Improved inpatient deterioration detection in general wards by using time-series vital signs.

ABSTRACT: Although in-hospital cardiac arrest is uncommon, it has a high mortality rate. Risk identification of at-risk patients is critical for post-cardiac arrest survival rates. Early warning scoring systems are generally used to identify hospitalized patients at risk of deterioration. However, these systems often require clinical data that are not always regularly measured. We developed a more accurate, machine learning-based model to predict clinical deterioration. The time series early warning score (TEWS) used only heart rate, systolic blood pressure, and respiratory data, which are regularly measured in general wards. We tested the performance of the TEWS in two tasks performed with data from the electronic medical records of 16,865 adult admissions and compared the results with those of oth

SUBMITTER: Su CF 

PROVIDER: S-EPMC9279370 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature