Supporting the Diagnosis of Fabry Disease Using a Natural Language Processing-Based Approach

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 kno...

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Bibliographic Details
Published in:Journal of clinical medicine 2023-05, Vol.12 (10), p.3599
Main Authors: 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
Format: Article
Language:English
Subjects:
Clinical medicine
Computational linguistics
Computer-aided medical diagnosis
Diagnosis
Disease
Electronic health records
Enzymes
Fabry's disease
Hospitals
Laboratories
Language processing
Medical records
Methods
Natural language interfaces
Patients
Physicians
Risk factors
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Summary: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.
ISSN:2077-0383
2077-0383
DOI:10.3390/jcm12103599