Frontiers in public health · 2026
Application study of an artificial intelligence and big data-based personalized chronic disease management model for diabetes patients.
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Xin Mei, Yao Yanbing, Huang Ping, Li Qiuxia
Аннотация
BACKGROUND: To evaluate the real-world effectiveness of an artificial intelligence (AI) and big data-driven personalized chronic disease management model for type 2 diabetes mellitus (T2DM) patients, compared to conventional nurse-led management, and to identify factors associated with successful glycemic control within the personalized model.
METHODS: A retrospective cohort study was conducted involving 280 T2DM patients discharged from a single hospital between January 2019 and December 2024. Patients were divided into a conventional management group (n = 100) and a personalized management group (n = 180). The personalized group utilized a model integrating gradient boosting (XGBoost) for risk prediction and rule-based reasoning with reinforcement learning to dynamically generate individualized dietary, exercise, and blood glucose monitoring plans via a mobile application (APP). Both groups received 6 months of follow-up. Glycemic control [fasting blood glucose (FBG), 2-h postprandial glucose (2hPG), glycated hemoglobin (HbA1c)], self-care activities [Summary of Diabetes Self-Care Activities (SDSCA) scale], and quality of life [Diabetes-Specific Quality of Life (DSQL) scale] were assessed at baseline and 6 months. Within the personalized group, patients were further categorized into well-controlled (HbA1c ≤ 6.5%, n = 98) and poorly-controlled (HbA1c > 6.5%, n = 82) subgroups for case-control analysis.
RESULTS: At 6 months, the personalized management group demonstrated significantly better glycemic control (FBG: 6.79 ± 0.72 vs. 7.03 ± 0.89 mmol/L, p = 0.022; 2hPG: 6.27 ± 1.18 vs. 6.62 ± 1.16 mmol/L, p = 0.018; HbA1c: 6.48 ± 0.53% vs. 6.63 ± 0.46%, p = 0.018), superior self-care scores across all SDSCA domains (all p < 0.05, largest improvement in special diet: p = 0.001), and significantly higher quality of life (all DSQL dimensions p < 0.05) compared to the conventional group. Within the personalized group, multivariate analysis identified alcohol consumption [odds ratio (OR) = 3.576, p < 0.001], low baseline high-density lipoprotein cholesterol (HDL-C) (OR = 0.102, p = 0.007), and reduced blood glucose monitoring adherence (OR = 0.958, p < 0.001) as independent risk factors for poor control, while higher exercise plan completion was protective (OR = 0.976, p = 0.037).
CONCLUSION: The AI and big data-driven personalized management model significantly improved glycemic control, self-care behaviors, and quality of life in T2DM patients over conventional care within 6 months. Success within the model is influenced by behavioral and biological factors, alongside alcohol consumption. This approach demonstrates promise for enhancing diabetes care.
METHODS: A retrospective cohort study was conducted involving 280 T2DM patients discharged from a single hospital between January 2019 and December 2024. Patients were divided into a conventional management group (n = 100) and a personalized management group (n = 180). The personalized group utilized a model integrating gradient boosting (XGBoost) for risk prediction and rule-based reasoning with reinforcement learning to dynamically generate individualized dietary, exercise, and blood glucose monitoring plans via a mobile application (APP). Both groups received 6 months of follow-up. Glycemic control [fasting blood glucose (FBG), 2-h postprandial glucose (2hPG), glycated hemoglobin (HbA1c)], self-care activities [Summary of Diabetes Self-Care Activities (SDSCA) scale], and quality of life [Diabetes-Specific Quality of Life (DSQL) scale] were assessed at baseline and 6 months. Within the personalized group, patients were further categorized into well-controlled (HbA1c ≤ 6.5%, n = 98) and poorly-controlled (HbA1c > 6.5%, n = 82) subgroups for case-control analysis.
RESULTS: At 6 months, the personalized management group demonstrated significantly better glycemic control (FBG: 6.79 ± 0.72 vs. 7.03 ± 0.89 mmol/L, p = 0.022; 2hPG: 6.27 ± 1.18 vs. 6.62 ± 1.16 mmol/L, p = 0.018; HbA1c: 6.48 ± 0.53% vs. 6.63 ± 0.46%, p = 0.018), superior self-care scores across all SDSCA domains (all p < 0.05, largest improvement in special diet: p = 0.001), and significantly higher quality of life (all DSQL dimensions p < 0.05) compared to the conventional group. Within the personalized group, multivariate analysis identified alcohol consumption [odds ratio (OR) = 3.576, p < 0.001], low baseline high-density lipoprotein cholesterol (HDL-C) (OR = 0.102, p = 0.007), and reduced blood glucose monitoring adherence (OR = 0.958, p < 0.001) as independent risk factors for poor control, while higher exercise plan completion was protective (OR = 0.976, p = 0.037).
CONCLUSION: The AI and big data-driven personalized management model significantly improved glycemic control, self-care behaviors, and quality of life in T2DM patients over conventional care within 6 months. Success within the model is influenced by behavioral and biological factors, alongside alcohol consumption. This approach demonstrates promise for enhancing diabetes care.