Project ID NS-MH2023_54

ThemeNS-MH

Co Supervisor 1A IoPPN/Biostatistics and Health InformaticsWebsite

Co Supervisor 1B IoPPN/Psychosis StudiesWebsite

Developing and implementing an online tool to assess the future external performance of prognostic models using electronic health records in mental health

Clinical prediction modelling is the backbone of precision medicine. However, a recent meta-analysis revealed that most prediction models are not validated in new, real-life settings (external validation) and consequently not considered for implementation in clinical practice. External validation, however, is costly and time-consuming, so a tool which can assess the robustness of a model in different settings would improve the likelihood of models being implemented in clinical practice and at lower costs.

This project aims to develop a user-friendly simulation model based on electronic health records of different boroughs to assess the robustness of clinical prediction models under different external validation scenarios.

The simulation model will be developed and validated using an existing prognostic model to predict the risk of developing psychoses and using data from the NIHR CRIS system allowing to compare simulation results with existing external validation results. New machine learning models will be developed and assessed using the new tool. The final simulation model will then be implemented as a user-friendly programme on an online platform allowing researchers to carry out their assessment of prognostic models to support translational research 

Timetable
Year 1:
Literature review (External validation, psychosis)
Attending relevant modules of our Data Science MSc 
Establishing Patient and public focus group
Developing simulation framework 
Extracting CRIS-EHR using NLP

Year 2:
Programming simulation tool 
Validation using EHR

Year 3
Refinement of simulation tool
Assessment of different machine learning models
Implementation on cloud database

Year 4
Writing of thesis
Writing publications and reports to patients and public

One representative publication from each co-supervisor:

Fusar-Poli, P., Stringer, D., M. S. Durieux, A. & Stahl, D. Clinical-learning versus machine-learning for transdiagnostic prediction of psychosis onset in individuals at-risk. Transl Psychiatry 9, 259 (2019). https://doi.org/10.1038/s41398-019-0600-9

Fusar-Poli, P., Rutigliano, G., Stahl, D., Davies, C., Bonoldi, I., Reilly, T., & McGuire, P. (2017). Development and Validation of a Clinically Based Risk Calculator for the Transdiagnostic Prediction of Psychosis. JAMA psychiatry, 74(5), 493–500. https://doi.org/10.1001/jamapsychiatry.2017.0284

Oliver D, Spada G, Colling C, Broadbent M, Baldwin H, Patel R, Stewart R, Stahl D, Dobson R, McGuire P, Fusar-Poli P. Real-world implementation of precision psychiatry: Transdiagnostic risk calculator for the automatic detection of individuals at-risk of psychosis. Schizophr Res. 2021 Jan;227:52-60. doi: 10.1016/j.schres.2020.05.007. Epub 2020 Jun 19. PMID: 32571619; PMCID: PMC7875179.