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

Gruendner, Julian; Schwachhofer, Thorsten; Sippl, Phillip; Wolf, Nicolas; Erpenbeck, Marcel; Gülden, Christian; Kapsner, Lorenz A.; Zierk, Jakob; Mate, Sebastian; Croner, Roland S.; Prokosch, Hans‐Ulrich; Toddenroth, Dennis

doi:10.1371/journal.pone.0225442

Cited by 10 publications

(8 citation statements)

References 1 publication

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“…They presented a streamlined architecture that facilitates predictive modeling using OMOP-CDM structured data sets and deployed the model into a clinical workflow using HL7 FHIR. More recently, Gruendner et al [ 19 ] introduced a sophisticated and comprehensive platform that included model development, deployment, and security with a graphical UI based on OMOP-CDM and HL7 FHIR standards. Unberath et al [ 31 ] suggested an operational CDSS case to predict relapses in patients with melanoma using OMOP-CDM and the REST API.…”

Section: Discussionmentioning

confidence: 99%

“…The OMOP-CDM and HL7 FHIR standards are good starting points for developing a platform that can deploy an interoperable CDSS to multiple organizations [ 18 , 19 ]. Moreover, the OMOP-CDM has acquired the status of a de facto standard in South Korea.…”

Section: Introductionmentioning

confidence: 99%

“…That is, the operative guideline informally states that each resource should contain only those data elements agreed upon by 80% or more of the participants in the development effort [ 13 , 21 ]. Because HL7 FHIR employs a web protocol, the standard is widely used to exchange information in a variety of medical settings, including those of CDSS deployments [ 10 , 18 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

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Development of an Interoperable and Easily Transferable Clinical Decision Support System Deployment Platform: System Design and Development Study

Yoo¹,

Lee²,

Min³

et al. 2022

J Med Internet Res

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Background A clinical decision support system (CDSS) is recognized as a technology that enhances clinical efficacy and safety. However, its full potential has not been realized, mainly due to clinical data standards and noninteroperable platforms. Objective In this paper, we introduce the common data model–based intelligent algorithm network environment (CANE) platform that supports the implementation and deployment of a CDSS. Methods CDSS reasoning engines, usually represented as R or Python objects, are deployed into the CANE platform and converted into C# objects. When a clinician requests CANE-based decision support in the electronic health record (EHR) system, patients’ information is transformed into Health Level 7 Fast Healthcare Interoperability Resources (FHIR) format and transmitted to the CANE server inside the hospital firewall. Upon receiving the necessary data, the CANE system’s modules perform the following tasks: (1) the preprocessing module converts the FHIRs into the input data required by the specific reasoning engine, (2) the reasoning engine module operates the target algorithms, (3) the integration module communicates with the other institutions’ CANE systems to request and transmit a summary report to aid in decision support, and (4) creates a user interface by integrating the summary report and the results calculated by the reasoning engine. Results We developed a CANE system such that any algorithm implemented in the system can be directly called through the RESTful application programming interface when it is integrated with an EHR system. Eight algorithms were developed and deployed in the CANE system. Using a knowledge-based algorithm, physicians can screen patients who are prone to sepsis and obtain treatment guides for patients with sepsis with the CANE system. Further, using a nonknowledge-based algorithm, the CANE system supports emergency physicians’ clinical decisions about optimum resource allocation by predicting a patient’s acuity and prognosis during triage. Conclusions We successfully developed a common data model–based platform that adheres to medical informatics standards and could aid artificial intelligence model deployment using R or Python.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Development of an Interoperable and Easily Transferable Clinical Decision Support System Deployment Platform: System Design and Development Study

Yoo¹,

Lee²,

Min³

et al. 2022

J Med Internet Res

View full text Add to dashboard Cite

show abstract

“…Using this data set, we implemented three different sample medical questions to investigate the capabilities of the implemented web-based preprocessing service. Furthermore, we integrated the service into the locally deployed web-based analysis platform KETOS [ 20 ], which enables data retrieval and analysis using Jupyter Notebooks [ 21 ] within the hospital, thus respecting a patients’ privacy and allowing the data custodians to have ownership of the data.…”

Section: Introductionmentioning

confidence: 99%

A Framework for Criteria-Based Selection and Processing of Fast Healthcare Interoperability Resources (FHIR) Data for Statistical Analysis: Design and Implementation Study

Gruendner¹,

Gülden²,

Kampf³

et al. 2021

JMIR Med Inform

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Background The harmonization and standardization of digital medical information for research purposes is a challenging and ongoing collaborative effort. Current research data repositories typically require extensive efforts in harmonizing and transforming original clinical data. The Fast Healthcare Interoperability Resources (FHIR) format was designed primarily to represent clinical processes; therefore, it closely resembles the clinical data model and is more widely available across modern electronic health records. However, no common standardized data format is directly suitable for statistical analyses, and data need to be preprocessed before statistical analysis. Objective This study aimed to elucidate how FHIR data can be queried directly with a preprocessing service and be used for statistical analyses. Methods We propose that the binary JavaScript Object Notation format of the PostgreSQL (PSQL) open source database is suitable for not only storing FHIR data, but also extending it with preprocessing and filtering services, which directly transform data stored in FHIR format into prepared data subsets for statistical analysis. We specified an interface for this preprocessor, implemented and deployed it at University Hospital Erlangen-Nürnberg, generated 3 sample data sets, and analyzed the available data. Results We imported real-world patient data from 2016 to 2018 into a standard PSQL database, generating a dataset of approximately 35.5 million FHIR resources, including “Patient,” “Encounter,” “Condition” (diagnoses specified using International Classification of Diseases codes), “Procedure,” and “Observation” (laboratory test results). We then integrated the developed preprocessing service with the PSQL database and the locally installed web-based KETOS analysis platform. Advanced statistical analyses were feasible using the developed framework using 3 clinically relevant scenarios (data-driven establishment of hemoglobin reference intervals, assessment of anemia prevalence in patients with cancer, and investigation of the adverse effects of drugs). Conclusions This study shows how the standard open source database PSQL can be used to store FHIR data and be integrated with a specifically developed preprocessing and analysis framework. This enables dataset generation with advanced medical criteria and the integration of subsequent statistical analysis. The web-based preprocessing service can be deployed locally at the hospital level, protecting patients’ privacy while being integrated with existing open source data analysis tools currently being developed across Germany.

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“…These integrated data sets are analyzed with open-source analysis tools and frameworks to generate or validate hypotheses. Our proposed system extends GEMINI with multiple web services and a user interface to support the whole data flow as well as the subsequent integration with the already established FHIR [ 9 ] data repository, KETOS [ 16 ], and DataSHIELD [ 17 ] platforms for statistical analysis. To validate our approach, we implemented and applied this data analysis pipeline to data from the 1000 Genomes Project, which yielded highly consistent results.…”

Section: Introductionmentioning

confidence: 99%

Integrating Genomics and Clinical Data for Statistical Analysis by Using GEnome MINIng (GEMINI) and Fast Healthcare Interoperability Resources (FHIR): System Design and Implementation

Gruendner¹,

Wolf²,

Tögel³

et al. 2020

J Med Internet Res

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Background The introduction of next-generation sequencing (NGS) into molecular cancer diagnostics has led to an increase in the data available for the identification and evaluation of driver mutations and for defining personalized cancer treatment regimens. The meaningful combination of omics data, ie, pathogenic gene variants and alterations with other patient data, to understand the full picture of malignancy has been challenging. Objective This study describes the implementation of a system capable of processing, analyzing, and subsequently combining NGS data with other clinical patient data for analysis within and across institutions. Methods On the basis of the already existing NGS analysis workflows for the identification of malignant gene variants at the Institute of Pathology of the University Hospital Erlangen, we defined basic requirements on an NGS processing and analysis pipeline and implemented a pipeline based on the GEMINI (GEnome MINIng) open source genetic variation database. For the purpose of validation, this pipeline was applied to data from the 1000 Genomes Project and subsequently to NGS data derived from 206 patients of a local hospital. We further integrated the pipeline into existing structures of data integration centers at the University Hospital Erlangen and combined NGS data with local nongenomic patient-derived data available in Fast Healthcare Interoperability Resources format. Results Using data from the 1000 Genomes Project and from the patient cohort as input, the implemented system produced the same results as already established methodologies. Further, it satisfied all our identified requirements and was successfully integrated into the existing infrastructure. Finally, we showed in an exemplary analysis how the data could be quickly loaded into and analyzed in KETOS, a web-based analysis platform for statistical analysis and clinical decision support. Conclusions This study demonstrates that the GEMINI open source database can be augmented to create an NGS analysis pipeline. The pipeline generates high-quality results consistent with the already established workflows for gene variant annotation and pathological evaluation. We further demonstrate how NGS-derived genomic and other clinical data can be combined for further statistical analysis, thereby providing for data integration using standardized vocabularies and methods. Finally, we demonstrate the feasibility of the pipeline integration into hospital workflows by providing an exemplary integration into the data integration center infrastructure, which is currently being established across Germany.

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

Cited by 10 publications

References 1 publication

Development of an Interoperable and Easily Transferable Clinical Decision Support System Deployment Platform: System Design and Development Study

Development of an Interoperable and Easily Transferable Clinical Decision Support System Deployment Platform: System Design and Development Study

A Framework for Criteria-Based Selection and Processing of Fast Healthcare Interoperability Resources (FHIR) Data for Statistical Analysis: Design and Implementation Study

Integrating Genomics and Clinical Data for Statistical Analysis by Using GEnome MINIng (GEMINI) and Fast Healthcare Interoperability Resources (FHIR): System Design and Implementation

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