IHIT-BED: an interpretable transformer approach using unbiased hematology analyzer impedance data for early identification of bacteremia in emergency department.

Motivation: Early detection of severe bloodstream infections is essential for early treatment initiation. However, the suspicion of bacteremia relies on the combined interpretation of routine laboratory tests, such as complete blood count (CBC), differential count (DC), and elevated C-reactive protein (CRP). Furthermore, a definite diagnosis of bacteremia requires a positive blood culture, which takes several days. Results: We developed the Interpretable Hematology analyzer Impedance data-based Tabular network for early identification of Bacteremia in Emergency Department (IHIT-BED), a blood stream infection prediction system built by machine learning methods using the integrated data of hematology analyzer impedance histogram signals of CBC, blood culture reports, and CRP levels, which were simultaneously tested in the first blood draw of patients visiting the ED. To our knowledge, IHIT-BED is the first predictor based on hematology impedance histogram signals, which performs well not only in predicting a positive blood culture and severe inflammation, but also is sensitive to detect changes in blood cell morphologies correlated with active inflammatory responses to bacterial infections. IHIT-BED provides clinical decision support for prompt initiation of antibiotics treatment. Availability and implementation: The method can be found in https://github.com/appleRtsan/IHIT-BED.