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The CASH Database

The database is the hub of the CASH service. The content can be added, searched and exported to create personalised alerting services for library customers.  There are a number of newsfeeds that can be embedded into external CAS website pages keeping them constantly updated with the latest content.

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Predicting Sepsis Mortality in a Population-Based National Database: Machine Learning Approach

Source
Journal of Medical Internet Research
Year of publication
2022
Abstract
Although machine learning (ML) algorithms have been applied to point-of-care sepsis prognostication, ML has not been used to predict sepsis mortality in an administrative database. Therefore, we examined the performance of common ML algorithms in predicting sepsis mortality in adult patients with sepsis and compared it with that of the conventional context knowledge-based logistic regression approach.; Objective: The aim of this study is to examine the performance of common ML algorithms in predicting sepsis mortality in adult patients with sepsis and compare it with that of the conventional context knowledge-based logistic regression approach.; Conclusions: ML approaches can improve sensitivity, specificity, positive predictive value, negative predictive value, discrimination, and calibration in predicting in-hospital mortality in patients hospitalized with sepsis in the United States. These models need further validation and could be applied to develop more accurate models to compare risk-standardized mortality rates across hospitals and geographic regions, paving the way for research and policy initiatives studying disparities in sepsis care.
Date added
13/05/2022
Created by
Catherine Stead
Published by
Current Awareness Service for Health