Communicating exploratory unsupervised machine learning analysis in age clustering for paediatric disease.

ABSTRACT:

Background

Despite the increasing availability of electronic healthcare record (EHR) data and wide availability of plug-and-play machine learning (ML) Application Programming Interfaces, the adoption of data-driven decision-making within routine hospital workflows thus far, has remained limited. Through the lens of deriving clusters of diagnoses by age, this study investigated the type of ML analysis that can be performed using EHR data and how results could be communicated to lay stakeholders.

Methods

Observational EHR data from a tertiary paediatric hospital, containing 61 522 unique patients and 3315 unique ICD-10 diagnosis codes was used, after preprocessing. K-means clustering was applied to identify age distributions of patient diagnoses. The final model was selected u

SUBMITTER: Spear JW 

PROVIDER: S-EPMC11288139 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature