KETOS: Clinical decision support and machine learning as a service – A training and deployment platform based on Docker, OMOP-CDM, and FHIR Web Services

dc.contributor.authorGruendner, Julian
dc.contributor.authorSchwachhofer, Thorsten
dc.contributor.authorSippl, Phillip
dc.contributor.authorWolf, Nicolas
dc.contributor.authorErpenbeck, Marcel
dc.contributor.authorGulden, Christian
dc.contributor.authorKapsner, Lorenz A.
dc.contributor.authorZierk, Jakob
dc.contributor.authorMate, Sebastian
dc.contributor.authorStürzl, Michael
dc.contributor.authorCroner, Roland
dc.contributor.authorProkosch, Hans-Ulrich
dc.contributor.authorToddenroth, Dennis
dc.description.abstractBackground and objective To take full advantage of decision support, machine learning, and patient-level prediction models, it is important that models are not only created, but also deployed in a clinical setting. The KETOS platform demonstrated in this work implements a tool for researchers allowing them to perform statistical analyses and deploy resulting models in a secure environment. Methods The proposed system uses Docker virtualization to provide researchers with reproducible data analysis and development environments, accessible via Jupyter Notebook, to perform statistical analysis and develop, train and deploy models based on standardized input data. The platform is built in a modular fashion and interfaces with web services using the Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) standard to access patient data. In our prototypical implementation we use an OMOP common data model (OMOP-CDM) database. The architecture supports the entire research lifecycle from creating a data analysis environment, retrieving data, and training to final deployment in a hospital setting. Results We evaluated the platform by establishing and deploying an analysis and end user application for hemoglobin reference intervals within the University Hospital Erlangen. To demonstrate the potential of the system to deploy arbitrary models, we loaded a colorectal cancer dataset into an OMOP database and built machine learning models to predict patient outcomes and made them available via a web service. We demonstrated both the integration with FHIR as well as an example end user application. Finally, we integrated the platform with the open source DataSHIELD architecture to allow for distributed privacy preserving data analysis and training across networks of hospitals. Conclusion The KETOS platform takes a novel approach to data analysis, training and deploying decision support models in a hospital or healthcare setting. It does so in a secure and privacy-preserving manner, combining the flexibility of Docker virtualization with the advantages of standardized vocabularies, a widely applied database schema (OMOP-CDM), and a standardized way to exchange medical data (FHIR).en
dc.identifier.citationPLoS ONE 14.10 (2019): e0223010. <>
dc.subjectMachine learning
dc.subjectMachine learning algorithms
dc.subjectColorectal cancer
dc.subjectStatistical data
dc.subject.ddcDDC Classification::6 Technik, Medizin, angewandte Wissenschaften :: 61 Medizin und Gesundheit :: 610 Medizin und Gesundheit
dc.titleKETOS: Clinical decision support and machine learning as a service – A training and deployment platform based on Docker, OMOP-CDM, and FHIR Web Servicesen
dcterms.publisherFriedrich-Alexander-Universität Erlangen-Nürnberg (FAU)
local.journal.titlePLoS ONE
local.subject.fakultaetMedizinische Fakultät
local.subject.sammlungUniversität Erlangen-Nürnberg / Von der FAU geförderte Open Access Artikel / Von der FAU geförderte Open Access Artikel 2019
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