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首页> 外文期刊>Nonlinear dynamics, psychology and life sciences >Evidence of Reduced Complexity in Self-report Data from Patients with Medically Unexplained Symptoms
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Evidence of Reduced Complexity in Self-report Data from Patients with Medically Unexplained Symptoms

机译：患有医学上无法解释的症状的患者的自我报告数据中复杂性降低的证据

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摘要

Physical symptoms which cannot be adequately explained by organic disease are a common problem in all fields of medicine. Reduced complexity, shown using nonlinear dynamic analysis, has been found to be associated with a wide range of illnesses. These methods have been applied to short time series of mood but not to self-rated physical symptoms. We tested the hypothesis that self-reported medically unexplained physical symptoms display reduced complexity by measuring the approximate entropy of self-reported emotions and physical symptoms collected twice daily over 12 weeks and comparing the results with series-specific surrogate data. We found that approximate entropy (ApEn) was lower for actual data series than for surrogate data. There was no significant difference in entropy between different types of symptoms and no significant correlation between entropy and the diurnal variation of the data series. Future studies should concentrate on specific symptoms and conditions, and evaluate the effect of treatment on the entropy of symptom patterns.
机译：无法用器质性疾病充分解释的身体症状是所有医学领域的普遍问题。使用非线性动态分析表明降低的复杂性与多种疾病有关。这些方法已应用于短期的情绪序列，但不适用于自我评价的身体症状。我们通过测量在12周内每天两次收集的自我报告的情绪和身体症状的近似熵，并将结果与​​系列特定的替代数据进行比较，从而检验了自我报告的医学上无法解释的身体症状显示出降低的复杂性的假设。我们发现，实际数据系列的近似熵（ApEn）低于替代数据。不同类型症状之间的熵没有显着差异，并且熵与数据序列的日变化之间没有显着相关性。未来的研究应集中于特定的症状和状况，并评估治疗对症状模式熵的影响。

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