Zipf定律、幂律与对数正态分布在出院报告中的实证分析
Empirical Analysis of Zipf's Law, Power Law, and Lognormal Distributions in Medical Discharge Reports
摘要：
贝叶斯建模和统计文本分析依赖于知情的概率先验，以鼓励良好的解决方案。本文实证分析是否在医疗出院报告中的文本遵循齐普夫定律，一个普遍假设的统计性质的语言，词频遵循离散幂律分布。我们检查了来自MIMIC-III数据集的20，000份医疗出院报告。方法包括将放电报告拆分为令牌，计算令牌频率，将幂律分布拟合到数据，并测试替代分布-对数正态分布，指数分布，拉伸指数分布和截断幂律分布-是否提供了对数据的更优拟合。结果表明，流量报告的最佳拟合截尾幂律和对数正态分布。我们的研究结果表明，贝叶斯建模和统计文本分析的排放报告文本将受益于使用截断幂律和对数正态概率先验。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
