Your privacy, your choice

We use essential cookies to make sure the site can function. We also use optional cookies for advertising, personalisation of content, usage analysis, and social media.

By accepting optional cookies, you consent to the processing of your personal data - including transfers to third parties. Some third parties are outside of the European Economic Area, with varying standards of data protection.

See our privacy policy for more information on the use of your personal data.

for further information and to change your choices.
Skip to main content
Fig. 1 | BMC Medical Informatics and Decision Making

Fig. 1

From: Integrating structured and unstructured data for predicting emergency severity: an association and predictive study using transformer-based natural language processing models

Fig. 1

a Forest Plot of Odds Ratios with 95% CI (Log Scale). b Forest Plot of Odds Ratios with 95% CI (Log Scale). The odds ratios (ORs) presented in Figure. 1 were derived from a logistic regression model where all variables were mutually adjusted. This means that the ORs account for the influence of all other variables included in the model. For example, the OR for age reflects the effect of age on emergency severity while controlling for other factors such as gender, vital signs, and medical history. This mutual adjustment allows for a more accurate estimation of the individual contribution of each variable to the prediction of emergency severity, minimizing potential confounding effects

Back to article page

BMC Medical Informatics and Decision Making

ISSN: 1472-6947

Contact us