PhD Studentship Opportunity (2026–27) – Opportunities for Youth

The London School of Hygiene & Tropical Medicine (LSHTM) invites purposes for a slicing-edge doctoral analysis alternative exploring how Large Language Models (LLMs) can remodel the way in which advanced illnesses are recognized in massive-scale Electronic Health Record (EHR) databases.

This mission addresses probably the most urgent challenges in fashionable well being knowledge science: growing correct, clear and environment friendly strategies for phenotyping—the method of figuring out affected person teams with particular illnesses or circumstances from routine medical knowledge. With medical datasets increasing quickly, AI-pushed approaches supply the potential for main advances in epidemiological analysis.

Facilitating Complex Phenotyping for Electronic Health Records Using Large Language Models  MRC London Intercollegiate Doctoral Training Partnership (MRC LID) Studentship
Project obtainable for 2026/27 entry
Full-time or Part-time

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Supervisory Team

Primary Supervisor
Dr Julian Matthewman, LSHTM
Faculty of Epidemiology & Population Health
Department of Non-communicable Disease Epidemiology
Email: julian.matthewman@lshtm.ac.uk

Co-Supervisor
Professor Sinéad Langan, LSHTM
Faculty of Epidemiology & Population Health
Department of Non-communicable Disease Epidemiology
Email: sinead.langan@lshtm.ac.uk

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Project Overview

Accurate identification of sufferers with advanced circumstances inside EHR programs is important for excessive-high quality epidemiological analysis. Traditional phenotyping strategies rely closely on handbook knowledgeable interpretation and are sometimes tough to scale—notably for illnesses involving subtypes, diagnostic uncertainty, or multifaceted medical histories.

This PhD mission will examine how Large Language Models—with their capability to course of and interpret huge quantities of medical info—can improve and speed up phenotyping workflows. The profitable candidate will develop a clear, reproducible framework for LLM-assisted phenotyping and apply it to create an Atlas of Complex Disease Phenotypes utilizing the UK’s CPRD Aurum main care database.

The work will generate a novel methodology of broad worth to the well being knowledge science group, demonstrating the sensible affect of superior AI applied sciences on epidemiological analysis.

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Key Objectives

1. Review the present panorama of LLM purposes in medical classification and phenotyping, together with the event of a residing scoping evaluate.

2. Design a reproducible framework integrating medical experience with LLM-derived insights for excessive-high quality phenotyping.

3. Apply and validate the framework by creating phenotype definitions for a spread of advanced illnesses in CPRD Aurum—doubtless inside pores and skin or inflammatory illness areas, relying on candidate and supervisor experience.

4. Evaluate epidemiological metrics (e.g. incidence, prevalence) produced utilizing the brand new LLM-derived phenotypes and evaluate them with established strategies.

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Skills You Will Develop

• Large Language Model analysis, immediate design and high quality-tuning

• Electronic Health Record knowledge administration and evaluation

• Epidemiological study design and interpretation

• Quantitative evaluation utilizing R or Python

• Natural Language Processing strategies for medical textual content

• Reproducible analysis and open science practices

This mission aligns with MRC LID themes in Health Data Science, Translational & Implementation Research, and Global Health.

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Data Resource

You will work primarily with CPRD Aurum, a complete UK main care dataset with linkages to hospital data and different well being knowledge sources. It contains diagnoses, signs, prescriptions, referrals and take a look at outcomes.

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Eligibility & Entry Requirements

Applicants should meet LSHTM’s customary doctoral eligibility standards and will display:

• A robust quantitative background and well being-associated coaching

• A Master’s diploma in epidemiology, well being knowledge science, medical statistics, or a associated area

• Proficiency in Python or R

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Study Format & Location

• Full-time: Yes

• Part-time: Yes

• Primary location: LSHTM, Bloomsbury, London

• Travel necessities: None past customary convention attendance (as much as three throughout the studentship)

Students funded by means of MRC LID are anticipated to work usually on website.

For extra info click on here

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