Tracking trajectories of multiple long-term conditions using dynamic patient-cluster associations

Kremer, Ron; Raza, Syed Mohib; Eto, Fabiola; Casement, John; Atallah, Christian; Finer, Sarah; Lendrem, Dennis; Barnes, Michael R.; Reynolds, Nick; Missier, Paolo

doi:10.1109/bigdata55660.2022.10021034

Cited by 2 publications

(1 citation statement)

References 29 publications

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“…to capture non-linear patterns between features and EA risk) and the incorporation of features derived by a topic model which extracts more granular information (with respect to the manually curated features used by SPARRA v 3) from past diagnoses and prescriptions data. The latter can be thought of as a proxy for multi-morbidity patterns, in that topic models identify patterns of diagnoses and prescriptions which commonly occur together [Kremer et al, 2022], which can be seen to occur in our data (Supplementary Table S4). The use of an ensemble of models also allows stronger models and methods to dominate the final predictor, and weaker models to be discarded.…”

Section: Discussionmentioning

confidence: 99%

Development and assessment of a machine learning tool for predicting emergency admission in Scotland

Liley

Bohner

Emerson

et al. 2021

Preprint

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Avoiding emergency hospital admission (EA) is advantageous to individual health and the healthcare system. We develop a statistical model estimating risk of EA for most of the Scottish population (>4.8M individuals) using electronic health records, such as hospital episodes and prescribing activity. We demonstrate good predictive accuracy (AUROC 0.80), calibration and temporal stability. We find strong prediction of respiratory and metabolic EA, show a substantial risk contribution from socioeconomic decile, and highlight an important problem in model updating. Our work constitutes a rare example of a population-scale machine learning score to be deployed in a healthcare setting.

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Section: Discussionmentioning

confidence: 99%

Development and assessment of a machine learning tool for predicting emergency admission in Scotland

Liley

Bohner

Emerson

et al. 2021

Preprint

View full text Add to dashboard Cite

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Development and assessment of a machine learning tool for predicting emergency admission in Scotland

Liley,

Bohner,

Emerson

et al. 2024

npj Digit. Med.

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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 health planning. Here, we introduce SPARRAv4, a predictive score for EA risk that will be deployed nationwide in Scotland. SPARRAv4 was derived using supervised and unsupervised machine-learning methods applied to routinely collected electronic health records from approximately 4.8M Scottish residents (2013-18). We demonstrate improvements in discrimination and calibration with respect to previous scores deployed in Scotland, as well as stability over a 3-year timeframe. Our analysis also provides insights about the epidemiology of EA risk in Scotland, by studying predictive performance across different population sub-groups and reasons for admission, as well as by quantifying the effect of individual input features. Finally, we discuss broader challenges including reproducibility and how to safely update risk prediction models that are already deployed at population level.

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Tracking trajectories of multiple long-term conditions using dynamic patient-cluster associations

Cited by 2 publications

References 29 publications

Development and assessment of a machine learning tool for predicting emergency admission in Scotland

Development and assessment of a machine learning tool for predicting emergency admission in Scotland

Development and assessment of a machine learning tool for predicting emergency admission in Scotland

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