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

Elsevier BV
Publication Type:
Journal Article
International journal of medical informatics, 2020, 145, pp. 104324
Issue Date:
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BACKGROUND:Bayesian modelling and statistical text analysis rely on informed probability priors to encourage good solutions. OBJECTIVE: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. METHOD: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. RESULT:Discharge reports are best fit by the truncated power-law and lognormal distributions. Discharge reports appear to be near-Zipfian by having the truncated power-law provide superior fits over a pure power-law. CONCLUSION: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 and non-parametric models that capture power-law behavior.
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