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Development of an explainable AI system using routine clinical parameters for rapid differentiation of inflammatory conditions
Introduction: Inflammatory conditions in patients have various causes and require different treatments. Bacterial infections are treated with antibiotics, while these medications are ineffective against viral infections. Autoimmune diseases and graft-versus-host disease (GVHD) after allogeneic st...
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| Autori principali: | , , , , |
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| Natura: | Articolo |
| Lingua: | inglese |
| Pubblicazione: |
Philipps-Universität Marburg
2024
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| Soggetti: | |
| Accesso online: | PDF Full Text |
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| Riassunto: | Introduction: Inflammatory conditions in patients have various causes and
require different treatments. Bacterial infections are treated with antibiotics,
while these medications are ineffective against viral infections. Autoimmune
diseases and graft-versus-host disease (GVHD) after allogeneic stem cell
transplantation, require immunosuppressive therapies such as glucocorticoids,
which may be contraindicated in other inflammatory states. In this study, we
employ a combination of straightforward blood tests to devise an explainable
artificial intelligence (XAI) for distinguishing between bacterial infections, viral
infections, and autoimmune diseases/graft-versus-host disease.
Patients and methods: We analysed peripheral blood from 80 patients with
inflammatory conditions and 38 controls. Complete blood count, CRP analysis,
and a rapid flow cytometric test for myeloid activation markers CD169, CD64,
and HLA-DR were utilized. A two-step XAI distinguished firstly with C5.0 rules
pruned by ABC analysis between controls and inflammatory conditions and
secondly between the types of inflammatory conditions with a new bivariate
decision tree using the Simpson impurity function.
Results: Inflammatory conditions were distinguished using an XAI, achieving an
overall accuracy of 81.0% (95%CI 72 – 87%). Bacterial infection (N = 30), viral
infection (N = 26), and autoimmune diseases/GVHD (N = 24) were differentiated
with accuracies of 90.3%, 80.0%, and 79.0%, respectively. The most critical
parameter for distinguishing between controls and inflammatory conditions was
the expression of CD64 on neutrophils. Monocyte count and expression of CD169
were most crucial for the classification within the inflammatory conditions.
Conclusion: Treatment decisions for inflammatory conditions can be effectively
guided by XAI rules, straightforward to implement and based on promptly
acquired blood parameters. |
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| Descrizione del documento: | Gefördert durch den Open-Access-Publikationsfonds der UB Marburg. |
| DOI: | 10.3389/fimmu.2024.1364954 |