PhD Project in Enhancing Chronic Disease Prediction Through AI-Driven Clinical Information Extraction from Electronic Health Records

The current Electronic Health Records (EHR) systems have a large volume of clinical free text documents available across healthcare networks. The unstructured data including clinical notes and discharge summaries represent 80% of the total EHR data. However, the current EHR system could not provide clear and concise insights for clinicians to understand the patients’ conditions as the quantity of information within clinical documents are overwhelming. Unstructured clinical documents play a vital role in comprehending a patient’s healthcare journey and offer valuable indicators for patient care. One of the essential uses is the clinical information extraction (IE) using rule-based NLP entities extraction, which allows automatic extraction of the useful information and saves clinicians’ time in synthesising the patient’s case/information. More recently, with the rapid advancements in artificial intelligence (AI), AI driven natural language processing (NLP) methods have been widely used in advancing EHR-based clinical research. This project will integrate clinical IE-based extraction methods and explore the use of deep learning-based models for automatic prediction of chronic diseases. It is expected that utilising AI-driven techniques that combine NLP with machine learning and deep learning will provide a solution for extracting valuable insights from unstructured text data and making predictions based on diverse and heterogeneous data sources.

The project aims to develop hybrid solutions by utilising linguistic methods and deep learning-based algorithms for the extraction of useful information from unstructured free text data and provide early diagnosis for certain chronic clinical medical conditions. It will enable the clinicians to better understand the development of various chronic diseases. A further objective will be sought to link the medical NLP analysis to other sources of data (e.g., patients’ physiological data) using appropriate data fusion algorithms for heterogeneous data sets.

This project will develop and apply NLP techniques to unstructured data obtained from clinical notes and discharge summaries. The open access datasets will be used including MIMIC-IV clinical database and i2b2 NLP research data sets. Deep learning models will be developed to predict the occurrence of a certain chronic disease (e.g., hypertension and diabetes) and discover the relationships between drugs, symptoms, and medical conditions. This project will focus on the following goals:

1. To explore novel algorithms, aimed at helping clinicians quickly extract information from unstructured clinical notes: we intend to develop novel ML/NLP algorithms for (1) extracting relevant information from unstructured clinical notes relevant to chronic diseases, (2) analysing the extracted information in the context of disease development and progression, (3) presenting the outcomes in an accessible way, facilitating informed decision-making and personalised patient care.

2. To investigate heterogeneous data fusion for more advanced clinical insights: Explore new methods for the integration of medical NLP analysis with other patient data sources, such as physiological data, to create a more comprehensive view of the patient’s health and gain deeper insights into the factors contributing to chronic conditions.

Funding Information

To be eligible for consideration for a Home DfE or EPSRC Studentship (covering tuition fees and maintenance stipend of approx. £19,237 per annum), a candidate must satisfy all the eligibility criteria based on nationality, residency and academic qualifications.

To be classed as a Home student, candidates must meet the following criteria and the associated residency requirements:

• Be a UK National,
or • Have settled status,
or • Have pre-settled status,
or • Have indefinite leave to remain or enter the UK.

Candidates from ROI may also qualify for Home student funding.

Previous PhD study MAY make you ineligible to be considered for funding.

Please note that other terms and conditions also apply.

Please note that any available PhD studentships will be allocated on a competitive basis across a number of projects currently being advertised by the School.

A small number of international awards will be available for allocation across the School. An international award is not guaranteed to be available for this project, and competition across the School for these awards will be highly competitive.

Academic Requirements:

The minimum academic requirement for admission is normally an Upper Second Class Honours degree from a UK or ROI Higher Education provider in a relevant discipline, or an equivalent qualification acceptable to the University.

Entrance requirements

Graduate
The minimum academic requirement for admission to a research degree programme is normally an Upper Second Class Honours degree from a UK or ROI HE provider, or an equivalent qualification acceptable to the University. Further information can be obtained by contacting the School.

International Students

For information on international qualification equivalents, please check the specific information for your country.

English Language Requirements

Evidence of an IELTS* score of 6.0, with not less than 5.5 in any component or equivalent qualification acceptable to the University is required (*taken within the last 2 years).

International students wishing to apply to Queen’s University Belfast (and for whom English is not their first language), must be able to demonstrate their proficiency in English in order to benefit fully from their course of study or research. Non-EEA nationals must also satisfy UK Visas and Immigration (UKVI) immigration requirements for English language for visa purposes.

For more information on English Language requirements for EEA and non-EEA nationals see: www.qub.ac.uk/EnglishLanguageReqs.