Language Models for Enhancing Healthcare and Clinical Decision Support Systems

The aim of this Ph.D. project is to leverage Large Language Models (LLMs), such as ChatGPT or variations, to enhance clinical decision support systems in the medical domain. The goal is to develop advanced natural language understanding capabilities that can assist healthcare professionals in making more informed and context-aware decisions.

Example Research Topics:

1. Clinical Note Understanding: Investigate the use of LLMs to enhance the understanding of unstructured clinical notes. Develop models capable of extracting relevant information, including medical conditions, treatments, and patient histories, from free-text clinical narratives. Investigate seamless integration with electronic health records.
2. Multimodal Integration: Explore the integration of LLMs with other modalities of medical data, such as imaging reports, pathology reports, and genetic information. Develop models that can effectively combine information from diverse sources to provide a comprehensive view of decision-making.
3. Patient-Specific Recommendations: Develop LLM-based models that can generate patient-specific recommendations by considering individual medical histories, treatment responses, and other personalized factors. This involves training the model to understand and synthesize information from longitudinal patient records.
4. Explainability and Interpretability: Address the challenge of explainability in LLM-based CDSS. Develop methods to provide clear and interpretable explanations for the model's recommendations, fostering trust and understanding among healthcare professionals. Investigate techniques to enhance the robustness and generalization capabilities of the model, considering variations in medical practices and real-time decision support.
5. Impact and Contributions: The project aims to contribute to the field by advancing the capabilities of clinical decision support systems through the integration of Large Language Models. The developed models should not only demonstrate improved accuracy but also address practical challenges such as explainability, real-time support, and ethical considerations in healthcare applications. The project will evaluate the approach and model/algorithm performance on benchmark datasets and its application potential in collaboration with industry partners.

Loughborough University and Department of Computer Science: Loughborough University is one of UK top universities in research and innovation in science and engineering fields. 94% of Loughborough’s research impact is rated world-leading or internationally excellent in most recent Research Excellence Framework (REF 2021) assessment. Loughborough University has been awarded gold for student experience, gold for student outcomes and gold overall in the 2023 Teaching Excellence Framework- Triple Gold in TEF 2023.

The Department of Computer Science has an excellent research record in AI, machine learning, robotics, computer vision and data science. The successful candidate will have access to robotics and AI laboratories, high-spec computing facilities (e.g., A100 GPUs), HPC, and £5.8M DigLabs, complementing a £9m investment in research and teaching. You will have regular supervision meetings and work with a strong AI research team including over 30 PhDs/PDRAs/academic staff in the department. You will also have opportunities to join our ongoing research projects funded by UKRI, EPSRC, and industry and work closely with our academic and industry collaborators. You will take part in various outreach and impact generation activities, and develop your career profile throughout your PhD study.

94% of Loughborough’s research impact is rated world-leading or internationally excellent. REF 2021

Supervisors

Name of primary supervisor: Prof. Baihua Li

Primary supervisor email address: [Email Address Removed]

Primary supervisor telephone number: +441509222672

Entry requirements

Applicants should have, or expect to achieve, at least a 2:1 Honours degree in computer science or a related science and engineering subject. A relevant Master’s degree, experience in machine learning, deep learning and computer vision, strong programming skills, and passion in interdisciplinary research and innovation will be advantages.

English language requirements

Applicants must meet the minimum English language requirements. Further details are available on the International website.

How to apply

All applications should be made online. Under programme name, select Computer Science. Please quote the advertised reference number: CO/BL-Un4/2024 in your application. To avoid delays in processing your application, please ensure that you submit a CV and the minimum supporting documents. The following selection criteria used by the academic School to help them make a decision on your application, will be the same as we use for funded studentships.

We support excellent applicants from China to apply for China Scholarship Council (CSC) funding for PhD projects starting from October 2024. Applications would need to be made by 31st January 2024, to be eligible for funding. Further details on how to apply and associated application deadlines are available on our Research degree funding webpage.

Apply now