JMIR Publications

ABSTRACT

Background:

Sepsis and mortality are the superior cause of death in intensive care unit patients. The ability to predict the onset of sepsis and mortality is of paramount importance for guiding clinical decision-making within hospitals. Many sepsis and mortality prediction studies have been developed to predict each task in advance. However, previous studies have concentrated on single tasks, namely, the prediction of sepsis or mortality. Furthermore, although it is essential to provide clinicians with real-time information that incorporates dynamic alterations in hospitalized patients, the emphasis is on enhancing the efficacy of long-term forecasting over a period exceeding one day.

Objective:

We propose a novel framework for multi-task short-term prediction that integrates gated recurrent units and bidirectional long- and short-term memory networks, optimized for the prediction of sepsis and mortality and used 6-hours of information. Therefore, the proposed framework could provide timely and accurate predictions of sepsis and mortality within 12-hour. Additionally, a subject-independent evaluation was performed to evaluate the generalization performance of the model.

Methods:

The performance of sepsis and mortality prediction was evaluated using the medical information mart for intensive care (MIMIC)-IV database. A comparison of single-task and multi-task existing methods revealed that the latter exhibited superior performance. To assess the generalizability of the proposed framework, the eICU Collaborative Research Database (eICU-CRD) was tested using a model trained on MIMIC-IV, which demonstrated enhanced performance compared with each method.

Results:

The proposed method was statistically superior to performance achieved by single- and multi-task predictions for MIMIC-IV. Next, the eICU-CRD was tested using a MIMIC-IV-trained model to check its generalization ability, and a statistically superior performance was achieved compared with the baselines.

Conclusions:

Our novel framework for learning unique features provides stable predictive power in different environments within the intensive care unit. Therefore, the proposed multi-task, including the sepsis and mortality prediction system, allows clinicians to intervene early in patients through sepsis and mortality prediction information, and can be applied to clinical trials in digital health.