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      Validation of a Novel Traditional Chinese Medicine Pulse Diagnostic Model Using an Artificial Neural Network

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          Abstract

          In view of lacking a quantifiable traditional Chinese medicine (TCM) pulse diagnostic model, a novel TCM pulse diagnostic model was introduced to quantify the pulse diagnosis. Content validation was performed with a panel of TCM doctors. Criterion validation was tested with essential hypertension. The gold standard was brachial blood pressure measured by a sphygmomanometer. Two hundred and sixty subjects were recruited (139 in the normotensive group and 121 in the hypertensive group). A TCM doctor palpated pulses at left and right cun, guan, and chi points, and quantified pulse qualities according to eight elements (depth, rate, regularity, width, length, smoothness, stiffness, and strength) on a visual analog scale. An artificial neural network was used to develop a pulse diagnostic model differentiating essential hypertension from normotension. Accuracy, specificity, and sensitivity were compared among various diagnostic models. About 80% accuracy was attained among all models. Their specificity and sensitivity varied, ranging from 70% to nearly 90%. It suggested that the novel TCM pulse diagnostic model was valid in terms of its content and diagnostic ability.

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          Most cited references42

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          Neural networks for classification: a survey

          G.P. Zhang (2000)
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            Validity of a verbally administered numeric rating scale to measure cancer pain intensity.

            The ability to quantify pain intensity is essential when caring for individuals in pain in order to monitor patient progress and analgesic effectiveness. Three scales are commonly employed: the simple descriptor scale (SDS), the visual analog scale (VAS), and the numeric (pain intensity) rating scale (NRS). Patients with English as a second language may not be able to complete the SDS without translation, and visually, cognitively, or physically impaired patients may have difficulty using the VAS. The NRS has been found to be a simple and valid alternative in some disease states; however, the validity of this scale administered verbally, without visual cues, to oncology patients has not yet been established. The present study examined validity of a verbally administered 0-10 NRS using convergence methods. The correlation between the VAS and the NRS was strong and statistically significant (r = 0.847, p < 0.001), supporting the validity of the verbally administered NRS. Although all subjects were able to complete the NRS and SDS without apparent difficulty, 11 subjects (20%) were unable to complete the VAS. The mean opioid intake was significantly higher for the group that was unable to complete the VAS (mean 170.8 mg, median 120.0 mg, SD = 135.8) compared to the group that had no difficulty with the scale (mean 65.6 mg, 33.0 mg, SD = 99.7) (Mann-Whitney test, p = 0.0065). The verbally administered 0-10 NRS provides a useful alternative to the VAS, particularly as more contact with patients is established via telephone and patients within the hospital are more acutely ill.
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              Comparison of artificial neural networks with other statistical approaches: results from medical data sets.

              In recent years, considerable attention has been given to the development of sophisticated techniques for exploring data sets. One such class of techniques is artificial neural networks (ANNs). Artificial neural networks have many attractive theoretic properties, specifically, the ability to detect non predefined relations such as nonlinear effects and/or interactions. These theoretic advantages come at the cost of reduced interpretability of the model output. Many authors have analyzed the same data set, based on these factors, with both standard statistical methods (such as logistic or Cox regression) and ANN. The goal of this work is to review the literature comparing the performance of ANN with standard statistical techniques when applied to medium to large data sets (sample size > 200 patients). A thorough literature search was performed, with specific criteria for a published comparison to be included in this review. In the 28 studies included in this review, ANN outperformed regression in 10 cases (36%), was outperformed by regression in 4 cases (14%), and the 2 methods had similar performance in the remaining 14 cases (50%). However, in the 8 largest studies (sample size > 5000), regression and ANN tied in 7 cases, with regression winning in the remaining case. In addition, there is some suggestion of publication bias. Neither method achieves the desired performance. Both methods should continue to be used and explored in a complementary manner. However, based on the available data, ANN should not replace standard statistical approaches as the method of choice for the classification of medical data. Copyright 2001 American Cancer Society.
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                Author and article information

                Journal
                Evid Based Complement Alternat Med
                Evid Based Complement Alternat Med
                ECAM
                Evidence-based Complementary and Alternative Medicine : eCAM
                Hindawi Publishing Corporation
                1741-427X
                1741-4288
                2012
                13 September 2011
                13 September 2011
                : 2012
                : 685094
                Affiliations
                1School of Nursing, Caritas Medical Centre, Hong Kong
                2Department of Health and Physical Education, Hong Kong Institute of Education, Hong Kong
                3Tung Wah College, Hong Kong
                4School of Nursing, The Hong Kong Polytechnic University, Hong Kong
                Author notes
                *Anson Chui Yan Tang: tcy312@ 123456ha.org.hk

                Academic Editor: Vitaly Napadow

                Article
                10.1155/2012/685094
                3171770
                21918652
                e683b518-5c5f-4f57-a989-fbbff345c412
                Copyright © 2012 Anson Chui Yan Tang et al.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 3 April 2011
                : 27 June 2011
                : 12 July 2011
                Categories
                Research Article

                Complementary & Alternative medicine
                Complementary & Alternative medicine

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