ALRt: An Active Learning Framework for Irregularly Sampled Temporal Data

TL;DR

This paper introduces ALRt, an active learning framework utilizing recurrent neural networks to improve early sepsis prediction from irregularly sampled temporal data, reducing data requirements while maintaining accuracy.

Contribution

The paper presents a novel active learning RNN approach tailored for irregularly sampled data to enhance sepsis prediction with limited labeled data.

Findings

Active learning RNN achieves comparable accuracy with less data.

ALRt effectively predicts sepsis onset from irregular temporal data.

Model reduces the need for extensive labeled datasets.

Abstract

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.