Predicting asthma attacks in primary care: protocol for developing a machine learning-based prediction model.

ABSTRACT:

Introduction

Asthma is a long-term condition with rapid onset worsening of symptoms ('attacks') which can be unpredictable and may prove fatal. Models predicting asthma attacks require high sensitivity to minimise mortality risk, and high specificity to avoid unnecessary prescribing of preventative medications that carry an associated risk of adverse events. We aim to create a risk score to predict asthma attacks in primary care using a statistical learning approach trained on routinely collected electronic health record data.

Methods and analysis

We will employ machine-learning classifiers (naïve Bayes, support vector machines, and random forests) to create an asthma attack risk prediction model, using the Asthma Learning Health System (ALHS) study patient registry comprisi

SUBMITTER: Tibble H 

PROVIDER: S-EPMC6624024 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature