An Efficacy Predictive Method for Diabetic Ulcers Based on Higher-Order Markov Chain-Set Pair Analysis.

BACKGROUND: Clinical comprehensive decision-making of diabetic ulcers includes curative effect evaluation and curative effect prediction. Nevertheless, there are few studies on the prediction of diabetic ulcers. METHODS: Set pair analysis (SPA) was used to assess the curative effect evaluation, and therapeutic effect was evaluated by connection degree (CD). The higher-order Markov chain-SPA curative effect prediction model was established to predict the future curative effect development. The predicted results with higher-order Markov chain-SPA and traditional first-order Markov-SPA model were compared with the actual results of the patients to verify the effectiveness of prediction. RESULTS: The connection degree of index levels I and II of 15 patients with diabetic ulcers after traditional Chinese medicine (TCM) treatment increased with time, while that of index levels IV and V decreased, indicating that the curative effect tends to improve. The higher-order Markov chain-SPA model was used to predict the curative effect. The results showed that the relative errors were fewer than the traditional first-order Markov-SPA model. CONCLUSIONS: The present study suggests that a method of SPA combined with higher-order Markov-SPA is relatively effective and can be applied to the clinical prediction of diabetic ulcers, which has higher accuracy than traditional first-order curative effect prediction model.

Application of a Cloud Model-Set Pair Analysis in Efficacy Assessment for Diabetic Ulcers.

Because treatment of diabetic ulcers includes various uncertainties, efficacy assessments are needed and significant. In previous studies, set pair analysis (SPA) has been applied to the efficacy assessments of traditional Chinese medicine (TCM) that pick out uncertainties related to the development and prognosis of disease. Optimized clinical protocols of SPA improve clinical efficacy. In the article, cloud model (CM) is employed to improve SPA, and a novel efficacy assessment method for a treatment of diabetic ulcers is proposed based on the cloud model-set pair analysis (CM-SPA). It is recommended to replace connection degree (CD) with cloud connection degree (CCD) that the efficacy assessment results are shown as normal clouds. Then, three diabetic ulcers patients treated with TCM made importance assessment by both CM-SPA and AHP based SPA. The comparison of assessment results shows that the CM-SPA is efficacious for the efficacy assessment of a treatment for diabetic ulcers and the results will be more scientific and accurate via CM-SPA.

[Study of syndrome differentiation factor of gouty arthritis with blood stasis syndrome based on connection number].

OBJECTIVE: To find the related syndrome factors of gouty arthritis with blood stasis syndrome by mathematical methods of set pair analysis and connection number so as to guide the diagnosis. METHODS: Seventy gouty arthritis patients diagnosed with blood stasis syndrome were analyzed according to priority by the method of connection number. Syndrome factors of gouty arthritis with blood stasis syndrome were selected according to the numerical values of dialectics connection value (DCV). RESULTS: In the nine observed items, the DCVs of five syndrome factors were above 0.5. These five factors were pain (0.858 0), sublingual varices and positive signs (varices and dark purple color) for palatal mucosa (0.773 1), tophus (0.723 0), varicose veins and telangiectasis (0.700 9), and squamous and dry skin (0.612 3). The DCVs of the other factors were below 0.5. CONCLUSION: The main factor 1 is pain; factor 2 is sublingual varices and positive signs (varices and dark purple color) for palatal mucosa; factor 3 is tophus; factor 4 is varicose veins and telangiectasis; factor 5 is dry skin. The others are secondary factors.

[A connection number-based principal factor analysis forecast method to forecast the encephalitis B epidemics].

OBJECTIVE: To detect the relations between incidence rate of the epidemical encephalitis B and related factors, to provide a simple, valid and practical new method for forecasting encephalitis B eipdemics. METHODS: Connection number between the incidence rate of encephalitis B and the historical forecast factors was computed, before ranking the first, second and the third principal factor, to remove the factor with the smallest value in the light of the connection number before comparing the newest value of forecast factors with the same kind of history while the most nearly value becoming the forecasting factor value and to establish a forecasting equation according to the factor value and the consistent degree of the incidence rate of encephalitis B at that time. Finally, to put into the new factor value to get this forecast value under this equation. Assuming that there are n' (n' >or= 2) forecast factors, this time forecast value can then be directly obtained from the average of these estimate values. RESULTS: Using above forecast method to forecast the incidence rate of encephalitis B at certain place and year, the predicting value is very much close to the actual incidence rate. Difference between the predicting value forecasted by the above-mentioned method and the actual incidence rate is only 0.0264/100 000 with an accurate rate of 97.94%. CONCLUSION: This principal factor analysis forecast method based on connection number in forecasting the incidence rate of encephalitis B prevention is acceptable.