Keywords: MachineLearning, Classification, Tabular
Need: Predicting demand for healthcare resources often requires a wider system view of need as often focussing on a single part of a complex system misses the meta-dynamics that need to be addressed. The Leeds Office of the NHS West Yorkshire ICB and Leeds City Council have developed the Leeds Data Model (LDM) as a linked dataset designed to support population health management and health planning in Leeds.
This project would look to deliver a suite of analyses on the drivers of demand by population cohort characteristics (demographics, health need, geospatial). An alternative or extension would be to investigate the generation and comparison of pathways for different levels of interaction with the healthcare system.
Current Knowledge/Examples & Possible Techniques/Approaches: Previous work from the Networked Data Lab can be seen in The Health Foundation Analytics Lab - GitHub and background can be viewed at The Networked Data Lab - The Health Foundation. The Leeds Data Model has also been used within this Evaluation of the Leeds Neighbourhood Network.
Related Previous Internship Projects: Predicting the Impact of Health Inequalities
Enables Future Work: Analysis will inform the ICB’s demand modelling as well as demonstrating the value intrinsic in such linked data assets.
Outcome/Learning Objectives: Suite of reusable code for demand predictions.
Datasets:
The Leeds Data Model (LDM) contains pseudonymised population health data linking health and social care data from organisations including hospitals, GP practices, urgent care including 111 and ambulance, community and mental health, maternity, adult social care and population data. The data set contains information relating to 880,000 individuals
Desired skill set: When applying please highlight any experience around informatics, statistical analysis, deep learning, coding experience, or any other data science experience you feel relevant.
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