Funding

Self-funded

Project code

COMP6451025

Department

School of Computing

Start dates

October, February and April

Application deadline

Applications accepted all year round

Applications are invited for a self-funded, 3 year full-time or 6 year part-time PhD project.

The PhD will be based in the School of Computing and will be supervised by Dr Alaa Mohasseb, Dr Rinat Khasinov, Professor Jim Briggs from the School of Computing, СÀ¶ÊÓƵ and  Professor Robert Stewart from the Department of Psychological Medicine, King's College London.

The work on this project could involve:

  • Obtaining sample datasets from CRIS and familiarising with data structures and contents.
  • Cleaning and preprocessing the data as necessary for subsequent NLP and ML tasks.
  • Carrying out experiments and building an analysis and predictive model.

Context

In mental health, Natural Language Processing (NLP) and machine learning play pivotal roles, particularly in understanding patients with psychosis. NLP analyses linguistic nuances in patient data, providing valuable insights into the manifestation of symptoms and emotional states. Machine learning models predict and identify patterns associated with psychosis onset, facilitating early intervention and personalised treatment. These technologies enhance diagnostic precision, enable continuous monitoring, and contribute to a comprehensive understanding of the patient journey. Ultimately, NLP and machine learning underscore the importance of leveraging innovative tools for nuanced insights, fostering empathy, and advancing more effective, tailored approaches in mental health care.

This PhD project will employ advanced machine learning and data analysis techniques to unravel the intricate journey of patients with psychosis within the secondary care system. Utilising the , the project seeks to extract meaningful patterns, predictive insights, and personalised trajectories to enhance the understanding of psychosis progression and inform targeted interventions.

The project aims to investigate the use of machine learning algorithms to identify distinct stages and trajectories in the patient journey of individuals with psychosis. Different approaches will be explored to extract valuable insights and uncover hidden patterns. In addition,  the research will focus on exploring and designing a predictive model using NLP and machine learning to anticipate critical points in the progression of psychosis. This model will consider a range of factors, including demographic information, clinical characteristics, and historical treatment responses, to inform timely and personalised interventions.

The research will contribute to the field of mental health by integrating machine learning techniques with comprehensive data analysis. By unravelling the patient journey in psychosis, the Identification of personalised psychosis trajectories based on machine learning analyses will offer a more nuanced understanding of the diverse patient experiences.

Funding

Visit the research subject area page for fees and funding information for this project.

Funding availability: Self-funded PhD students only. 

PhD full-time and part-time courses are eligible for the (conditions apply).

Bench fees

Some PhD projects may include additional fees – known as bench fees – for equipment and other consumables, and these will be added to your standard tuition fee. Speak to the supervisory team during your interview about any additional fees you may have to pay. Please note, bench fees are not eligible for discounts and are non-refundable.

Entry requirements

The entry requirements for a PhD or MPhil include an upper second class honours degree or equivalent in a relevant subject or a master's degree in an appropriate subject. Exceptionally, equivalent professional experience and/or qualifications will be considered. All applicants are subject to interview.

If English is not your first language, you'll need English language proficiency at a minimum of IELTS band 6.5 with no component score below 6.0.

If you don't meet the English language requirements yet, you can achieve the level you need by successfully completing a pre-sessional English programme before you start your course.

 

Good experience in the fundamentals of Natural Language Processing, Data Analytics and Machine Learning techniques, preferably good technical skills in text processing. Competent in applying NLP toolkits, such as NLTK or Spacy, or ML toolkits such as Scikit-Learn or Tensorflow.

Good programming skills in Python, analytical skills, and knowledge of foundations of computer science are also required. You should be able to think independently, including the formulation of research problems and have strong oral and written communication skills and good time management.

How to apply

We’d encourage you to contact Dr Alaa Mohasseb  (alaa.mohasseb@port.ac.uk) to discuss your interest before you apply, quoting the project code.

When you are ready to apply, please follow the 'Apply now' link on the Computing PhD subject area page and select the link for the relevant intake. Make sure you submit a personal statement, proof of your degrees and grades, details of two referees, proof of your English language proficiency and an up-to-date CV.  Our ‘How to Apply’ page offers further guidance on the PhD application process. 

When applying please quote project code: COMP6451025