18 citations · 24 across the 4 of their papers we have counts for
6 papers
MedGPT: Medical Concept Prediction from Clinical Narratives
Zeljko Kraljevic, Anthony Shek, Daniel Bean +3
The data available in Electronic Health Records (EHRs) provides the opportunity to transform care, and the best way to provide better care for one patient is through learning from…
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…
Comparative Analysis of Text Classification Approaches in Electronic Health Records
Aurelie Mascio, Zeljko Kraljevic, Daniel Bean +4
Text classification tasks which aim at harvesting and/or organizing information from electronic health records are pivotal to support clinical and translational research. However t…
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…
MedCAT -- Medical Concept Annotation Tool
Zeljko Kraljevic, Daniel Bean, Aurelie Mascio +5
Biomedical documents such as Electronic Health Records (EHRs) contain a large amount of information in an unstructured format. The data in EHRs is a hugely valuable resource docume…