Beyond technology: Can artificial intelligence support clinical decisions in the prediction of sepsis?
Rev Bras Enferm
; 75(5): e20210586, 2022.
Article
in English, Portuguese
| MEDLINE | ID: covidwho-1855075
ABSTRACT
OBJECTIVE:
To analyze the critical alarms predictors of clinical deterioration/sepsis for clinical decision making in patients admitted to a reference hospital complex.METHODS:
An observational retrospective cohort study. The Machine Learning (ML) tool, Robot Laura®, scores changes in vital parameters and lab tests, classifying them by severity. Inpatients and patients over 18 years of age were included.RESULTS:
A total of 122,703 alarms were extracted from the platform, classified as 2 to 9. The pre-selection of critical alarms (6 to 9) indicated 263 urgent alerts (0.2%), from which, after filtering exclusion criteria, 254 alerts were delimited for 61 inpatients. Patient mortality from sepsis was 75%, of which 52% was due to sepsis related to the new coronavirus. After the alarms were answered, 82% of the patients remained in the sectors.CONCLUSIONS:
Far beyond technology, ML models can speed up assertive clinical decisions by nurses, optimizing time and specialized human resources.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Artificial Intelligence
/
Sepsis
Type of study:
Cohort study
/
Diagnostic study
/
Observational study
/
Prognostic study
Limits:
Adolescent
/
Adult
/
Humans
Language:
English
/
Portuguese
Journal:
Rev Bras Enferm
Year:
2022
Document Type:
Article
Affiliation country:
0034-7167-2021-0586
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