Outputs (8)

Development of an open-source tool for risk assessment in pulmonary endarterectomy (2024)
Journal Article
Liley, J., Bunclark, K., Newnham, M., Cannon, J., Sheares, K., Taboada, D., Ng, C., Screaton, N., Jenkins, D., Pepke-Zaba, J., & Toshner, M. (online). Development of an open-source tool for risk assessment in pulmonary endarterectomy. European Respiratory Journal, https://doi.org/10.1183/13993003.01001-2024

Risk prediction tools are routinely utilised in cardiothoracic surgery but have not been developed for pulmonary endarterectomy (PEA). There is no data on whether patients undergoing PEA may benefit from a tailored risk modelling approach. We develop... Read More about Development of an open-source tool for risk assessment in pulmonary endarterectomy.

Holdout Sets for Safe Predictive Model Updating (2024)
Journal Article
Haidar-Wehbe, S., Emerson, S. R., Aslett, L. J., & Liley, J. (in press). Holdout Sets for Safe Predictive Model Updating. Annals of Applied Statistics,

Predictive risk scores for adverse outcomes are increasingly crucial in guiding health interventions. Such scores may need to be periodically updated due to change in the distributions they model. However, directly updating risk scores used to guide... Read More about Holdout Sets for Safe Predictive Model Updating.

Development and assessment of a machine learning tool for predicting emergency admission in Scotland (2024)
Journal Article
Liley, J., Bohner, G., Emerson, S., Mateen, B., Borland, K., Carr, D., Heald, S., Oduro, S., Ireland, J., Moffat, K., Porteous, R., Riddell, S., Cunningham, N., Holmes, C., Payne, K., Vollmer, S., Vallejos, C., & Aslett, L. (2024). Development and assessment of a machine learning tool for predicting emergency admission in Scotland. Nature, 7, Article 277. https://doi.org/10.1038/s41746-024-01250-1

Emergency admissions (EA), where a patient requires urgent in-hospital care, are a major challenge for healthcare systems. The development of risk prediction models can partly alleviate this problem by supporting primary care interventions and public... Read More about Development and assessment of a machine learning tool for predicting emergency admission in Scotland.

Differential behaviour of a risk score for emergency hospital admission by demographics in Scotland -a retrospective study (2024)
Journal Article
Thoma, I., Rogers, S., Ireland, J., Porteous, R., Borland, K., Vallejos, C. A., Aslett, L. J. M., & Liley, J. (in press). Differential behaviour of a risk score for emergency hospital admission by demographics in Scotland -a retrospective study. PLoS Digital Health,

The Scottish Patients at Risk of Re-Admission and Admission (SPARRA) score predicts individual risk of emergency hospital admission for approximately 80% of the Scottish population. It was developed using routinely collected electronic health records... Read More about Differential behaviour of a risk score for emergency hospital admission by demographics in Scotland -a retrospective study.

Ethical considerations of use of hold-out sets in clinical prediction model management (2024)
Journal Article
Chislett, L., Aslett, L. J. M., Davies, A. R., Vallejos, C. A., & Liley, J. (online). Ethical considerations of use of hold-out sets in clinical prediction model management. AI and Ethics, https://doi.org/10.1007/s43681-024-00561-z

Clinical prediction models are statistical or machine learning models used to quantify the risk of a certain health outcome using patient data. These can then inform potential interventions on patients, causing an effect called performative predictio... Read More about Ethical considerations of use of hold-out sets in clinical prediction model management.

Effects of a novel, brief psychological therapy (Managing Unusual Sensory Experiences) for hallucinations in first episode psychosis (MUSE FEP): findings from an exploratory randomised controlled trial. (2024)
Journal Article
Dudley, R., Dodgson, G., Common, S., Ogundimu, E., Liley, J., O’Grady, L., Watson, F., Gibbs, C., Arnott, B., Fernyhough, C., Alderson-Day, B., & Aynsworth, C. (2024). Effects of a novel, brief psychological therapy (Managing Unusual Sensory Experiences) for hallucinations in first episode psychosis (MUSE FEP): findings from an exploratory randomised controlled trial. Journal of Psychiatric Research, 174, 289-296. https://doi.org/10.1016/j.jpsychires.2024.04.031

Hallucinations are a common feature of psychosis, yet access to effective psychological treatment is limited. The Managing Unusual Sensory Experiences for First-Episode-Psychosis (MUSE-FEP) trial aimed to establish the feasibility and acceptability o... Read More about Effects of a novel, brief psychological therapy (Managing Unusual Sensory Experiences) for hallucinations in first episode psychosis (MUSE FEP): findings from an exploratory randomised controlled trial..

Shared and Distinct Genomics of Chronic Thromboembolic Pulmonary Hypertension and Pulmonary Embolism. (2024)
Journal Article
Liley, J., Newnham, M., Bleda, M., Bunclark, K., Auger, W., Barbera, J. A., Bogaard, H., Delcroix, M., Fernandes, T. M., Howard, L., Jenkins, D., Lang, I., Mayer, E., Rhodes, C., Simpson, M., Southgate, L., Trembath, R., Wharton, J., Wilkins, M. R., Gräf, S., …Toshner, M. (2024). Shared and Distinct Genomics of Chronic Thromboembolic Pulmonary Hypertension and Pulmonary Embolism. American Journal of Respiratory and Critical Care Medicine, 209(12), 1477-1485. https://doi.org/10.1164/rccm.202307-1236OC

Chronic Thromboembolic Pulmonary Hypertension involves formation and non-resolution of thrombus, dysregulated inflammation, angiogenesis and the development of a small vessel vasculopathy. We aimed to establish the genetic basis of chronic thromboemb... Read More about Shared and Distinct Genomics of Chronic Thromboembolic Pulmonary Hypertension and Pulmonary Embolism..

Model updating after interventions paradoxically introduces bias (2021)
Presentation / Conference Contribution
Liley, J., Emerson, S., Mateen, B., Vallejos, C., Aslett, L., & Vollmer, S. (2021, December). Model updating after interventions paradoxically introduces bias. Presented at The 24th International Conference on Artificial Intelligence and Statistics, Virtual