Khalilia Mohammed, Choi Myung, Henderson Amelia, Iyengar Sneha, Braunstein Mark, Sun Jimeng
Georgia Institute of Technology, Atlanta, Georgia.
AMIA Annu Symp Proc. 2015 Nov 5;2015:717-26. eCollection 2015.
Clinical predictive modeling involves two challenging tasks: model development and model deployment. In this paper we demonstrate a software architecture for developing and deploying clinical predictive models using web services via the Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) standard. The services enable model development using electronic health records (EHRs) stored in OMOP CDM databases and model deployment for scoring individual patients through FHIR resources. The MIMIC2 ICU dataset and a synthetic outpatient dataset were transformed into OMOP CDM databases for predictive model development. The resulting predictive models are deployed as FHIR resources, which receive requests of patient information, perform prediction against the deployed predictive model and respond with prediction scores. To assess the practicality of this approach we evaluated the response and prediction time of the FHIR modeling web services. We found the system to be reasonably fast with one second total response time per patient prediction.
模型开发和模型部署。在本文中，我们展示了一种软件架构，用于通过健康级别7（HL7）快速医疗保健互操作性资源（FHIR）标准使用网络服务来开发和部署临床预测模型。这些服务支持使用存储在OMOP通用数据模型（CDM）数据库中的电子健康记录（EHR）进行模型开发，并通过FHIR资源对个体患者进行评分以进行模型部署。将MIMIC2重症监护病房数据集和一个合成门诊数据集转换为OMOP CDM数据库以进行预测模型开发。生成的预测模型作为FHIR资源进行部署，这些资源接收患者信息请求，针对部署的预测模型进行预测，并以预测分数进行响应。为了评估这种方法的实用性，我们评估了FHIR建模网络服务的响应时间和预测时间。我们发现该系统速度相当快，每位患者预测的总响应时间为一秒。
