Rapid triage for ischemic stroke: a machine learning-driven approach in the context of predictive, preventive and personalised medicine.

ABSTRACT:

Background

Recognising the early signs of ischemic stroke (IS) in emergency settings has been challenging. Machine learning (ML), a robust tool for predictive, preventive and personalised medicine (PPPM/3PM), presents a possible solution for this issue and produces accurate predictions for real-time data processing.

Methods

This investigation evaluated 4999 IS patients among a total of 10,476 adults included in the initial dataset, and 1076 IS subjects among 3935 participants in the external validation dataset. Six ML-based models for the prediction of IS were trained on the initial dataset of 10,476 participants (split participants into a training set [80%] and an internal validation set [20%]). Selected clinical laboratory features routinely assessed at admission were used

SUBMITTER: Zheng Y 

PROVIDER: S-EPMC9203613 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature