Predictive Health Analytics from Integrated Social Service Records: A Natural Language Processing (NLP)-Based Framework for Early Intervention

About the Project

Project Description

Social Care practitioners navigate critical information overload when evaluating at-risk populations, synthesizing dispersed information held across multiple sources, including social support documentation, health records and educational evaluations, among many others. This data dispersion masks crucial health vulnerability signals, such as food scarcity, housing precarity, intimate partner violence, that constitute early indicators of addressable health emergencies. Current approaches promote reactive rather than proactive interventions, contributing to avoidable emergency situations and widening health inequalities.

This PhD research will employ NLP to identify and surface health risk indicators embedded within heterogeneous social and health care data. The candidate will examine the potential and constraints of NLP methodologies, developing fundamental competency in deploying these computational techniques for cross-sector data integration in healthcare and social care.

The research programme will pursue four core aims:

1. Extract and prioritize health vulnerability markers from multisectoral social and health records
2. Predict individuals at risk of preventable health issues, including psychiatric emergencies or disease deterioration
3. Detect distributional health inequalities across population subgroups to facilitate targeted interventions
4. Generate interpretable risk prioritization to coordinate timely engagement between health and social support systems

The methodological approach will push forward several computational frontiers:

A) Temporal risk modelling- applyinglongitudinal pattern recognition techniques across sporadic, asynchronous interactions to identify escalating risk trajectories.

B) Uncertainty quantification: - flagging confidence levels in predictions, critical for high-stakes decision support systems in safety-critical contexts.

Additional technical considerations encompass integrating heterogeneous and multimodal data and implementing debiasing strategies to prevent amplification of existing health disparities.

Evaluation.The research will assess predictive performance (precision, recall, calibration), clinical utility (impact on preventable admissions and crisis interventions), and equity metrics (performance across demographic groups). Validation will involve retrospective analysis on historical data and prospective piloting with partner organizations.

Ethical and Privacy Framework

The research embeds critical safeguards reflecting the sensitive nature of personal information:

• Confidentiality-preserving computational architectures
• Interpretable decision-support mechanisms enabling clinical and social work professionals to validate model recommendations
• Participatory design engagement with service users, ensuring technological systems enhance agency rather than reinforcing stigma
• Systematic fairness assessment across protected demographic dimensions

Collaborative Ecosystem

The project will operate within partnerships with SCALE (Centre of Social Care and Artificial Intelligence Learning) and CASCADE (Children's Social Care Research and Development Centre), providing access to social and health data repositories, domain expertise in social care systems, engagement with service users and institutional stakeholders, and computational infrastructure support.

How to Apply

This project is accepting applications all year round, for self-funded candidates.

Mode of Study: Full-time or part-time

Please submit your application via Computer Science and Informatics - Study - Cardiff University

In the funding field of your application, indicate “I am applying for a self-funded PhD in Computer Science and Informatics”, and specify the project title and supervisors of this project in the text box provided.

Academic criteria: A 2:1 Honours undergraduate degree or a master's degree, in computing or a related subject. Applicants with appropriate professional experience are also considered. Degree-level mathematics (or equivalent) is required for research in some project areas.

Applicants must demonstrate English language proficiency. Students who do not have English as a first language must prove this by obtaining an IELTS score of at least 6.5 overall, with a minimum of 6.0 in each skills component. A full list of accepted qualifications is available here: https://www.cardiff.ac.uk/study/international/english-language-requirements/postgraduate

If you are interested, please contact Dr Carla Perez Almendros (perezalmendrosc@cardiff.ac.uk) sending your CV in the first instance. The application process requires you to develop an individual research proposal**jointly with the supervision team, which builds on the information provided in this advert.

Once you have developed the proposal with support from the supervisors, please submit your application following the instructions provided below.

Please submit your application via Computer Science and Informatics - Study - Cardiff University

In order to be considered candidates must submit the following information:

• In the ‘Research Proposal’ section of the application enter the name of the project you are applying to and upload your Individual research proposal. Your research proposal should not exceed 2000 words, including references and bibliography.
• A personal statement (as part of the university application form, or as a separate attachment, if you prefer).
• A CV. Guidance on CVs for a PhD position can be found on the FindAPhD website.
• Qualification certificates and Transcripts - original and English translation, if applicable.
• References x 2 which should be academic references. Please note you need to provide the reference documents as part of your application.
• Proof of English language (if applicable).

Interview– If the application meets all ofthe entrance requirements listed above, you will be invited to an interview.