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

English version

Autoři: Julian Gruendner ^aff001; Thorsten Schwachhofer ^aff001; Phillip Sippl ^aff001; Nicolas Wolf ^aff001; Marcel Erpenbeck ^aff001; Christian Gulden ^aff001; Lorenz A. Kapsner ^aff002; Jakob Zierk ^aff002; Sebastian Mate ^aff002; Michael Stürzl ^aff004; Roland Croner ^aff005; Hans-Ulrich Prokosch ^aff001; Dennis Toddenroth ^aff001
Působiště autorů: Chair of Medical Informatics, Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Erlangen, Germany ^aff001; Medical Centre for Information and Communication Technology, Universitätsklinikum Erlangen, Erlangen, Germany ^aff002; Department of Pediatrics and Adolescent Medicine, UniversitätsklinikumErlangen, Erlangen, Germany ^aff003; Department of Surgery, Division of Molecular and Experimental Surgery, Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Erlangen, Germany ^aff004; Department of General, Visceral, Vascular and Graft Surgery, University Hospital, Magdeburg, Germany ^aff005
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pone.0223010

Souhrn

Background 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).

Klíčová slova:

Physicians – Hemoglobin – Statistical data – Machine learning algorithms – Machine learning – Preprocessing – Prototypes – Consortia

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