. 14. ▸ Tracks model runs ▸ Centralized model registry API nodes Model nodes ▸ Real-time monitoring ▸ Better UX ▸ Deployment model management system ▸ Data pipelines ▸ Model training data lake log models fetch models ▸ Model management ▸ Define experiments ▸ Monitoring System Schema Frontend API API
  15. 15. Model Management System
  16. 16. ▸ dbx makes local development running on Databricks easily ▸ Easy access and manipulation of data in the cloud ▸ For production pipeline we need full control (reliable, right time, right output): dbx2 ▸ In dbx2 we added other in house customizations to suit our development needs Data Pipeline Development
  17. 17. benshi ai platform: Machine Learning
  18. 18. benshi ai platform: Statistical Analysis
  19. 19. Experimentation
  20. 20. benshi ai platform: XP Engine
  21. 21. MRTs & Contextual Bandits ▸ MRTs involve multiple randomizations and enable causal modeling of proximal effects of the randomized intervention components ▸ Evaluation of when and for whom interventions are effective, and what factors moderate the intervention effects ▸ In contextual bandits settings, the system learns the optimal decision rules and is able to adapt ▸ Bridge connecting experimentation to a continuously adapting system providing personalized and contextualized interventions
  22. 22. Summary Our goal: to boost health outcomes in resource-poor countries through personalized incentives to nudge healthcare teams and patients ▸ Machine Learning platform focusing on: individual behavioral predictions, recommendations and reinforcement learning experimentation ▸ Democratizing behavioral machine learning: ease-of-use integrations, API/SDK, to reach existing health care teams and frontline workers
  23. 23. Thank you! Do you want to join us to push the boundaries of machine learning and global health? @benshi_ai We are hiring! benshi.ai

The rapid expansion of mobile phone usage in low-income and middle-income countries has created unprecedented opportunities for applying AI to improve individual and population health. In benshi.ai, a non-profit funded by the Bill and Melinda Gates Foundation, the goal is to transform health outcomes in resource-poor countries through advanced AI applications. We aim to do so by providing personalized predictions and recommendations to support diagnosis to medical care teams and frontline workers, as well as to nudge patients through personalized incentives towards an improvement in disease treatment management and general wellness. To this end, we have built an operational machine learning platform that provides personalized content and interventions real-time. Multiple engineering and machine learning decisions have been made to overcome different challenges and to build an experimentation engine and a centralized data and model management system for global health. Databricks served as a cornerstone upon which all our data/ML services were built. In particular, MLflow and dbx (an opensource tool from Databricks) have been crucial for the training, tracking and management of our end-to-end model pipelines. From the data science perspective, our challenges involved causal inference analysis, behavioral time series forecasting, micro-randomized trials, and contextual bandits-based experimentation at the individual level. This talk will focus on how we overcome the technical challenges to build a state-of-the-art machine learning platform that serves to improve global health outcomes.

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