An Scalable and Interoperable Real-time Software Platform for Forecasting the Onset-time of Sepsis Público
Amrollahi, Fatemeh (Summer 2019)
Abstract
Sepsis, a dysregulated inflammatory response to infection, is difficult to diagnose in advance of life-threatening physiological decompensations. Multiple studies have demonstrated improved outcomes when this condition is recognized and treated early. Nemati et al. have developed a real-time, high-dimensional machine learning algorithm capable of detecting sepsis four to six hours prior to clinical recognition, capable of substantially reducing the untoward effects associated with the condition. In this work, a software platform was developed that consumes live patient data, securely transports it into a cloud environment, and interprets it in real-time. Our approach leverages the benefits of cloud-based managed services that are scalable and fault tolerant. Though there are several pathways for extracting live data from electronic health records (EHR), the AIDEx platform proposed in this work is an EHR vendor-agnostic open-source solution that can be easily deployed in any clinical environments.
Table of Contents
Introduction
Sepsis: A Health Crisis
Sepsis Prediction Algorithm
Real-time Software Platform for Forecasting the Onset of Sepsis
AIDEx: A FHIR Based Real-time Software Platform for Forecasting
the Onset of Sepsis
Overview
The AIDEx 1.0 Services
Sepsis App on Tele-ICU: A SQL Based Real-time Software Platform
at Emory Hospital for Forecasting the Onset of Sepsis
Overview of SepsisApp on Tele-ICU
Tests and Statistical methods
Conclusion and Future directions
Bibliography
About this Master's Thesis
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