Michalski Adrian A, Lis Karol, Stankiewicz Joanna, Kloska Sylwester M, Sycz Arkadiusz, Dudziński Marek, Muras-Szwedziak Katarzyna, Nowicki Michał, Bazan-Socha Stanisława, Dabrowski Michal J, Basak Grzegorz W
Saventic Health, Polna 66/12 Street, 87-100 Torun, Poland.
Department of Analytical Chemistry, Nicolaus Copernicus University Ludwik Rydygier Collegium Medicum, 85-089 Bydgoszcz, Poland.
J Clin Med. 2023 May 22;12(10):3599. doi: 10.3390/jcm12103599.
In clinical practice, the consideration of non-specific symptoms of rare diseases in order to make a correct and timely diagnosis is often challenging. To support physicians, we developed a decision-support scoring system on the basis of retrospective research. Based on the literature and expert knowledge, we identified clinical features typical for Fabry disease (FD). Natural language processing (NLP) was used to evaluate patients' electronic health records (EHRs) to obtain detailed information about FD-specific patient characteristics. The NLP-determined elements, laboratory test results, and ICD-10 codes were transformed and grouped into pre-defined FD-specific clinical features that were scored in the context of their significance in the FD signs. The sum of clinical feature scores constituted the FD risk score. Then, medical records of patients with the highest FD risk score were reviewed by physicians who decided whether to refer a patient for additional tests or not. One patient who obtained a high-FD risk score was referred for DBS assay and confirmed to have FD. The presented NLP-based, decision-support scoring system achieved AUC of 0.998, which demonstrates that the applied approach enables for accurate identification of FD-suspected patients, with a high discrimination power.
在临床实践中，考虑罕见病的非特异性症状以做出正确及时的诊断往往具有挑战性。为了帮助医生，我们基于回顾性研究开发了一种决策支持评分系统。基于文献和专家知识，我们确定了法布里病（FD）的典型临床特征。使用自然语言处理（NLP）来评估患者的电子健康记录（EHR），以获取有关FD特异性患者特征的详细信息。将NLP确定的要素、实验室检查结果和ICD-10编码进行转换，并分组为预定义的FD特异性临床特征，根据它们在FD体征中的重要性进行评分。临床特征评分的总和构成FD风险评分。然后，由医生审查FD风险评分最高的患者的病历，决定是否将患者转诊进行进一步检查。一名获得高FD风险评分的患者被转诊进行DBS检测，并被确诊为FD。所提出的基于NLP的决策支持评分系统的AUC为0.998，这表明所应用的方法能够准确识别疑似FD的患者，具有很高的鉴别力。
