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

TL;DR

This paper introduces IHIT-BED, a machine learning model that uses hematology data to detect bloodstream infections early in emergency departments.

Contribution

IHIT-BED is the first model to use hematology impedance histogram signals for early bacteremia detection.

Findings

IHIT-BED effectively predicts positive blood cultures and severe inflammation.

The model is sensitive to changes in blood cell morphology linked to bacterial infections.

It supports prompt antibiotic treatment decisions in emergency settings.

Abstract

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.

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.

The method can be found in https://github.com/appleRtsan/IHIT-BED.

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