Construction and validation of prediction model for diabetic distal symmetric polyneuropathy based on neural network_Chinese Journal of Evidence-Based Medicine

Authors：

JIANG Aijuan ¹ , WANG Lujie ¹ , LI Jiajie ¹ , LIN Yixuan ² , ZHAO Jindong ² , FANG Zhaohui ² ,  SHEN Guoming ¹

1. School of Integrated Traditional Chinese and Western Medicine, Anhui University of Traditional Chinese Medicine, Hefei 230012, P. R. China;
2. The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei 230031, P. R. China;

Corresponding?author：

SHEN Guoming, Email: shengm_66@163.com

Keywords：

Diabetic neuropathy; Neural network algorithm; Prediction model; Characteristics of traditional Chinese medicine; Integration of traditional Chinese and Western medicine

DOI：

10.7507/1672-2531.202308003

Video：

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Abstract Full text Figures/Tables Video References Cited by

Objective To construct a prediction model of diabetics distal symmetric polyneuropathy (DSPN) based on neural network algorithm and the characteristic data of traditional Chinese medicine and Western medicine. Methods From the inpatients with diabetes in the First Affiliated Hospital of Anhui University of Chinese Medicine from 2017 to 2022, 4 071 cases with complete data were selected. The early warning model of DSPN was established by using neural network, and 49 indicators including general epidemiological data, laboratory examination, signs and symptoms of traditional Chinese medicine were included to analyze the potential risk factors of DSPN, and the weight values of variable features were sorted. Validation was performed using ten-fold crossover, and the model was measured by accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and AUC value. Results The mean duration of diabetes in the DSPN group was about 4 years longer than that in the non-DSPN group (P<0.001). Compared with non-DSPN patients, DSPN patients had a significantly higher proportion of Chinese medicine symptoms and signs such as numbness of limb, limb pain, dizziness and palpitations, fatigue, thirst with desire to drink, dry mouth and throat, blurred vision, frequent urination, slow reaction, dull complexion, purple tongue, thready pulse and hesitant pulse (P<0.001). In this study, the DSPN neural network prediction model was established by integrating traditional Chinese and Western medicine feature data. The AUC of the model was 0.945 3, the accuracy was 87.68%, the sensitivity was 73.9%, the specificity was 92.7%, the positive predictive value was 78.7%, and the negative predictive value was 90.72%. Conclusion The fusion of Chinese and Western medicine characteristic data has great clinical value for early diagnosis, and the established model has high accuracy and diagnostic efficacy, which can provide practical tools for DSPN screening and diagnosis in diabetic population.

Citation： JIANG Aijuan, WANG Lujie, LI Jiajie, LIN Yixuan, ZHAO Jindong, FANG Zhaohui, SHEN Guoming. Construction and validation of prediction model for diabetic distal symmetric polyneuropathy based on neural network. Chinese Journal of Evidence-Based Medicine, 2024, 24(3): 265-271. doi: 10.7507/1672-2531.202308003 Copy

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2.	Nicolucci A, Romeo L, Bernardini M, et al. Prediction of complications of type 2 diabetes: a machine learning approach. Diabetes Res Clin Pract, 2022, 190: 110013.
3.	Williams BM, Borroni D, Liu R, et al. An artificial intelligence-based deep learning algorithm for the diagnosis of diabetic neuropathy using corneal confocal microscopy: a development and validation study. Diabetologia, 2020, 63(2): 419-430.
4.	梁曉春. 中西醫結合防治糖尿病周圍神經病變的思路及其研究. 中國中西醫結合雜志, 2019, 39(3): 263-266.
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14.	Silver D, Schrittwieser J, Simonyan K, et al. Mastering the game of go without human knowledge. Nature, 2017, 550(7676): 354-359.
15.	Gholipour K, Asghari-Jafarabadi M, Iezadi S, et al. Modelling the prevalence of diabetes mellitus risk factors based on artificial neural network and multiple regression. East Mediterr Health J, 2018, 24(8): 770-777.
16.	Liu L, Wang M, Li G, et al. Construction of predictive model for type 2 diabetic retinopathy based on extreme learning machine. Diabetes Metab Syndr Obes, 2022, 15: 2607-2617.
17.	張玉璽, 賀松, 尤思夢. 集成學習在糖尿病預測中的應用. 智能計算機與應用, 2019, 9(5): 176-179.
18.	李軍, 胡曉娟, 屠立平, 等. 基于舌象參數與多指標特征聯合的2型糖尿病風險預測模型. 中國中醫基礎醫學雜志, 2021, 27(3): 451-456, 501.
19.	李軍, 胡曉娟, 周昌樂, 等. 基于隨機森林算法的糖尿病舌象特征分析和診斷模型研究. 中華中醫藥雜志, 2022, 37(3): 1639-1643.
20.	葉倩云, 侯政昆, 劉鳳斌. 中醫病證結合模式下的風險預后研究. 中醫雜志, 2018, 59(7): 550-553, 564.
21.	仝小林. 態靶醫學—中醫未來發展之路. 中國中西醫結合雜志, 2021, 41(1): 16-18.
22.	江愛娟, 申國明, 尤良震, 等. 病證結合慢性疾病嵌套風險預測模型的構建與應用. 中華中醫藥雜志, 2022, 37(8): 4546-4549.
23.	謝佛榮, 景海龍, 鄧菲菲. 人工智能的可解釋性問題探析—基于因果關系視角. 南華大學學報(社會科學版), 2022, 23(4): 40-45.
24.	Feldman EL, Callaghan BC, Pop-Busui R, et al. Diabetic neuropathy. Nat Rev Dis Primers, 2019, 5(1): 42.
25.	中國醫師協會中西醫結合醫師分會內分泌與代謝病學專業委員會. 糖尿病周圍神經病變病證結合診療指南. 中醫雜志, 2021, 62(18): 1648-1656.
26.	潘琦, 李洪梅, 邢小燕, 等. 北京地區13家醫院門診2型糖尿病患者周圍神經病變危險因素分析和篩查方法評估. 北京醫學, 2018, 40(11): 1009-1014.
27.	Ozaki K, Terayama Y, Matsuura T, et al. Effect of combined dyslipidemia and hyperglycemia on diabetic peripheral neuropathy in alloxan-induced diabetic WBN/Kob rats. J Toxicol Pathol, 2018, 31(2): 125-133.
28.	中國微循環學糖尿病與微循環專業委員會,中國醫療保健國際交流促進會基層衛生分會基層糖尿病學部,江蘇省基層內分泌特色科室孵化聯盟. 基層糖尿病微血管病變篩查與防治專家共識(2021年版). 中國醫學前沿雜志(電子版), 2021, 13(6): 16-38.
29.	梁曉春. 糖尿病周圍神經病變整合診治進展. 中國中西醫結合雜志, 2021, 41(6): 656-659.
30.	張雨楠, 朱濤, 曾維, 等. 基于機器學習的診斷性試驗準確性研究(一): 研究設計. 中國循證醫學雜志, 2023, 23(6): 725-730.

1. Elafros MA, Andersen H, Bennett DL, et al. Towards prevention of diabetic peripheral neuropathy: clinical presentation, pathogenesis, and new treatments. Lancet Neurol, 2022, 21(10): 922-936.
2. Nicolucci A, Romeo L, Bernardini M, et al. Prediction of complications of type 2 diabetes: a machine learning approach. Diabetes Res Clin Pract, 2022, 190: 110013.
3. Williams BM, Borroni D, Liu R, et al. An artificial intelligence-based deep learning algorithm for the diagnosis of diabetic neuropathy using corneal confocal microscopy: a development and validation study. Diabetologia, 2020, 63(2): 419-430.
4. 梁曉春. 中西醫結合防治糖尿病周圍神經病變的思路及其研究. 中國中西醫結合雜志, 2019, 39(3): 263-266.
5. Collins GS, Reitsma JB, Altman DG, et al. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. Eur Urol, 2015, 67(6): 1142-1151.
6. Debray TPA, Collins GS, Riley RD, et al. Transparent reporting of multivariable prediction models developed or validated using clustered data (TRIPOD-Cluster): explanation and elaboration. BMJ, 2023, 380: e071058.
7. 中華醫學會糖尿病學分會. 中國2型糖尿病防治指南(2020年版). 中華糖尿病雜志, 2021, 13(4): 315-409.
8. 黎敬波, 馬力. 中醫臨床常見癥狀術語規范(修訂). 北京: 中國醫藥科技出版社, 2015.
9. 李燦東, 方朝義. 中醫診斷學(5版). 北京: 中國中醫藥出版社, 2021.
10. 林逸軒, 汪敏生, 尤良震, 等. 基于不同維度統計學分析方法的糖尿病周圍神經病變中醫癥狀量表研制. 中國中醫基礎醫學雜志, 2020, 26(10): 1511-1515.
11. da Costa NL, de Lima MD, Barbosa R. Evaluation of feature selection methods based on artificial neural network weights. Exp Sys App, 2021, 168: 114312.
12. 田晨, 楊秋玉, 賴鴻皓, 等. 診斷試驗準確性比較研究的統計分析. 中國循證醫學雜志, 2022, 22(12): 1474-1482.
13. Almansour NA, Syed HF, Khayat NR, et al. Neural network and support vector machine for the prediction of chronic kidney disease: a comparative study. Comput Biol Med, 2019, 109: 101-111.
14. Silver D, Schrittwieser J, Simonyan K, et al. Mastering the game of go without human knowledge. Nature, 2017, 550(7676): 354-359.
15. Gholipour K, Asghari-Jafarabadi M, Iezadi S, et al. Modelling the prevalence of diabetes mellitus risk factors based on artificial neural network and multiple regression. East Mediterr Health J, 2018, 24(8): 770-777.
16. Liu L, Wang M, Li G, et al. Construction of predictive model for type 2 diabetic retinopathy based on extreme learning machine. Diabetes Metab Syndr Obes, 2022, 15: 2607-2617.
17. 張玉璽, 賀松, 尤思夢. 集成學習在糖尿病預測中的應用. 智能計算機與應用, 2019, 9(5): 176-179.
18. 李軍, 胡曉娟, 屠立平, 等. 基于舌象參數與多指標特征聯合的2型糖尿病風險預測模型. 中國中醫基礎醫學雜志, 2021, 27(3): 451-456, 501.
19. 李軍, 胡曉娟, 周昌樂, 等. 基于隨機森林算法的糖尿病舌象特征分析和診斷模型研究. 中華中醫藥雜志, 2022, 37(3): 1639-1643.
20. 葉倩云, 侯政昆, 劉鳳斌. 中醫病證結合模式下的風險預后研究. 中醫雜志, 2018, 59(7): 550-553, 564.
21. 仝小林. 態靶醫學—中醫未來發展之路. 中國中西醫結合雜志, 2021, 41(1): 16-18.
22. 江愛娟, 申國明, 尤良震, 等. 病證結合慢性疾病嵌套風險預測模型的構建與應用. 中華中醫藥雜志, 2022, 37(8): 4546-4549.
23. 謝佛榮, 景海龍, 鄧菲菲. 人工智能的可解釋性問題探析—基于因果關系視角. 南華大學學報(社會科學版), 2022, 23(4): 40-45.
24. Feldman EL, Callaghan BC, Pop-Busui R, et al. Diabetic neuropathy. Nat Rev Dis Primers, 2019, 5(1): 42.
25. 中國醫師協會中西醫結合醫師分會內分泌與代謝病學專業委員會. 糖尿病周圍神經病變病證結合診療指南. 中醫雜志, 2021, 62(18): 1648-1656.
26. 潘琦, 李洪梅, 邢小燕, 等. 北京地區13家醫院門診2型糖尿病患者周圍神經病變危險因素分析和篩查方法評估. 北京醫學, 2018, 40(11): 1009-1014.
27. Ozaki K, Terayama Y, Matsuura T, et al. Effect of combined dyslipidemia and hyperglycemia on diabetic peripheral neuropathy in alloxan-induced diabetic WBN/Kob rats. J Toxicol Pathol, 2018, 31(2): 125-133.
28. 中國微循環學糖尿病與微循環專業委員會,中國醫療保健國際交流促進會基層衛生分會基層糖尿病學部,江蘇省基層內分泌特色科室孵化聯盟. 基層糖尿病微血管病變篩查與防治專家共識(2021年版). 中國醫學前沿雜志(電子版), 2021, 13(6): 16-38.
29. 梁曉春. 糖尿病周圍神經病變整合診治進展. 中國中西醫結合雜志, 2021, 41(6): 656-659.
30. 張雨楠, 朱濤, 曾維, 等. 基于機器學習的診斷性試驗準確性研究(一): 研究設計. 中國循證醫學雜志, 2023, 23(6): 725-730.

Chinese Journal of Evidence-Based Medicine

Construction and validation of prediction model for diabetic distal symmetric polyneuropathy based on neural network

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