Visualizing Clinical Data Retrieval and Curation in Multimodal Healthcare AI Research: A Technical Note on RIL-workflow.
Curating and integrating data from sources are bottlenecks to procuring robust training datasets for artificial intelligence (AI) models in healthcare. While numerous applications can process discrete types of clinical data, it is still time-consuming to integrate heterogenous data types. Therefore,...
| Publicado en: | Journal of Digital Imaging Vol. 37; no. 3; pp. 1239 - 1248 |
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| Autores principales: | , , , |
| Formato: | pictorial tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=178678182&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 178678182 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Jun2024 vid: 37 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 178678182 178678182 178678182 10.1007/s10278-024-00977-3 178678182 ppf: 1239 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Visualizing Clinical Data Retrieval and Curation in Multimodal Healthcare AI Research: A Technical Note on RIL-workflow. aug: au: Ganjizadeh, Ali Zawada, Stephanie J. Langer, Steve G. Erickson, Bradley J. affil: Mayo Clinic Artificial Intelligence Laboratory, 200 1st Street SW, 55902, Rochester, MN, USA sug: subj: Research, Medical Artificial Intelligence Utilization Information Retrieval Methods Information Management Methods Research Methodology Health Information Systems Database Design Software Design Workflow Automation Electronic Data Interchange Graphical User Interface ab: Curating and integrating data from sources are bottlenecks to procuring robust training datasets for artificial intelligence (AI) models in healthcare. While numerous applications can process discrete types of clinical data, it is still time-consuming to integrate heterogenous data types. Therefore, there exists a need for more efficient retrieval and storage of curated patient data from dissimilar sources, such as biobanks, health records, and sensors. We describe a customizable, modular data retrieval application (RIL-workflow), which integrates clinical notes, images, and prescription data, and show its feasibility applied to research at our institution. It uses the workflow automation platform Camunda (Camunda Services GmbH, Berlin, Germany) to collect internal data from Fast Healthcare Interoperability Resources (FHIR) and Digital Imaging and Communications in Medicine (DICOM) sources. Using the web-based graphical user interface (GUI), the workflow runs tasks to completion according to visual representation, retrieving and storing results for patients meeting study inclusion criteria while segregating errors for human review. We showcase RIL-workflow with its library of ready-to-use modules, enabling researchers to specify human input or automation at fixed steps. We validated our workflow by demonstrating its capability to aggregate, curate, and handle errors related to data from multiple sources to generate a multimodal database for clinical AI research. Further, we solicited user feedback to highlight the pros and cons associated with RIL-workflow. The source code is available at github.com/magnooj/RIL-workflow. pubtype: Academic Journal doctype: pictorial tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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