1 citations · 1 across the 1 of their papers we have counts for
7 papers
A Knowledge Distillation Ensemble Framework for Predicting Short and Long-term Hospitalisation Outcomes from Electronic Health Records Data
Zina M Ibrahim, Daniel Bean, Thomas Searle +7
The ability to perform accurate prognosis of patients is crucial for proactive clinical decision making, informed resource management and personalised care. Existing outcome predic…
Multi-domain Clinical Natural Language Processing with MedCAT: the Medical Concept Annotation Toolkit
Zeljko Kraljevic, Thomas Searle, Anthony Shek +15
Electronic health records (EHR) contain large volumes of unstructured text, requiring the application of Information Extraction (IE) technologies to enable clinical analysis. We pr…
Modeling Rare Interactions in Time Series Data Through Qualitative Change: Application to Outcome Prediction in Intensive Care Units
Zina Ibrahim, Honghan Wu, Richard Dobson
Many areas of research are characterised by the deluge of large-scale highly-dimensional time-series data. However, using the data available for prediction and decision making is h…
Identifying physical health comorbidities in a cohort of individuals with severe mental illness: An application of SemEHR
Rebecca Bendayan, Honghan Wu, Zeljko Kraljevic +9
Multimorbidity research in mental health services requires data from physical health conditions which is traditionally limited in mental health care electronic health records. In t…
The side effect profile of Clozapine in real world data of three large mental hospitals
Ehtesham Iqbal, Risha Govind, Alvin Romero +8
Objective: Mining the data contained within Electronic Health Records (EHRs) can potentially generate a greater understanding of medication effects in the real world, complementing…
On Classifying Sepsis Heterogeneity in the ICU: Insight Using Machine Learning
Zina Ibrahim, Honghan Wu, Ahmed Hamoud +3
Current machine learning models aiming to predict sepsis from Electronic Health Records (EHR) do not account for the heterogeneity of the condition, despite its emerging importance…