AWS HealthLake integrates with AWS analytics services. For example, see how HAQM QuickSight can deploy interactive dashboards to analyze your population data in this blog. HealthLake also integrates with ML services like HAQM SageMaker for developers and data scientists to build, train, and deploy their own predictive analytics using machine learning models. For example, see how you can build disease predictive models using HAQM SageMaker with AWS HealthLake normalized data. Clinicians can also use web or mobile application dashboards to view the results of custom or pre-built models. Using HAQM Neptune and HAQM Kendra, you can also build an ML-enabled cognitive search application where clinical evidence is tagged, indexed, and structured to provide evidence-based information on topics like transmission, risk factors, therapeutics, and incubation.