Zina M Ibrahim
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Explore the profile of Zina M Ibrahim including associated specialties, affiliations and a list of published articles.
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11
Citations
198
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Recent Articles
1.
Ibrahim Z, Bean D, Searle T, Qian L, Wu H, Shek A, et al.
IEEE J Biomed Health Inform
. 2021 Jun;
26(1):423-435.
PMID: 34129509
The ability to perform accurate prognosis is crucial for proactive clinical decision making, informed resource management and personalised care. Existing outcome prediction models suffer from a low recall of infrequent...
2.
Iqbal E, Govind R, Romero A, Dzahini O, Broadbent M, Stewart R, et al.
PLoS One
. 2020 Dec;
15(12):e0243437.
PMID: 33290433
Objective: Mining the data contained within Electronic Health Records (EHRs) can potentially generate a greater understanding of medication effects in the real world, complementing what we know from Randomised control...
3.
Ibrahim Z, Wu H, Hamoud A, Stappen L, Dobson R, Agarossi A
J Am Med Inform Assoc
. 2020 Jan;
27(3):437-443.
PMID: 31951005
Objectives: Current machine learning models aiming to predict sepsis from electronic health records (EHR) do not account 20 for the heterogeneity of the condition despite its emerging importance in prognosis...
4.
Wu H, Hodgson K, Dyson S, Morley K, Ibrahim Z, Iqbal E, et al.
JMIR Med Inform
. 2019 Dec;
7(4):e14782.
PMID: 31845899
Background: Much effort has been put into the use of automated approaches, such as natural language processing (NLP), to mine or extract data from free-text medical records in order to...
5.
Bean D, Wu H, Iqbal E, Dzahini O, Ibrahim Z, Broadbent M, et al.
Sci Rep
. 2018 Mar;
8(1):4284.
PMID: 29511265
A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has been fixed in the paper.
6.
Wu H, Toti G, Morley K, Ibrahim Z, Folarin A, Jackson R, et al.
J Am Med Inform Assoc
. 2018 Jan;
25(5):530-537.
PMID: 29361077
Objective: Unlocking the data contained within both structured and unstructured components of electronic health records (EHRs) has the potential to provide a step change in data available for secondary research...
7.
Bean D, Wu H, Iqbal E, Dzahini O, Ibrahim Z, Broadbent M, et al.
Sci Rep
. 2017 Nov;
7(1):16416.
PMID: 29180758
Unknown adverse reactions to drugs available on the market present a significant health risk and limit accurate judgement of the cost/benefit trade-off for medications. Machine learning has the potential to...
8.
Iqbal E, Mallah R, Rhodes D, Wu H, Romero A, Chang N, et al.
PLoS One
. 2017 Nov;
12(11):e0187121.
PMID: 29121053
Adverse drug events (ADEs) are unintended responses to medical treatment. They can greatly affect a patient's quality of life and present a substantial burden on healthcare. Although Electronic health records...
9.
Iqbal E, Mallah R, Jackson R, Ball M, Ibrahim Z, Broadbent M, et al.
PLoS One
. 2015 Aug;
10(8):e0134208.
PMID: 26273830
Objectives: Electronic healthcare records (EHRs) are a rich source of information, with huge potential for secondary research use. The aim of this study was to develop an application to identify...
10.
Ibrahim Z, Ngom A
BMC Bioinformatics
. 2015 Mar;
16 Suppl 4:S3.
PMID: 25734691
Background: Cellular processes are known to be modular and are realized by groups of proteins implicated in common biological functions. Such groups of proteins are called functional modules, and many...