Repository for Disease Progression Modeling workbench 360 - An end-to-end deep learning model training framework in python on OMOP data
APACHE-2.0 License
Repository for Disease Progression Modeling workbench 360 - An end-to-end deep learning model training framework in python on OHDSI-OMOP data
Overview and YouTube demonstration are available here. License, Contribution, Publications are also available there.
DPM360 components are interoperable but can also work as independent tools. DPM360 is typically installed over a cluster that sets up a number of interconnected micro-services. We can broadly divide the components into two groups viz (i) components that are concerned with micro service setups and (ii) standalone python packages providing core functional capabilities, having separate installation procedures. Please see the guides below to install each component.
One of the key micro-service utilties is installer that sets up an OHDSI stack (Atlas, WebAPI, a Postgres Database, and Achilles) into a cloud cluster such as Kubernetes or OpenShift. See installation guide for details. Its Express Installation Script section provides minimum setup operations. You also follow non-cloud-cluster setup if you want to try OHDSI stack without using a cluster. Using this component:
The service builder component packages and deploys the learned models to the target cloud cluster. See installation guide for details. Using this component:
The lightsaber component is an extensible Python training framework which provides blueprints for the development of disease progression models. See installation guide. Also see user guide for data loading and training details. Using this component:
The cohort tools component provides python scripts to extract features from cohorts defined via ATLAS or custom queries. It enables integration with lightsaber to use features extracted from OHDSI databases. Using this component: