Predicting the risk of asthma attacks in children, adolescents and adults: protocol for a machine learning algorithm derived from a primary care-based retrospective cohort

Zain U. Hussain; Syed Ahmar Shah; Mome Mukherjee; Aziz Sheikh

doi:10.1136/ bmjopen-2019-036099

Predicting the risk of asthma attacks in children, adolescents and adults: protocol for a machine learning algorithm derived from a primary care-based retrospective cohort

Zain U. Hussain, Syed Ahmar Shah, Mome Mukherjee, Aziz Sheikh

Research output: Contribution to journal › Article › peer-review

Abstract

Most asthma attacks and subsequent deaths are potentially preventable. We aim to develop a prognostic tool for identifying patients at high risk of asthma attacks in primary care by leveraging advances in machine learning.

Original language	English
Journal	BMJ Open
DOIs	https://doi.org/10.1136/ bmjopen-2019-036099
Publication status	Published - 23 Jul 2020

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10.1136/ bmjopen-2019-036099

ASthma_ML_OPCRD_Revision_Clean_FullAccepted author manuscript, 250 KB
Predicting the risk of asthma attacks using machine learning protocolFinal published version, 337 KB

https://bmjopen.bmj.com/content/10/7/e036099.info

BREATHE - The Health Data Research Hub for Respiratory Health
Sheikh, A.
MRC
1/10/19 → 31/03/23
Project: Research

Cite this

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title = "Predicting the risk of asthma attacks in children, adolescents and adults: protocol for a machine learning algorithm derived from a primary care-based retrospective cohort ",

abstract = "Most asthma attacks and subsequent deaths are potentially preventable. We aim to develop a prognostic tool for identifying patients at high risk of asthma attacks in primary care by leveraging advances in machine learning.",

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AU - Sheikh, Aziz

PY - 2020/7/23

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Predicting the risk of asthma attacks in children, adolescents and adults: protocol for a machine learning algorithm derived from a primary care-based retrospective cohort

Abstract

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Projects

BREATHE - The Health Data Research Hub for Respiratory Health

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