  Sepsis is a deadly condition affecting many patients in the hospital. Recent
studies have shown that patients diagnosed with sepsis have significant
mortality and morbidity, resulting from the body's dysfunctional host response
to infection. Clinicians often rely on the use of Sequential Organ Failure
Assessment (SOFA), Systemic Inflammatory Response Syndrome (SIRS), and the
Modified Early Warning Score (MEWS) to identify early signs of clinical
deterioration requiring further work-up and treatment. However, many of these
tools are manually computed and were not designed for automated computation.
There have been different methods used for developing sepsis onset models, but
many of these models must be trained on a sufficient number of patient
observations in order to form accurate sepsis predictions. Additionally, the
accurate annotation of patients with sepsis is a major ongoing challenge. In
this paper, we propose the use of Active Learning Recurrent Neural Networks
(ALRts) for short temporal horizons to improve the prediction of irregularly
sampled temporal events such as sepsis. We show that an active learning RNN
model trained on limited data can form robust sepsis predictions comparable to
models using the entire training dataset.
