AIDEx - An Open-source Platform for Real-Time Forecasting Sepsis and A Case Study on Taking ML Algorithms to Production

• Type of Material: Article
• Authors: Fatemeh Amrollahi, University of California San Diego; Supreeth Prajwal Shashikumar, University of California San Diego; Pradeeban Kathiravelu, Emory University; Ashish Sharma, Emory University; Shamim Nemati, University of California San Diego
• Language: English
• Date: 2020-07
• Publisher: IEEE
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2020, IEEE
• Final Published Version (URL): https://doi.org/10.1109/EMBC44109.2020.9175947
• Title of Journal or Parent Work: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
• Start Page: 5610
• End Page: 5614
• Keywords: AIDEx; machine learning; Sepsis
• Subject - Topics: Diagnosis; Sepsis; Machine learning

Abstract

Sepsis, a dysregulated immune response to infection, has been the leading cause of morbidity and mortality in critically ill patients. Multiple studies have demonstrated improved survival outcomes when early treatment is initiated for septic patients. In our previous work, we developed a real-time machine learning algorithm capable of predicting onset of sepsis four to six hours prior to clinical recognition. In this work, we develop AIDEx, an open-source platform that consumes data as FHIR resources. It is capable of consuming live patient data, securely transporting it into a cloud environment, and monitoring patients in real-time. We build AIDEx as an EHR vendor-agnostic open-source platform that can be easily deployed in clinical environments. Finally, the computation of the sepsis risk scores uses a common design pattern that is seen in streaming clinical informatics and predictive analytics applications. AIDEx provides a comprehensive case study in the design and development of a production-ready ML platform that integrates with Healthcare IT systems.