Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, Ontario, Canada.
Institute for Medical Informatics and Statistics, Kiel University and University Hospital Center Schleswig-Holstein, Campus Kiel, Germany.
J Am Med Inform Assoc. 2023 Mar 16;30(4):718-725. doi: 10.1093/jamia/ocad002.
Convert the Medical Information Mart for Intensive Care (MIMIC)-IV database into Health Level 7 Fast Healthcare Interoperability Resources (FHIR). Additionally, generate and publish an openly available demo of the resources, and create a FHIR Implementation Guide to support and clarify the usage of MIMIC-IV on FHIR.
FHIR profiles and terminology system of MIMIC-IV were modeled from the base FHIR R4 resources. Data and terminology were reorganized from the relational structure into FHIR according to the profiles. Resources generated were validated for conformance with the FHIR profiles. Finally, FHIR resources were published as newline delimited JSON files and the profiles were packaged into an implementation guide.
The modeling of MIMIC-IV in FHIR resulted in 25 profiles, 2 extensions, 35 ValueSets, and 34 CodeSystems. An implementation guide encompassing the FHIR modeling can be accessed at mimic.mit.edu/fhir/mimic. The generated demo dataset contained 100 patients and over 915 000 resources. The full dataset contained 315 000 patients covering approximately 5 840 000 resources. The final datasets in NDJSON format are accessible on PhysioNet.
Our work highlights the challenges and benefits of generating a real-world FHIR store. The challenges arise from terminology mapping and profiling modeling decisions. The benefits come from the extensively validated openly accessible data created as a result of the modeling work.
The newly created MIMIC-IV on FHIR provides one of the first accessible deidentified critical care FHIR datasets. The extensive real-world data found in MIMIC-IV on FHIR will be invaluable for research and the development of healthcare applications.
将医疗信息监护室（MIMIC）-IV 数据库转换为健康水平 7 快速医疗互操作性资源（FHIR）。此外，生成并发布资源的公开演示，并创建 FHIR 实施指南，以支持和阐明在 FHIR 上使用 MIMIC-IV。
从基础 FHIR R4 资源中对 MIMIC-IV 的 FHIR 配置文件和术语系统进行建模。根据配置文件，将数据和术语从关系结构重新组织到 FHIR 中。生成的资源经过验证，以符合 FHIR 配置文件。最后，将 FHIR 资源发布为换行分隔的 JSON 文件，并将配置文件打包到实施指南中。
在 FHIR 中对 MIMIC-IV 的建模产生了 25 个配置文件、2 个扩展、35 个值集和 34 个代码系统。一个涵盖 FHIR 建模的实施指南可在 mimic.mit.edu/fhir/mimic 上访问。生成的演示数据集包含 100 名患者和超过 915000 个资源。完整数据集包含 315000 名患者，涵盖约 5840000 个资源。NDJSON 格式的最终数据集可在 PhysioNet 上访问。
我们的工作强调了生成真实世界 FHIR 存储的挑战和益处。挑战来自于术语映射和配置文件建模决策。好处来自于由于建模工作而创建的广泛验证的公开可访问数据。
新创建的 FHIR 上的 MIMIC-IV 提供了第一个可访问的去识别关键护理 FHIR 数据集之一。FHIR 上的 MIMIC-IV 中发现的广泛的真实世界数据对于研究和医疗保健应用的开发将是非常宝贵的。
