Detecting Unusual Intravenous Infusion Alerting Patterns with Machine Learning Algorithms.

ABSTRACT:

Objective

To detect unusual infusion alerting patterns using machine learning (ML) algorithms as a first step to advance safer inpatient intravenous administration of high-alert medications.

Materials and methods

We used one year of detailed propofol infusion data from a hospital. Interpretable and clinically relevant variables were feature engineered, and data points were aggregated per calendar day. A univariate (maximum times-limit) moving range (mr) control chart was used to simulate clinicians' common approach to identifying unusual infusion alerting patterns. Three different unsupervised multivariate ML-based anomaly detection algorithms (Local Outlier Factor, Isolation Forest, and k-Nearest Neighbors) were used for the same purpose. Results from the control chart and

SUBMITTER: Obuseh M 

PROVIDER: S-EPMC9767430 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature