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Nursing Science 2025
数据挖掘技术在代谢相关脂肪性肝病预测中应用的研究进展
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Abstract:
代谢相关脂肪性肝病(MAFLD)作为全球最常见的慢性肝病，其早期预测和精准干预对预防和延缓疾病进展至关重要。近年来，随着医疗大数据和人工智能技术的快速发展，数据挖掘技术在代谢相关脂肪性肝病风险预测方面展现出巨大潜力。数据挖掘技术为代谢相关脂肪性肝病的个体化预测和精准干预提供了新工具，但其临床应用仍需跨学科协作与标准化数据生态支持。本文从数据挖掘及代谢相关脂肪性肝病的概念入手，对数据挖掘技术在代谢相关脂肪性肝病预测特征选择和模型构建中的应用现状加以总结和讨论，梳理现有预测模型的局限，以期为更好发挥数据挖掘技术在代谢相关脂肪性肝病预测的实际效用提供参考。
Metabolic associated fatty liver disease (MAFLD), as the most prevalent chronic liver disease globally, requires early prediction and precise intervention to effectively prevent and delay disease progression. In recent years, driven by advancements in medical big data and artificial intelligence technologies, data mining has demonstrated significant potential for risk prediction of MAFLD. Data mining offers novel tools for individualized prediction and precision intervention in MAFLD; however, its clinical application necessitates interdisciplinary collaboration and the establishment of a standardized data ecosystem. This article begins with an overview of the concepts of data mining and MAFLD, systematically summarizing and analyzing the current status of data mining technology in feature selection and model construction for MAFLD prediction. Furthermore, it critically evaluates the limitations of existing prediction models, aiming to provide insights and guidance for maximizing the practical utility of data mining in MAFLD prediction.
| [1] | Riazi, K., Azhari, H., Charette, J.H., Underwood, F.E., King, J.A., Afshar, E.E., et al. (2022) The Prevalence and Incidence of NAFLD Worldwide: A Systematic Review and Meta-Analysis. The Lancet Gastroenterology & Hepatology, 7, 851-861. https://doi.org/10.1016/s2468-1253(22)00165-0 |
| [2] | 张君秋, 赵建光, 孟凡明, 等. 基于数据挖掘技术的相关模型与算法研究综述[J]. 中国新通信, 2023, 25(2): 45-48. |
| [3] | 任芳, 刘硕. 数据挖掘技术在医学信息中的广泛应用[J]. 中国多媒体与网络教学学报(上旬刊), 2019(6): 9-10. |
| [4] | 李志鹏, 杨阳朝, 廖勇, 等. 数据驱动的事件预测技术最新研究进展[J]. 信息安全学报, 2022, 7(1): 40-55. |
| [5] | 李金金. 天津市慢性病队列研究及风险预测模型的建立[D]: [博士学位论文]. 天津: 天津医科大学, 2018. |
| [6] | Eslam, M., Newsome, P.N., Sarin, S.K., Anstee, Q.M., Targher, G., Romero-Gomez, M., et al. (2020) A New Definition for Metabolic Dysfunction-Associated Fatty Liver Disease: An International Expert Consensus Statement. Journal of Hepatology, 73, 202-209. https://doi.org/10.1016/j.jhep.2020.03.039 |
| [7] | Lazarus, J.V., Newsome, P.N., Francque, S.M., Kanwal, F., Terrault, N.A. and Rinella, M.E. (2023) Reply: A Multi-Society Delphi Consensus Statement on New Fatty Liver Disease Nomenclature. Hepatology, 79, E93-E94. https://doi.org/10.1097/hep.0000000000000696 |
| [8] | 姚旭, 王晓丹, 张玉玺, 等. 特征选择方法综述[J]. 控制与决策, 2012, 27(2): 161-166. |
| [9] | 施启军, 潘峰, 龙福海, 等. 特征选择方法研究综述[J]. 微电子学与计算机, 2022, 39(3): 1-8. |
| [10] | 李绒, 李敏, 杨鹏程, 等. 基于女性体检数据的非酒精性脂肪肝风险因素筛选及预测模型[J]. 军事医学, 2022, 46(2): 140-144, 149. |
| [11] | 张培文. 基于stacking融合模型的脂肪肝致病影响因素的筛选分析[D]: [硕士学位论文]. 重庆: 重庆大学, 2022. |
| [12] | Zhao, M., Song, C., Luo, T., Huang, T. and Lin, S. (2021) Fatty Liver Disease Prediction Model Based on Big Data of Electronic Physical Examination Records. Frontiers in Public Health, 9, Article 668351. https://doi.org/10.3389/fpubh.2021.668351 |
| [13] | Huang, G., Jin, Q. and Mao, Y. (2023) Predicting the 5-Year Risk of Nonalcoholic Fatty Liver Disease Using Machine Learning Models: Prospective Cohort Study. Journal of Medical Internet Research, 25, e46891. https://doi.org/10.2196/46891 |
| [14] | Wang, H., Cheng, W., Hu, P., Ling, T., Hu, C., Chen, Y., et al. (2024) Integrative Analysis Identifies Oxidative Stress Biomarkers in Non-Alcoholic Fatty Liver Disease via Machine Learning and Weighted Gene Co-Expression Network Analysis. Frontiers in Immunology, 15, Article 1335112. https://doi.org/10.3389/fimmu.2024.1335112 |
| [15] | Peng, H., Duan, S., Pan, L., Wang, M., Chen, J., Wang, Y., et al. (2023) Development and Validation of Machine Learning Models for Nonalcoholic Fatty Liver Disease. Hepatobiliary & Pancreatic Diseases International, 22, 615-621. https://doi.org/10.1016/j.hbpd.2023.03.009 |
| [16] | 黄娅. 随机森林-Lasso Logistic回归模型筛选脂肪肝健康风险因素效果研究[D]: [硕士学位论文]. 成都: 西南交通大学, 2020. |
| [17] | 秦嘉怡, 周学谦, 孟祥勇, 等. 瘦型代谢功能障碍相关脂肪性肝病风险预测模型的构建与验证[J]. 陆军军医大学学报, 2025, 47(9): 969-979. |
| [18] | 李楠, 王雪莹, 郭佳桐, 等. 中青年人群非酒精性脂肪肝发生风险预测模型的建立[J]. 中国慢性病预防与控制, 2021, 29(3): 167-171. |
| [19] | 张家丽. 两种机器学习方法构建非酒精性脂肪性肝病患病风险预测模型的研究[D]: [硕士学位论文]. 桂林: 桂林医学院, 2022. |
| [20] | 高福来, 谢长顺, 张利利. 基于血清Betatrophin水平的非酒精性脂肪肝列线图预测模型的建立与分析[J]. 中国医药导报, 2019, 16(10): 103-106. |
| [21] | 王林, 夏春玲, 范玲. 基于职业护士健康队列研究建立护士非酒精性脂肪肝风险预测模型[J]. 护理研究, 2020, 34(14): 2445-2451. |
| [22] | 徐红平, 李旭, 王浩, 等. 绝经前HR+乳腺癌患者内分泌治疗相关脂肪肝预测模型的研究[J]. 现代预防医学, 2020, 47(7): 1332-1335. |
| [23] | 雷丽, 郭望, 李运明. 基于机器学习与生存模型建立脂肪肝Joint联合预测模型[J]. 现代预防医学, 2021, 48(17): 3259-3264. |
| [24] | Cao, T., Zhu, Q., Tong, C., Halengbieke, A., Ni, X., Tang, J., et al. (2024) Establishment of a Machine Learning Predictive Model for Non-Alcoholic Fatty Liver Disease: A Longitudinal Cohort Study. Nutrition, Metabolism and Cardiovascular Diseases, 34, 1456-1466. https://doi.org/10.1016/j.numecd.2024.02.004 |
| [25] | 陈霆, 蒋伏松, 朱兴敏, 等. 基于随机森林法的2型糖尿病合并非酒精性脂肪肝预测模型[J]. 中国数字医学, 2018, 13(11): 61-63. |
| [26] | Razmpour, F., Daryabeygi-Khotbehsara, R., Soleimani, D., Asgharnezhad, H., Shamsi, A., Bajestani, G.S., et al. (2023) Application of Machine Learning in Predicting Non-Alcoholic Fatty Liver Disease Using Anthropometric and Body Composition Indices. Scientific Reports, 13, Article No. 4942. https://doi.org/10.1038/s41598-023-32129-y |
| [27] | Qin, S., Hou, X., Wen, Y., Wang, C., Tan, X., Tian, H., et al. (2023) Machine Learning Classifiers for Screening Nonalcoholic Fatty Liver Disease in General Adults. Scientific Reports, 13, Article No. 3638. https://doi.org/10.1038/s41598-023-30750-5 |
| [28] | 吕航, 王昊, 刘媛, 等. 基于决策树的中医人格体质对2型糖尿病患者伴发非酒精性脂肪肝病风险的预测研究[J]. 中国中医基础医学杂志, 2017, 23(9): 1257-1259. |
| [29] | 王菊芳, 范瑞, 杜金满. 非肥胖人群脂肪肝患病风险的神经网络分析[J]. 现代实用医学, 2020, 32(6): 668-670. |
| [30] | 梁宏涛, 刘硕, 杜军威, 等. 深度学习应用于时序预测研究综述[J]. 计算机科学与探索, 2023, 17(6): 1285-1300. |
| [31] | 雪峰豪, 蒋海波, 唐聃. 深度学习在健康医疗中的应用研究综述[J]. 计算机科学, 2023, 50(4): 1-15. |
| [32] | Wu, C., Chu, T. and Jang, J.R. (2021) Current-Visit and Next-Visit Prediction for Fatty Liver Disease with a Large-Scale Dataset: Model Development and Performance Comparison. JMIR Medical Informatics, 9, e26398. https://doi.org/10.2196/26398 |
| [33] | Deng, Y., Ma, Y., Fu, J., Wang, X., Yu, C., Lv, J., et al. (2023) A Dynamic Machine Learning Model for Prediction of NAFLD in a Health Checkup Population: A Longitudinal Study. Heliyon, 9, e18758. https://doi.org/10.1016/j.heliyon.2023.e18758 |
| [34] | Yip, T.C.‐F., Ma, A.J., Wong, V.W.‐S., Tse, Y.‐K., Chan, H.L.‐Y., Yuen, P.‐C., et al. (2017) Laboratory Parameter‐based Machine Learning Model for Excluding Non‐alcoholic Fatty Liver Disease (NAFLD) in the General Population. Alimentary Pharmacology & Therapeutics, 46, 447-456. https://doi.org/10.1111/apt.14172 |
| [35] | Weng, S., Hu, D., Chen, J., Yang, Y. and Peng, D. (2023) Prediction of Fatty Liver Disease in a Chinese Population Using Machine-Learning Algorithms. Diagnostics, 13, Article 1168. https://doi.org/10.3390/diagnostics13061168 |
| [36] | Liu, J., Li, Y., Ma, J., Wan, X., Zhao, M., Zhang, Y., et al. (2023) Identification and Immunological Characterization of Lipid Metabolism-Related Molecular Clusters in Nonalcoholic Fatty Liver Disease. Lipids in Health and Disease, 22, Article No. 124. https://doi.org/10.1186/s12944-023-01878-0 |
| [37] | 余秋燕, 赵莹, 孙继佳, 等. 典型机器学习算法在脂肪肝分类预测研究中的实现与比较[J]. 数理医药学杂志, 2019, 32(1): 1-3. |
| [38] | Njei, B., Osta, E., Njei, N., Al-Ajlouni, Y.A. and Lim, J.K. (2024) An Explainable Machine Learning Model for Prediction of High-Risk Nonalcoholic Steatohepatitis. Scientific Reports, 14, Article No. 8589. https://doi.org/10.1038/s41598-024-59183-4 |
| [39] | Yang, B., Lu, H. and Ran, Y. (2024) Advancing Non-Alcoholic Fatty Liver Disease Prediction: A Comprehensive Machine Learning Approach Integrating SHAP Interpretability and Multi-Cohort Validation. Frontiers in Endocrinology, 15, Article 1450317. https://doi.org/10.3389/fendo.2024.1450317 |
| [40] | 曾芷青, 杨淞淳, 余灿清, 等. 慢性肾脏病发病风险预测模型研究进展[J]. 中华流行病学杂志, 2023, 44(3): 498-503. |
