PhD Study : Artificial intelligence and natural language processing for chronic diseases prediction

Summary

The proposed project originates from previous work which aimed to extract clinical entities from clinical notes (Bai et al., 2021). It aligns well with the school research focus in the areas of healthcare and interdisciplinary research. COVID-19 pandemic has led to a significant increased need for rapid updates in patients electronic health records (EHR). The current 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 (Li et al., 2022). However, the current electronic health records (EHR) system could not provide clear information for clinicians to understand the patients’ conditions as the quantity of information within clinical documents are overwhelming (Kostrinsky-Thomas et al., 2021). Unstructured clinical documents form a critical part in understanding the patient’s journey and provide indication on patient care. One of the essential uses is the clinical information extraction (IE) using rule-based NLP entities extraction, which allows automatically extraction of the useful information and saves clinicians’ time in synthesising the patient’s case/information (Bai et al., 2021).

This project will integrate previous work and also explore the use of deep learning based models for automatic prediction of chronic diseases. Recently, with the advancement in artificial intelligence (AI),  AI based natural language processing (NLP) methods have been widely used in advancing EHR-based clinical research (Juhn and Liu, 2020; Kaswan et al., 2021). AI driven methods combining natural language processing and machine learning/deep learning are expected to provide a solution by capturing useful information from unstructured text data and make predictions based on heterogeneous data.

This project will develop and apply NLP techniques on unstructured data from clinical notes and discharge summaries. The open dataset will be used including MIMIC-III clinical database (Johnson et al., 2016) and i2b2 NLP research data sets (Murphy et al., 2010). 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.

The project aims to develop hybrid solutions by utilising linguistic solutions 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 the chronic disease and provide predictions. A further contribution 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.

Essential criteria

Applicants should hold, or expect to obtain, a First or Upper Second Class Honours Degree in a subject relevant to the proposed area of study.

We may also consider applications from those who hold equivalent qualifications, for example, a Lower Second Class Honours Degree plus a Master’s Degree with Distinction.

In exceptional circumstances, the University may consider a portfolio of evidence from applicants who have appropriate professional experience which is equivalent to the learning outcomes of an Honours degree in lieu of academic qualifications.

Desirable Criteria

If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.

• First Class Honours (1st) Degree
• Masters at 70%
• Experience using research methods or other approaches relevant to the subject domain
• Work experience relevant to the proposed project
• Publications - peer-reviewed

Funding and eligibility

The University offers the following levels of support:

Vice Chancellors Research Studentship (VCRS)

The following scholarship options are available to applicants worldwide:

• Full Award: (full-time tuition fees + £19,000 (tbc))
• Part Award: (full-time tuition fees + £9,500)
• Fees Only Award: (full-time tuition fees)

These scholarships will cover full-time PhD tuition fees for three years (subject to satisfactory academic performance) and will provide a £900 per annum research training support grant (RTSG) to help support the PhD researcher.

Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.

Please note: you will automatically be entered into the competition for the Full Award, unless you state otherwise in your application.

Department for the Economy (DFE)

The scholarship will cover tuition fees at the Home rate and a maintenance allowance of £19,000 (tbc) per annum for three years (subject to satisfactory academic performance).

This scholarship also comes with £900 per annum for three years as a research training support grant (RTSG) allocation to help support the PhD researcher.

• Candidates with pre-settled or settled status under the EU Settlement Scheme, who also satisfy a three year residency requirement in the UK prior to the start of the course for which a Studentship is held MAY receive a Studentship covering fees and maintenance.
• Republic of Ireland (ROI) nationals who satisfy three years’ residency in the UK prior to the start of the course MAY receive a Studentship covering fees and maintenance (ROI nationals don’t need to have pre-settled or settled status under the EU Settlement Scheme to qualify).
• Other non-ROI EU applicants are ‘International’ are not eligible for this source of funding.
• Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.

Due consideration should be given to financing your studies. Further information on cost of living