Tam Sing-Fai, Cheing Gladys L Y, Hui-Chan Christina W Y
Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Kowloon, Hong Kong.
Int J Rehabil Res. 2004 Mar;27(1):65-9. doi: 10.1097/00004356-200403000-00009.
Artificial neural networks (ANN) have been applied to assist in clinical decision-making and prediction. While we consider possible effective treatments for patients with osteoarthritic knee such as Transcutaneous Electrical Nerve Stimulation (TENS), exercise, and TENS with exercise respectively, we have to select a treatment protocol for patients such that they would gain the best improvements according to their clinical conditions. To facilitate this functionality with the existing patient assessment, we hope to apply the ANN programming techniques to develop a computerized prediction system. A preliminary validation was performed to test the validity of the newly developed prediction protocol on knee rehabilitation. We input the key clinical attributes of 62 patients who have undergone the three above-mentioned knee treatments to the protocol. The expected pain improvement of each patient as predicted by the protocol was obtained. Spearman rank-order correlation was used to identify whether there was a significant correlation between the rankings of the observed and expected pain improvement. We found that the Spearman's rho was 0.424, which is statistically significant at p < 0.001. From this preliminary analysis, we are confident that this newly developed prediction protocol will be useful when deciding which treatment regime best suits a patient.
人工神经网络（ANN）已被应用于辅助临床决策和预测。当我们分别考虑骨关节炎膝关节患者可能的有效治疗方法，如经皮电神经刺激（TENS）、运动以及TENS与运动相结合时，我们必须为患者选择一种治疗方案，以便他们根据自身临床状况获得最佳改善。为了利用现有的患者评估来实现这一功能，我们希望应用人工神经网络编程技术来开发一个计算机化的预测系统。进行了初步验证，以测试新开发的膝关节康复预测方案的有效性。我们将62名接受了上述三种膝关节治疗的患者的关键临床属性输入到该方案中。获得了该方案预测的每位患者预期的疼痛改善情况。使用Spearman等级相关来确定观察到的和预期的疼痛改善排名之间是否存在显著相关性。我们发现Spearman相关系数为0.424，在p < 0.001时具有统计学显著性。从这一初步分析中，我们相信这种新开发的预测方案在决定哪种治疗方案最适合患者时将是有用的。
