Article: Diagnosing Hemophagocytic Lymphohistiocytosis with Machine Learning: A Proof of Concept.
2022 Volume 11, Issue 20
Abstract: Hemophagocytic lymphohistiocytosis is a hyperinflammatory syndrome characterized by uncontrolled activation of immune cells and mediators. Two diagnostic tools are widely used in clinical practice: the HLH-2004 criteria and the Hscore. Despite their good ...
Abstract | Hemophagocytic lymphohistiocytosis is a hyperinflammatory syndrome characterized by uncontrolled activation of immune cells and mediators. Two diagnostic tools are widely used in clinical practice: the HLH-2004 criteria and the Hscore. Despite their good diagnostic performance, these scores were constructed after a selection of variables based on expert consensus. We propose here a machine learning approach to build a classification model for HLH in a cohort of patients selected by glycosylated ferritin dosage in our tertiary center in Lyon, France. On a dataset of 207 adult patients with 26 variables, our model showed good overall diagnostic performances with a sensitivity of 71.4% and high specificity, and positive and negative predictive values which were 100%, 100%, and 96.9%, respectively. Although generalization is difficult on a selected population, this is the first study to date to provide a machine-learning model for HLH detection. Further studies will be required to improve the machine learning model performances with a large number of HLH cases and with appropriate controls. |
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Language | English |
Publishing date | 2022-10-21 |
Publishing country | Switzerland |
Document type | Journal Article |
ZDB-ID | 2662592-1 |
ISSN | 2077-0383 |
ISSN | 2077-0383 |
DOI | 10.3390/jcm11206219 |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
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