78 citations · 349 across the 18 of their papers we have counts for
19 papers
Deployment of a Free-Text Analytics Platform at a UK National Health Service Research Hospital: CogStack at University College London Hospitals
Kawsar Noor, Lukasz Roguski, Alex Handy +9
As more healthcare organisations transition to using electronic health record (EHR) systems it is important for these organisations to maximise the secondary use of their data to s…
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…
Estimating Redundancy in Clinical Text
Thomas Searle, Zina Ibrahim, James Teo +1
The current mode of use of Electronic Health Record (EHR) elicits text redundancy. Clinicians often populate new documents by duplicating existing notes, then updating accordingly.…
Predicting Depressive Symptom Severity through Individuals' Nearby Bluetooth Devices Count Data Collected by Mobile Phones: A Preliminary Longitudinal Study
Yuezhou Zhang, Amos A Folarin, Shaoxiong Sun +21
The Bluetooth sensor embedded in mobile phones provides an unobtrusive, continuous, and cost-efficient means to capture individuals' proximity information, such as the nearby Bluet…
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…
Measuring the effect of Non-Pharmaceutical Interventions (NPIs) on mobility during the COVID-19 pandemic using global mobility data
Berber T Snoeijer, Mariska Burger, Shaoxiong Sun +2
The implementation of governmental Non-Pharmaceutical Interventions (NPIs) has been the primary means of controlling the spread of the COVID-19 disease. The intended effect of thes…
Experimental Evaluation and Development of a Silver-Standard for the MIMIC-III Clinical Coding Dataset
Thomas Searle, Zina Ibrahim, Richard JB Dobson
Clinical coding is currently a labour-intensive, error-prone, but critical administrative process whereby hospital patient episodes are manually assigned codes by qualified staff f…
Comparing Natural Language Processing Techniques for Alzheimer's Dementia Prediction in Spontaneous Speech
Thomas Searle, Zina Ibrahim, Richard Dobson
Alzheimer's Dementia (AD) is an incurable, debilitating, and progressive neurodegenerative condition that affects cognitive function. Early diagnosis is important as therapeutics c…
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…
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…
Personalized acute stress classification from physiological signals with neural processes
Callum L. Stewart, Amos Folarin, Richard Dobson
Objective: A person's affective state has known relationships to physiological processes which can be measured by wearable sensors. However, while there are general trends those re…
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…
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…
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…
MedCATTrainer: A Biomedical Free Text Annotation Interface with Active Learning and Research Use Case Specific Customisation
Thomas Searle, Zeljko Kraljevic, Rebecca Bendayan +2
We present MedCATTrainer an interface for building, improving and customising a given Named Entity Recognition and Linking (NER+L) model for biomedical domain text. NER+L is often…
Efficiently Reusing Natural Language Processing Models for Phenotype-Mention Identification in Free-text Electronic Medical Records: Methodology Study
Honghan Wu, Karen Hodgson, Sue Dyson +6
Background: Many efforts have been put into the use of automated approaches, such as natural language processing (NLP), to mine or extract data from free-text medical records to co…
Application of Clinical Concept Embeddings for Heart Failure Prediction in UK EHR data
Spiros Denaxas, Pontus Stenetorp, Sebastian Riedel +3
Electronic health records (EHR) are increasingly being used for constructing disease risk prediction models. Feature engineering in EHR data however is challenging due to their hig…