Development of intelligent model to determine favorable wheelchair tilt and recline angles for people with spinal cord injury.
Development of intelligent model to determine favorable wheelchair tilt and recline angles for people with spinal cord injury.
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开发智能模型以确定脊髓损伤患者有利的轮椅倾斜和倾斜角度。
DOI：
10.1109/iembs.2011.6090377
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发表时间：
2011
期刊：
影响因子：
--
通讯作者：
Jones,Maria
中科院分区：
文献类型：
--
作者：
Fu,Jicheng;Jan,Yih-Kuen;Jones,Maria
机器学习技术在生物信息学中得到了广泛的应用。这些技术为理解复杂的生物医学机制和预测患者的最佳个体化干预提供了宝贵的见解。就我们而言，我们特别感兴趣的是为脊髓损伤 (SCI) 患者制定关于轮椅倾斜和倾斜使用的个性化临床指南。当前的临床实践建议对所有患者进行统一的设置。然而，我们之前的研究表明，皮肤血流对轮椅倾斜和倾斜设置的反应在患者之间存在很大差异。我们的研究结果表明，SCI 患者需要个性化的设置，以最大限度地利用残余神经功能来降低压疮风险。为了实现这一目标，我们打算开发一种智能模型来确定有利的轮椅使用方式，以降低患有 SCI 的轮椅使用者发生压疮的风险。在这项研究中，我们使用人工神经网络 (ANN) 构建了一个智能模型，可以根据神经功能和 SCI 损伤史来预测给定的倾斜和倾斜设置是否有利于 SCI 患者。我们的结果表明，智能模型在准确分类有利的轮椅倾斜和倾斜设置方面明显优于传统统计方法。据我们所知，这是第一项使用智能模型来预测有利的轮椅倾斜和倾斜角度的研究。我们的方法证明了使用 ANN 为 SCI 患者开发个性化轮椅倾斜和倾斜指导的可行性。
Machine-learning techniques have found widespread applications in bioinformatics. Such techniques provide invaluable insight on understanding the complex biomedical mechanisms and predicting the optimal individualized intervention for patients. In our case, we are particularly interested in developing an individualized clinical guideline on wheelchair tilt and recline usage for people with spinal cord injury (SCI). The current clinical practice suggests uniform settings to all patients. However, our previous study revealed that the response of skin blood flow to wheelchair tilt and recline settings varied largely among patients. Our finding suggests that an individualized setting is needed for people with SCI to maximally utilize the residual neurological function to reduce pressure ulcer risk. In order to achieve this goal, we intend to develop an intelligent model to determine the favorable wheelchair usage to reduce pressure ulcers risk for wheelchair users with SCI. In this study, we use artificial neural networks (ANNs) to construct an intelligent model that can predict whether a given tilt and recline setting will be favorable to people with SCI based on neurological functions and SCI injury history. Our results indicate that the intelligent model significantly outperforms the traditional statistical approach in accurately classifying favorable wheelchair tilt and recline settings. To the best of our knowledge, this is the first study using intelligent models to predict the favorable wheelchair tilt and recline angles. Our methods demonstrate the feasibility of using ANN to develop individualized wheelchair tilt and recline guidance for people with SCI.