Work

An optimal antibiotic selection framework for Sepsis patients using Artificial Intelligence

Year: 2024

Type: article

Abstract: In this work we present OptAB, the first completely data-driven online-updateable antibiotic selection model based on Artificial Intelligence for Sepsis patients accounting for side-effects. OptAB performs an iterative optimal antibiotic selection for real-world Sepsis patients focussing on minimizing the Sepsis-related organ failure score (SOFA-Score) as treatment success while accounting for nephrotoxicity and hepatotoxicity as serious antibiotic side-effects. OptAB provides disease progression forecasts for (combinations of) the antibiotics Vancomycin, Ceftriaxone and Piperacillin/Tazobactam and learns realistic treatment influences on the SOFA-Score and the laboratory values creatinine, bilirubin total and alanine-transaminase indicating possible side-effects. OptAB is based on a hybrid neural network differential equation algorithm and can handle the special characteristics of patient data including irregular measurements, a large amount of missing values and time-dependent confounding. OptAB's selected optimal antibiotics exhibit faster efficacy than the administered antibiotics.

Source: npj Digital Medicine

DOI: 10.1038/s41746-024-01350-y

Authors: Philipp Wendland, Christof Schenkel-Häger, Ingobert Wenningmann, Maik Kschischo

Institutions: Koblenz University of Applied Sciences, University Hospital Bonn, Universität Koblenz

Language: English

Topic: Sepsis Diagnosis and Treatment

Subfield: Epidemiology

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $3,790