Utilising unsupervised machine learning to predict outbreaks of respiratory tract infections in acute Irish hospitals (2016-2021).

ABSTRACT:

Objectives

To apply unsupervised machine learning (ML) to predict outbreaks of respiratory tract infections (RTIs) in acute Irish hospitals (2016-2021).

Study design

A retrospective study.

Methods

RTIs data was extracted from Irish hospital inpatient enquiry (HIPE). Three k-modes clustering models were developed, whose resulting clusters were compared via graphical visualisation of main RTIs to choose the model which captured the outbreaks best. To understand the individual RTIs behind the outbreaks, further exploration was carried out.

Results

Nearly half a million patients (491,099) were admitted to 55 acute Irish hospitals with an RTI. Model 2, including 212 diagnostic groups according to hierarchical clustering, was able to capture all outbreaks. Further an

SUBMITTER: Amin D 

PROVIDER: S-EPMC12925592 | biostudies-literature | 2026 Jun

REPOSITORIES: biostudies-literature