[MiHUBx: a digital progress hub for the use of intersectoral clinical data sets using the example of diabetic macular edema].

Gabriel Stolze, Vinodh Kakkassery, Danny Kowerko, Martin Bartos, Katja Hoffmann, Martin Sedlmayr, Katrin Engelmann
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Abstract

Background: Evidence-based treatment recommendations are helpful in the corresponding discipline-specific treatment but can hardly take data from real-world care into account. In order to make better use of this in everyday clinical practice, including with respect to predictive statements about disease development or treatment success, models with data from treatment must be developed in order to use them for the development of assistive artificial intelligence.

Goal: The aim of the Use Case 1 of the medical informatics hub in Saxony (MiHUBx) is the development of a model based on treatment and research data for a treatment algorithm supported by biomarkers and also the development of the necessary digital infrastructure.

Material and methods: Step by step, the necessary partners in hospitals and practices will be brought together technically or through research questions within Use Case 1 "Ophthalmology meets Diabetology", a regional digital progress hub in health, the medical informatics hub in Saxony (MiHUBx ) of the nationwide medical informatics initiative (MII).

Results: Based on joint studies with diabetologists, robust serological and imaging biomarkers were selected that provide evidence of the development of diabetic macular edema (DME). In the future, these and other scientifically proven prognostic markers will be incorporated into a treatment algorithm that is supported by artificial intelligence (AI). For this purpose, model procedures are being developed together with medical informatics specialists. At the same time, a data integration center (DIZ) was established.

Conclusion: In addition to the structured and technical combination of the previously disseminated and partially heterogeneous treatment data, the Use Case 1 defines the chances and hurdles for using such real-world data to develop artificial intelligence.

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[MiHUBx:以糖尿病性黄斑水肿为例,使用跨部门临床数据集的数字进展中心]。
背景:循证治疗建议有助于相应的学科特异性治疗,但很难考虑到现实世界护理的数据。为了在日常临床实践中更好地利用这一点,包括关于疾病发展或治疗成功的预测性陈述,必须开发具有治疗数据的模型,以便将其用于辅助人工智能的开发。目标:萨克森州医疗信息中心(MiHUBx)用例1的目的是开发基于生物标志物支持的治疗算法的治疗和研究数据的模型,并开发必要的数字基础设施。材料和方法:逐步地,医院和实践中的必要合作伙伴将通过技术或研究问题在用例1“眼科与糖尿病学”中聚集在一起,这是一个区域健康数字进步中心,萨克森州医疗信息中心(MiHUBx)的全国医疗信息倡议(MII)。结果:基于与糖尿病学家的联合研究,我们选择了强有力的血清学和成像生物标志物,为糖尿病性黄斑水肿(DME)的发展提供证据。未来,这些和其他经过科学验证的预后指标将被纳入人工智能(AI)支持的治疗算法中。为此目的,正在与医学信息学专家一起制定示范程序。同时,建立了数据集成中心(DIZ)。结论:除了对先前传播的和部分异构的治疗数据进行结构化和技术组合外,用例1还定义了使用这些真实世界数据开发人工智能的机会和障碍。
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