Arthrographic and clinical findings in patients with hemiplegic shoulder pain

中國醫藥大學機構典藏 China Medical University Repository, Taiwan > 健康照護學院 > 物理治療學系(停用) > 期刊論文 > Item 310903500/29229

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题名:	Arthrographic and clinical findings in patients with hemiplegic shoulder pain
作者:	Lo, SF;Chen, SY;Lin, HC;Jim, YF;Meng, NH;Kao, MJ
贡献者:	健康照護學院物治系;China Med Univ, Sch Phys Therapy, Taichung 404, Taiwan;China Med Univ, Dept Phys Med & Rehabil, Taichung 404, Taiwan;China Med Univ Hosp, Dept Radiol, Taichung, Taiwan
关键词:	Hemiplegia;Pain;Rehabilitation;Shoulder
日期:	2003
上传时间:	2010-09-24 14:27:53 (UTC+8)
出版者:	W B SAUNDERS CO
摘要:	Lung cancer, a common malignancy in Taiwan, involves multiple factors, including genetics and environmental factors. The survival time is very short once cancer is diagnosed as being in advanced stage and surgically unresectable. Therefore, a good model of prediction of disease outcome is important for a treatment plan. We investigated the survival time in advanced lung cancer by using computer science from the genetic polymorphism of the p21 and p53 genes in conjunction with patients' general data. We studied 75 advanced and surgical unresectable lung cancer patients. The prediction of survival time was made by comparing real data obtained from follow-up periods with data generated by an artificial neural network (ANN). The most important input variable was the clinical staging of lung cancer patients. The second and third most important variables were pathological type and responsiveness to treatment, respectively. There were 25 neurons in the input layer, four neurons in the hidden layer-1, and one neuron in the output layer. The predicted accuracy was 86.2%. The average survival time was 12.44 +/- 7.95 months according to real data and 13.16 +/- 1.77 months based on the ANN results. ANN provides good prediction results when clinical parameters and genetic polymorphisms are considered in the model. It is possible to use computer science to integrate the genetic polymorphisms and clinical parameters in the prediction of disease outcome. Data mining provides a promising approach to the study of genetic markers for advanced lung cancer. (C) 2003 Wiley-Liss, Inc.
關聯:	ARCHIVES OF PHYSICAL MEDICINE AND REHABILITATION 84(12):1786-1791
显示于类别:	[物理治療學系(停用)] 期刊論文

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