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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 topicdoi:10.3233/shti200195 pmid:32570419 fatcat:z5hokogcerct7c5zpnn3thwc7m