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

Allbwn ymchwil: Cyfraniad at gyfnodolyn › Cyfarfod Abstract › adolygiad gan gymheiriaid

Crynodeb

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.

Iaith wreiddiol	Saesneg
Cyfnodolyn	Lancet
Dynodwyr Gwrthrych Digidol (DOIs)	https://doi.org/10.1016/S0140-6736(17)33034-9
Statws	Cyhoeddwyd - Tach 2017
Cyhoeddwyd yn allanol	Ie

NDC y CU

Mae’r allbwn hwn yn cyfrannu at y Nod(au) Datblygu Cynaliadwy canlynol

NDC 3 Iechyd a Llesiant Da

Mynediad at Ddogfen

10.1016/S0140-6736(17)33034-9

Dyfynnu hyn

@article{576da90ff5564126972e68aed986bea9,

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",

month = nov,

doi = "10.1016/S0140-6736(17)33034-9",

language = "English",

journal = "Lancet",

issn = "0140-6736",

}

TY - JOUR

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 -