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- Title
Explainable Artificial Intelligence Helps in Understanding the Effect of Fibronectin on Survival of Sepsis.
- Authors
Lemańska-Perek, Anna; Krzyżanowska-Gołąb, Dorota; Kobylińska, Katarzyna; Biecek, Przemysław; Skalec, Tomasz; Tyszko, Maciej; Gozdzik, Waldemar; Adamik, Barbara
- Abstract
Fibronectin (FN) plays an essential role in the host's response to infection. In previous studies, a significant decrease in the FN level was observed in sepsis; however, it has not been clearly elucidated how this parameter affects the patient's survival. To better understand the relationship between FN and survival, we utilized innovative approaches from the field of explainable machine learning, including local explanations (Break Down, Shapley Additive Values, Ceteris Paribus), to understand the contribution of FN to predicting individual patient survival. The methodology provides new opportunities to personalize informative predictions for patients. The results showed that the most important indicators for predicting survival in sepsis were INR, FN, age, and the APACHE II score. ROC curve analysis showed that the model's successful classification rate was 0.92, its sensitivity was 0.92, its positive predictive value was 0.76, and its accuracy was 0.79. To illustrate these possibilities, we have developed and shared a web-based risk calculator for exploring individual patient risk. The web application can be continuously updated with new data in order to further improve the model.
- Subjects
ARTIFICIAL intelligence; FIBRONECTINS; SEPSIS; OVERALL survival; WEB-based user interfaces; RECEIVER operating characteristic curves; MACHINE learning
- Publication
Cells (2073-4409), 2022, Vol 11, Issue 15, p2433
- ISSN
2073-4409
- Publication type
Article
- DOI
10.3390/cells11152433