Article ; Online: Supervised Bayesian Statistical Learning to Identify Prognostic Risk Factor Patterns from Population Data.
Studies in health technology and informatics
2020 Volume 270, Page(s) 422–426
Abstract: Current methods for building risk models assume averaged uniform effects across populations. They use weighted sums of individual risk factors from regression models with only a few interactions, such as age. This does not allow risk factor effects to ... ...
Abstract | Current methods for building risk models assume averaged uniform effects across populations. They use weighted sums of individual risk factors from regression models with only a few interactions, such as age. This does not allow risk factor effects to vary in different morbidity contexts. This study modified a supervised Bayesian statistical learning method of topic modelling, allowing individual factors to have different effects depending on a patient's other comorbidity. This study used topic modelling to assess more than 71,000 unique risk factors in a population cohort of 1.4 million adults within routine data. The model learnt prognostically important risk factor patterns that predicted 5 year survival, and the resulting model achieved excellent calibration and discrimination with a C statistic of 0.9 in a held out validation cohort. The model explained 92% of the observed variation in 5 year survival in the population. This paper validates using survival supervised Bayesian topic modelling within large routine electronic population health data to identify prognostically important risk factor patterns. |
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MeSH term(s) | Bayes Theorem ; Calibration ; Cohort Studies ; Humans ; Prognosis ; Risk Factors |
Language | English |
Publishing date | 2020-06-20 |
Publishing country | Netherlands |
Document type | Journal Article |
ISSN | 1879-8365 |
ISSN (online) | 1879-8365 |
DOI | 10.3233/SHTI200195 |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
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