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医疗实践表现出与口语相似的幂律行为。

Medical practices display power law behaviors similar to spoken languages.

机构信息

Veteran's Affairs Healthcare System, Pittsburgh, PA, USA.

出版信息

BMC Med Inform Decis Mak. 2013 Sep 4;13:102. doi: 10.1186/1472-6947-13-102.

Abstract

BACKGROUND

Medical care commonly involves the apprehension of complex patterns of patient derangements to which the practitioner responds with patterns of interventions, as opposed to single therapeutic maneuvers. This complexity renders the objective assessment of practice patterns using conventional statistical approaches difficult.

METHODS

Combinatorial approaches drawn from symbolic dynamics are used to encode the observed patterns of patient derangement and associated practitioner response patterns as sequences of symbols. Concatenating each patient derangement symbol with the contemporaneous practitioner response symbol creates "words" encoding the simultaneous patient derangement and provider response patterns and yields an observed vocabulary with quantifiable statistical characteristics.

RESULTS

A fundamental observation in many natural languages is the existence of a power law relationship between the rank order of word usage and the absolute frequency with which particular words are uttered. We show that population level patterns of patient derangement: practitioner intervention word usage in two entirely unrelated domains of medical care display power law relationships similar to those of natural languages, and that-in one of these domains-power law behavior at the population level reflects power law behavior at the level of individual practitioners.

CONCLUSIONS

Our results suggest that patterns of medical care can be approached using quantitative linguistic techniques, a finding that has implications for the assessment of expertise, machine learning identification of optimal practices, and construction of bedside decision support tools.

摘要

背景

医疗护理通常涉及对患者紊乱模式的复杂感知,医生会根据这些模式做出干预,而不是单一的治疗手段。这种复杂性使得使用传统统计方法客观评估实践模式变得困难。

方法

从符号动力学中提取组合方法,将观察到的患者紊乱模式和相关的医生反应模式编码为符号序列。将每个患者紊乱符号与同时发生的医生反应符号串联起来,创建“单词”,对同时发生的患者紊乱和提供者反应模式进行编码,并产生具有可量化统计特征的观察词汇。

结果

在许多自然语言中,一个基本的观察结果是,单词使用的等级顺序与特定单词的绝对频率之间存在幂律关系。我们表明,在两个完全不相关的医疗护理领域中,患者紊乱的人群模式:医生干预单词的使用都显示出与自然语言相似的幂律关系,并且在其中一个领域中,人群水平的幂律行为反映了个体医生水平的幂律行为。

结论

我们的研究结果表明,可以使用定量语言技术来研究医疗护理模式,这一发现对评估专业知识、机器学习确定最佳实践以及构建床边决策支持工具都具有重要意义。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c37b/3766655/614edb5aa91e/1472-6947-13-102-1.jpg

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