Predicting bloodstream infection outcome using machine learning

Bloodstream infections (BSI) are a main cause of infectious disease morbidity and mortality worldwide. Early prediction of BSI patients at high risk of poor outcomes is important for earlier decision making and effective patient stratification. We developed electronic medical record-based machine le...

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Bibliographic Details
Published in:Scientific reports 2021-10, Vol.11 (1), p.20101-20101, Article 20101
Main Authors: Zoabi, Yazeed, Kehat, Orli, Lahav, Dan, Weiss-Meilik, Ahuva, Adler, Amos, Shomron, Noam
Format: Article
Language:English
Subjects:
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Anti-Bacterial Agents - pharmacology
Bacteremia - diagnosis
Bacteremia - drug therapy
Bacteremia - epidemiology
Bacteremia - microbiology
Bacteria - drug effects
Bacteria - isolation & purification
Decision making
Demography
Electronic Health Records - statistics & numerical data
Electronic medical records
Female
Humanities and Social Sciences
Humans
Infectious diseases
Learning algorithms
Machine Learning
Male
Medical records
Morbidity
multidisciplinary
Patients
Retrospective Studies
Risk Factors
ROC Curve
Science
Science (multidisciplinary)
Sepsis - diagnosis
Sepsis - drug therapy
Sepsis - epidemiology
Sepsis - microbiology
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Summary:Bloodstream infections (BSI) are a main cause of infectious disease morbidity and mortality worldwide. Early prediction of BSI patients at high risk of poor outcomes is important for earlier decision making and effective patient stratification. We developed electronic medical record-based machine learning models that predict patient outcomes of BSI. The area under the receiver-operating characteristics curve was 0.82 for a full featured inclusive model, and 0.81 for a compact model using only 25 features. Our models were trained using electronic medical records that include demographics, blood tests, and the medical and diagnosis history of 7889 hospitalized patients diagnosed with BSI. Among the implications of this work is implementation of the models as a basis for selective rapid microbiological identification, toward earlier administration of appropriate antibiotic therapy. Additionally, our models may help reduce the development of BSI and its associated adverse health outcomes and complications.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-021-99105-2