Empirical analysis of Zipf’s law, power law, and lognormal distributions in medical discharge reports

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