ArXiv Preprint
Acute brain dysfunctions (ABD), which include coma and delirium, are
prevalent in the ICU, especially among older patients. The current approach in
manual assessment of ABD by care providers may be sporadic and subjective.
Hence, there exists a need for a data-driven robust system automating the
assessment and prediction of ABD. In this work, we develop a machine learning
system for real-time prediction of ADB using Electronic Health Record (HER)
data. Our data processing pipeline enables integration of static and temporal
data, and extraction of features relevant to ABD. We train several
state-of-the-art transformer models and baseline machine learning models
including CatBoost and XGB on the data that was collected from patients
admitted to the ICU at UF Shands Hospital. We demonstrate the efficacy of our
system for tasks related to acute brain dysfunction including binary
classification of brain acuity and multi-class classification (i.e., coma,
delirium, death, or normal), achieving a mean AUROC of 0.953 on our Long-former
implementation. Our system can then be deployed for real-time prediction of ADB
in ICUs to reduce the number of incidents caused by ABD. Moreover, the
real-time system has the potential to reduce costs, duration of patients stays
in the ICU, and mortality among those afflicted.
Brandon Silva, Miguel Contreras, Tezcan Ozrazgat Baslanti, Yuanfang Ren, Guan Ziyuan, Kia Khezeli, Azra Bihorac, Parisa Rashidi
2023-03-13
