The use of Electronic Health Records (EHRs) in recording the details of patient interactions with healthcare services has generated large amounts of data with great potential for secondary usage in research. However, although the vast information available offers opportunities to improve care by learning from similar patients in parallel situations, there are great challenges in extracting correct and contextually meaningful knowledge due to the free-text, unstandardised and uncertainty-ridden form of clinical text.
The focus of the presented work has been on detecting concepts related to Adverse Drug Events (ADEs) from the EHR using Natural Language Processing (NLP) tools to transform the unstructured text into semantically meaningful annotated knowledge. Specifically, this thesis explored the potential of NLP to identify ADEs from mental health EHRs in order to understand how drugs are working in real-world settings, to complement the current body of knowledge from clinical trials. Four studies were performed on the EHRs of the South London and Maudsley (SLAM) NHS Foundation Trust, with some analyses further performed on two other large psychiatric NHS Trusts: Camden & Islington (C&I) NHS Foundation Trust and the Oxford Health (Oxford) NHS Foundation Trust.
The first study presented means to identify ADEs within an EHR, with a use case in identifying patients who have experienced Extra-Pyramidal Side Effects (EPSEs) at any point and achieved an overall 0.85 precision and 0.86 recall. The second study focused on anchoring ADEs to a point in time and achieved 0.89 precision and 0.86 recall in SLAM and 0.84 precision and 0.87 recall in C&I, contributing to the third study, which built a complete view of the patient medication and Adverse Drugs Reaction (ADR) profile. These methods were applied to study the side effect profile of Clozapine, a potent antipsychotic, in the three large mental health hospitals.
|Date of Award
|1 May 2021
|Richard Dobson (Supervisor), Zina Ibrahim (Supervisor) & Caroline Johnston (Supervisor)