Title: Leveraging natural language processing, genomics and imaging to evaluate the determinants of adverse cardiovascular events
Funder: The Academy of Medical Sciences

Abstract: My research vision is to establish a deeply curated database for Barts BioResource (BBR), the biorepository of Barts Health NHS Trust, with the diagnosis labels from electronic health records (EHR), imaging and genomic data, which will allow investigations into the mechanisms and predictability of major adverse cardiovascular events (MACE). The objectives are to: (i) develop a bespoke natural language processing (NLP) pipeline for EHR annotation; (ii) enrich BBR with machine learning-derived cardiac imaging phenotypes and genetic risk scores and (iii) interrogate the multi-omics dataset to understand the determinants of MACE. BBR has recruited over 23,000 patients, of which 3,727 had donated blood and 13,644 had cardiovascular magnetic resonance (CMR) imaging. Currently, the rich clinical data from EHR and cardiac imaging reports are mostly inaccessible to data-driven research. To address this, a specialised NLP pipeline adapted from Medical Concept Annotation Tool will be created to extract the biomedical terms from EHR and the imaging reports. Structured clinical data will be integrated with machine-learning-derived CMR imaging phenotypes and the genetic risk score of each MACE subtype. Finally, the clinical, imaging and genomic signatures of MACE will be elucidated using clustering analyses. Patient-specific risk prediction models will be developed using the elastic-net regression and gradient boosting techniques. I envisage that this project will produce an invaluable, highly enriched data resource and a scalable, trained NLP pipeline which will be beneficial for many prospective research projects. The in-depth analysis of multi-dimensional data may yield novel biological insights into the mediators and predictors of MACE.

Lay abstract: Electronic health records (EHR) contain detailed information on symptoms, medications, allergies, clinical narrative and patient feedback. The majority of EHR data is in a ‘free-text’ format. While the ‘free-text’ data can be easily read by humans, they are usually not understood by the computers, creating a major challenge for clinical and research applications. Natural language processing (NLP), a branch of artificial intelligence (AI), is a new technology that can extract salient biomedical information from the text data and translate them into a format suitable for computer algorithms. We aim to create a rich dataset for Barts BioResource (BBR), the research database of Barts Health NHS Trust, which has recruited over 23,000 patients. We will use an NLP system called Medical Concept Annotation Tool (MedCAT) to organise the EHR and cardiac imaging reports of BBR participants. This data will be supplemented with the cardiac imaging measurements made by the AI algorithms and the genetic data obtained from donated blood samples. The combined dataset will be used to detect the hidden patterns that define the sub-groups of patients at risk of harmful outcomes such as heart attack and stroke. We will develop personalised risk prediction algorithms using a “data-driven” approach empowered by advanced statistical techniques. At a minimum, this project will produce a rich research-ready dataset together with a fine-tuned NLP system for future research. We anticipate that the integrated multi-layered data will produce new knowledge to identify and treat the individuals who are at high-risk of life-threatening cardiovascular events.

Research activity codes: 2.5 RESEARCH DESIGN AND METHODOLOGIES (AETIOLOGY);2.1 BIOLOGICAL AND ENDOGENOUS FACTORS
Health categories: CARDIOVASCULAR