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首页> 外文会议>Conference on PACS and imaging informatics >A web-based neurological pain classifier tool utilizing Bayesian decision theory for pain classification in spinal cord injury patients
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A web-based neurological pain classifier tool utilizing Bayesian decision theory for pain classification in spinal cord injury patients

机译：基于贝叶斯决策理论的基于网络的神经痛分类器，用于脊髓损伤患者的疼痛分类

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摘要

Pain is a common complication after spinal cord injury with prevalence estimates ranging 77% to 81%, which highly affects a patient's lifestyle and well-being. In the current clinical setting paper-based forms are used to classify pain correctly, however, the accuracy of diagnoses and optimal management of pain largely depend on the expert reviewer, which in many cases is not possible because of very few experts in this field. The need for a clinical decision support system that can be used by expert and non-expert clinicians has been cited in literature, but such a system has not been developed. We have designed and developed a stand-alone tool for correctly classifying pain type in spinal cord injury (SCI) patients, using Bayesian decision theory. Various machine learning simulation methods are used to verify the algorithm using a pilot study data set, which consists of 48 patients data set. The data set consists of the paper-based forms, collected at Long Beach VA clinic with pain classification done by expert in the field. Using the WEKA as the machine learning tool we have tested on the 48 patient dataset that the hypothesis that attributes collected on the forms and the pain location marked by patients have very significant impact on the pain type classification. This tool will be integrated with an imaging informatics system to support a clinical study that will test the effectiveness of using Proton Beam radiotherapy for treating spinal cord injury (SCI) related neuropathic pain as an alternative to invasive surgical lesioning.
机译：疼痛患者患有普遍估计的血液损伤率为77％至81％，这影响了患者的生活方式和福祉。在目前的临床环境中，基于纸张的形式用于正确对疼痛进行分类，然而，诊断的准确性和疼痛的最佳管理在很大程度上取决于专家评审员，这在许多情况下是不可能的，因为这一领域的专家很少，这是不可能的。文学中引用了专家和非专家临床医生可以使用的临床决策支持系统，但尚未开发这种系统。我们设计并开发了一种独立的工具，用于使用贝叶斯决策理论正确分类脊髓损伤（SCI）患者的疼痛类型。各种机器学习仿真方法用于使用试验研究数据集验证算法，该算法由48名患者数据集组成。数据集包括基于纸张的形式，在长滩VA诊所收集，由该领域专家完成的疼痛分类。使用Weka作为机器学习工具，我们在48名患者数据集上测试了本假设，该患者收集的属性和患者标记的疼痛位置对疼痛类型分类产生非常显着的影响。该工具将与成像信息系统集成，以支持临床研究，该研究将测试使用质子束放射治疗脊髓损伤（SCI）相关神经性疼痛的有效性，作为侵入性外科病变的替代方案。

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