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.
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.
If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.
The University offers the following levels of support:
Full award (full-time PhD fees + DfE level of maintenance grant + RTSG for 3 years).
This scholarship will cover full-time PhD tuition fees and provide the recipient with £18,000 (tbc) maintenance grant 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.
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.
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.
Part award (full-time PhD fees + 50% DfE level of maintenance grant + RTSG for 3 years).
This scholarship will cover full-time PhD tuition fees and provide the recipient with £8,000 maintenance grant 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.
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.
Fees only award (PhD fees + RTSG for 3 years).
This scholarship will cover full-time PhD tuition fees 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.
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.
The scholarship will cover tuition fees at the Home rate and a maintenance allowance of £18,000 (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.
Due consideration should be given to financing your studies. Further information on cost of living
Kostrinsky-Thomas, A.L., Hisama, F.M. and Payne, T.H., 2021. Searching the PDF Haystack: automated knowledge discovery in scanned EHR documents. Applied Clinical Informatics, 12(02), pp.245-250.
Bai, L., Mulvenna, M.D., Wang, Z. and Bond, R., 2021, June. Clinical entity extraction: Comparison between metamap, ctakes, clamp and amazon comprehend medical. In 2021 32nd Irish Signals and Systems Conference (ISSC) (pp. 1-6). IEEE.
Johnson, A.E., Pollard, T.J., Shen, L., Lehman, L.W.H., Feng, M., Ghassemi, M., Moody, B., Szolovits, P., Anthony Celi, L. and Mark, R.G., 2016. MIMIC-III, a freely accessible critical care database. Scientific data, 3(1), pp.1-9.
Juhn, Y. and Liu, H., 2020. Artificial intelligence approaches using natural language processing to advance EHR-based clinical research. Journal of Allergy and Clinical Immunology, 145(2), pp.463-469.
Kaswan, K.S., Gaur, L., Dhatterwal, J.S. and Kumar, R., 2021. AI-based natural language processing for the generation of meaningful information electronic health record (EHR) data. In Advanced AI Techniques and Applications in Bioinformatics (pp. 41-86). CRC Press.
Li, I., Pan, J., Goldwasser, J., Verma, N., Wong, W.P., Nuzumlalı, M.Y., Rosand, B., Li, Y., Zhang, M., Chang, D. and Taylor, R.A., 2022. Neural natural language processing for unstructured data in electronic health records: A review. Computer Science Review, 46, p.100511.
Murphy, S.N., Weber, G., Mendis, M., Gainer, V., Chueh, H.C., Churchill, S. and Kohane, I., 2010. Serving the enterprise and beyond with informatics for integrating biology and the bedside (i2b2). Journal of the American Medical Informatics Association, 17(2), pp.124-130.
Submission deadline
Monday 27 February 2023
04:00PM
Interview Date
week commencing 17 April 2023
Preferred student start date
18 Sept 2023
Telephone
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Email
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