Search Results for author:
Found 6 papers, 4 papers with code
Improving Extraction of Clinical Event Contextual Properties from Electronic Health Records: A Comparative Study
Electronic Health Records are large repositories of valuable clinical data, with a significant portion stored in unstructured text format.
Validating transformers for redaction of text from electronic health records in real-world healthcare
Protecting patient privacy in healthcare records is a top priority, and redaction is a commonly used method for obscuring directly identifiable information in text.
Foresight -- Generative Pretrained Transformer (GPT) for Modelling of Patient Timelines using EHRs
We explore how temporal modelling of patients from free text and structured data, using deep generative transformers can be used to forecast a wide range of future disorders, substances, procedures or findings.
MedGPT: Medical Concept Prediction from Clinical Narratives
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 the data available on all other patients.
A Knowledge Distillation Ensemble Framework for Predicting Short and Long-term Hospitalisation Outcomes from Electronic Health Records Data
The ability to perform accurate prognosis of patients is crucial for proactive clinical decision making, informed resource management and personalised care.
Multi-domain Clinical Natural Language Processing with MedCAT: the Medical Concept Annotation Toolkit
Electronic health records (EHR) contain large volumes of unstructured text, requiring the application of Information Extraction (IE) technologies to enable clinical analysis.
