Ghalwash Mohamed, Koski Eileen, Veijola Riitta, Toppari Jorma, Hagopian William, Rewers Marian, Anand Vibha
Center for Computational Health, IBM Research, NY, USA.
Department of Pediatrics, PEDEGO Research Unit, University of Oulu and Oulu University Hospital, Oulu, Finland.
AMIA Annu Symp Proc. 2022 Feb 21;2021:516-525. eCollection 2021.
The Collaborative Open Outcomes tooL (COOL) is a novel, highly configurable application to simulate, evaluate and compare potential population-level screening schedules. Its first application is type 1 diabetes (T1D) screening, where known biomarkers for risk exist but clinical application lags behind. COOL was developed with the T1DI Study Group, in order to assess screening schedules for islet autoimmunity development based on existing datasets. This work shows clinical research utility, but the tool can be applied in other contexts. COOL helps the user define and evaluate a domain knowledge-driven screening schedule, which can be further refined with data-driven insights. COOL can also compare performance of alternative schedules using adjusted sensitivity, specificity, PPV and NPV metrics. Insights from COOL may support a variety of needs in disease screening and surveillance.
协作式开放结果工具（COOL）是一款新颖的、高度可配置的应用程序，用于模拟、评估和比较潜在的人群层面筛查方案。其首个应用领域是1型糖尿病（T1D）筛查，在该领域虽然存在已知的风险生物标志物，但临床应用却滞后。COOL是与T1DI研究小组共同开发的，旨在基于现有数据集评估胰岛自身免疫发展的筛查方案。这项工作展示了临床研究效用，但该工具也可应用于其他场景。COOL帮助用户定义和评估基于领域知识驱动的筛查方案，并且可以通过数据驱动的见解进一步优化。COOL还可以使用调整后的灵敏度、特异性、阳性预测值和阴性预测值指标来比较替代方案的性能。来自COOL的见解可能支持疾病筛查和监测中的各种需求。
