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Human Experts’ and a Fuzzy Model's Predictions of Outcomes of Scoliosis Treatment: A Comparative Analysis

机译:人类专家和脊柱侧凸治疗结果的模糊模型预测:比较分析

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摘要

Brace treatment is the most commonly used nonsurgical treatment for adolescents with idiopathic scoliosis. However, brace treatment is not always successful and the factors influencing its success are not completely clear. This makes treatment outcome difficult to predict. A computer model which can accurately predict treatment outcomes could potentially provide valuable treatment recommendations. This paper describes a fuzzy system that includes a prediction model and a decision support engine. The model was constructed using conditional fuzzy c-means clustering to discover patterns in retrospective patient data. The model's ability to predict treatment outcome was compared to the ability of eight Scoliosis experts. The model and experts each predicted treatment outcome retrospectively for 28 braced patients, and these predictions were compared to the actual outcomes. The model outperformed all but one expert individually and performed similarly to the experts as a group. These results suggest that the fuzzy model is capable of providing meaningful treatment recommendations. This study offers the first model for this application whose performance has been shown to be at or above the human expert level.
机译:对于患有特发性脊柱侧弯的青少年,支撑治疗是最常用的非手术治疗。但是,支架治疗并不总是成功的,影响其成功的因素也不是很清楚。这使得治疗结果难以预测。可以准确预测治疗结果的计算机模型可能会提供有价值的治疗建议。本文介绍了一种包含预测模型和决策支持引擎的模糊系统。使用条件模糊c均值聚类构建模型,以发现回顾性患者数据中的模式。该模型预测治疗结果的能力与八名脊柱侧弯专家的能力进行了比较。该模型和专家各自回顾性地预测了28名矫正患者的治疗结果,并将这些预测与实际结果进行了比较。该模型的表现优于除一位专家以外的所有专家,并且整体表现与专家相似。这些结果表明,模糊模型能够提供有意义的治疗建议。这项研究为该应用程序提供了第一个模型,该模型的性能已证明达到或高于人类专家水平。

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