DeepAISE -- An End-to-End Development and Deployment of a Recurrent Neural Survival Model for Early Prediction of Sepsis

08/10/2019
by   Supreeth P. Shashikumar, et al.
0

Sepsis, a dysregulated immune system response to infection, is among the leading causes of morbidity, mortality, and cost overruns in the Intensive Care Unit (ICU). Early prediction of sepsis can improve situational awareness amongst clinicians and facilitate timely, protective interventions. While the application of predictive analytics in ICU patients has shown early promising results, much of the work has been encumbered by high false-alarm rates. Efforts to improve specificity have been limited by several factors, most notably the difficulty of labeling sepsis onset time and the low prevalence of septic-events in the ICU. Here, we present DeepAISE (Deep Artificial Intelligence Sepsis Expert), a recurrent neural survival model for the early prediction of sepsis. We show that by coupling a clinical criterion for defining sepsis onset time with a treatment policy (e.g., initiation of antibiotics within one hour of meeting the criterion), one may rank the relative utility of various criteria through offline policy evaluation. Given the optimal criterion, DeepAISE automatically learns predictive features related to higher-order interactions and temporal patterns among clinical risk factors that maximize the data likelihood of observed time to septic events. DeepAISE has been incorporated into a clinical workflow, which provides real-time hourly sepsis risk scores. A comparative study of four baseline models indicates that DeepAISE produces the most accurate predictions (AUC=0.90 and 0.87) and the lowest false alarm rates (FAR=0.20 and 0.26) in two separate cohorts (internal and external, respectively), while simultaneously producing interpretable representations of the clinical time series and risk factors.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 40

09/06/2021

Early ICU Mortality Prediction and Survival Analysis for Respiratory Failure

Respiratory failure is the one of major causes of death in critical care...
12/20/2019

Dynamic Prediction of ICU Mortality Risk Using Domain Adaptation

Early recognition of risky trajectories during an Intensive Care Unit (I...
05/12/2021

Early prediction of respiratory failure in the intensive care unit

The development of respiratory failure is common among patients in inten...
06/26/2018

Semantically Enhanced Dynamic Bayesian Network for Detecting Sepsis Mortality Risk in ICU Patients with Infection

Although timely sepsis diagnosis and prompt interventions in Intensive C...
02/09/2020

A Physiology-Driven Computational Model for Post-Cardiac Arrest Outcome Prediction

Patients resuscitated from cardiac arrest (CA) face a high risk of neuro...
12/03/2019

Explainable artificial intelligence model to predict acute critical illness from electronic health records

We developed an explainable artificial intelligence (AI) early warning s...
08/19/2017

An Improved Multi-Output Gaussian Process RNN with Real-Time Validation for Early Sepsis Detection

Sepsis is a poorly understood and potentially life-threatening complicat...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.