  Bayesian modelling and statistical text analysis rely on informed probability
priors to encourage good solutions. This paper empirically analyses whether
text in medical discharge reports follow Zipf's law, a commonly assumed
statistical property of language where word frequency follows a discrete power
law distribution. We examined 20,000 medical discharge reports from the
MIMIC-III dataset. Methods included splitting the discharge reports into
tokens, counting token frequency, fitting power law distributions to the data,
and testing whether alternative distributions--lognormal, exponential,
stretched exponential, and truncated power law--provided superior fits to the
data. Results show that discharge reports are best fit by the truncated power
law and lognormal distributions. Our findings suggest that Bayesian modelling
and statistical text analysis of discharge report text would benefit from using
truncated power law and lognormal probability priors.
