A causal hidden Markov model corrects heterogeneous diagnostic bias in clinical prediction models by estimating an individual's diagnosis probability under a uniform reference testing rate.
To improve computational efficiency, the parameters are initialised to those of the fit in the overall data
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Correcting heterogeneous diagnostic bias when developing clinical prediction models using causal hidden Markov models
A causal hidden Markov model corrects heterogeneous diagnostic bias in clinical prediction models by estimating an individual's diagnosis probability under a uniform reference testing rate.