Prediction of Deterioration in Critically Ill Patients with Heart Failure Based on Vital Signs Monitoring

This study aims to develop a real-time machine learning model for acute heart failure onset based on vital signs in bedside monitoring. A group of 2284 patients were analyzed retrospectively from the Medical Information Mart for Intensive Care III database. We extracted various features building mac...

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Bibliographic Details
Main Authors: Zhang, Shengyu, Yang, Kang, Ye, Wenyu, Jiang, Haoyu, He, Xianliang, Wang, Lei, Li, Yijing
Format: Conference Proceeding
Language:English
Subjects:
Current measurement
Feature extraction
Medical services
Prediction algorithms
Predictive models
Real-time systems
Online Access:Request full text
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Summary:This study aims to develop a real-time machine learning model for acute heart failure onset based on vital signs in bedside monitoring. A group of 2284 patients were analyzed retrospectively from the Medical Information Mart for Intensive Care III database. We extracted various features building machine learning model. Extreme Gradient Boosting was used to develop the real-time prediction model. The validation on test set gave decent early warning performance. The model prediction can provide more timely notifications for doctors to perform better treatment for patients.
ISSN:2325-887X
DOI:10.22489/CinC.2022.264