FedICU: a federated learning model for reducing the medication prescription errors in intensive care units

Document Type

Article

Publication Title

Cogent Engineering

Abstract

Patients in the Intensive care unit need remarkable observation. This unit consists of people who are critically ill and may tend to lose their lives anytime. Healthcare professionals in critical care tend to commit prescription errors for many reasons. Since patients in intensive care are severely ill and have complicated health issues, mistakes while prescribing medicines can have serious repercussions. In this study, a federated learning model is simulated to reduce the mistakes while prescribing medicines in the intensive care unit, which provides an opportunity for many hospitals to collaborate, keeping their data local to themselves. Local training is performed with Logistic regression, Simple neural network, and Multilayer perceptron in which simple neural network achieves the highest accuracy of 95%. Model weights transferred to a federated server may be vulnerable to data and model poisoning attacks, eavesdropping, and model inversion attacks. So, model weights are encrypted using Paillier homomorphic encryption (PHE), achieving a model accuracy of 93.26% for a key size of 2048. With key size, the effect of encryption and decryption time is observed. The model is also applied with differential privacy, which achieved an accuracy of 94.24% when c = 0.5 and sigma = 0.05. Thus, this privacy-preserving federated learning model can be used to reduce drug prescription errors in critical care.

DOI

10.1080/23311916.2023.2301150

Publication Date

1-1-2024

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