Mining electronic health records to identify predictive factors associated with hospital admission for Campylobacter infections

Shang Ming Zhou; Muhammad Rahman; Samuel  Sheppard; Robin Howe; Ronan A. Lyons; Sinead Brophy

doi:10.1016/S0140-6736(17)33034-9

Mining electronic health records to identify predictive factors associated with hospital admission for Campylobacter infections

Shang Ming Zhou
, Muhammad Rahman
, Samuel Sheppard
, Robin Howe
, Ronan A. Lyons
, Sinead Brophy

Research output: Contribution to journal › Meeting Abstract › peer-review

Abstract

Campylobacter infection is one of the most common bacterial infections in human beings. Most cases of Campylobacter infection are not well explained by commonly recognised risk factors. Data-driven clinical rules offer patients an unprecedented measure of control over their own health information. This study sought to identify influential predictors from general practice (GP) data to predict the outcomes of Campylobacter infections—ie, being admitted to hospital or remaining with GP care.

Original language	English
Journal	Lancet
DOIs	https://doi.org/10.1016/S0140-6736(17)33034-9
Publication status	Published - Nov 2017
Externally published	Yes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Access to Document

10.1016/S0140-6736(17)33034-9

Cite this

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title = "Mining electronic health records to identify predictive factors associated with hospital admission for Campylobacter infections",

abstract = "Campylobacter infection is one of the most common bacterial infections in human beings. Most cases of Campylobacter infection are not well explained by commonly recognised risk factors. Data-driven clinical rules offer patients an unprecedented measure of control over their own health information. This study sought to identify influential predictors from general practice (GP) data to predict the outcomes of Campylobacter infections—ie, being admitted to hospital or remaining with GP care.",

author = "Zhou, \{Shang Ming\} and Muhammad Rahman and Samuel Sheppard and Robin Howe and Lyons, \{Ronan A.\} and Sinead Brophy",

year = "2017",

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language = "English",

journal = "Lancet",

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T1 - Mining electronic health records to identify predictive factors associated with hospital admission for Campylobacter infections

AU - Zhou, Shang Ming

AU - Rahman, Muhammad

AU - Sheppard, Samuel

AU - Howe, Robin

AU - Lyons, Ronan A.

AU - Brophy, Sinead

PY - 2017/11

Y1 - 2017/11

N2 - Campylobacter infection is one of the most common bacterial infections in human beings. Most cases of Campylobacter infection are not well explained by commonly recognised risk factors. Data-driven clinical rules offer patients an unprecedented measure of control over their own health information. This study sought to identify influential predictors from general practice (GP) data to predict the outcomes of Campylobacter infections—ie, being admitted to hospital or remaining with GP care.

AB - Campylobacter infection is one of the most common bacterial infections in human beings. Most cases of Campylobacter infection are not well explained by commonly recognised risk factors. Data-driven clinical rules offer patients an unprecedented measure of control over their own health information. This study sought to identify influential predictors from general practice (GP) data to predict the outcomes of Campylobacter infections—ie, being admitted to hospital or remaining with GP care.

U2 - 10.1016/S0140-6736(17)33034-9

DO - 10.1016/S0140-6736(17)33034-9

M3 - Meeting Abstract

SN - 0140-6736

JO - Lancet

JF - Lancet

ER -