Xie Haiyan
Medical College of Changsha Social Work College, Changsha, China.
Front Public Health. 2025 Mar 14;13:1526360. doi: 10.3389/fpubh.2025.1526360. eCollection 2025.
To observe the role of a public health chronic disease prediction method based on capsule network and information system in clinical treatment and public health management.
Patients with hypertension, diabetes, and asthma admitted from May 2022 to October 2023 were incorporated into the research. They were grouped into hypertension group ( = 341), diabetes group ( = 341), and asthma group ( = 341). The established chronic disease prediction method was used to diagnose these types of public health chronic diseases. The key influencing factors obtained by the prediction method were compared with the regression analysis results. In addition, its diagnostic accuracy and specificity were analyzed, and the clinical diagnostic value of this method was explored. This method was applied to public health management and the management approach was improved based on the distribution and prevalence of chronic diseases. The effectiveness and residents' acceptance of public health management before and after improvement were compared, and the application value of this method in public health management was explored.
The key factors affecting the three diseases obtained by the application of prediction methods were found to be significantly correlated with disease occurrence after regression analysis ( < 0.05). Compared with before application, the diagnostic accuracy, specificity and sensitivity values of the method were 88.6, 89 and 92%, respectively, which were higher than the empirical diagnostic methods of doctors ( < 0.05). Compared with other existing AI-based chronic disease prediction methods, the AUC value of the proposed method was significantly higher than theirs ( < 0.05). This indicates that the diagnostic method proposed in this study has higher accuracy. After applying this method to public health management, the wellbeing of individuals with chronic conditions in the community was notably improved, and the incidence rate was notably reduced ( < 0.05). The acceptance level of residents toward the management work of public health management departments was also notably raised ( < 0.05).
The public health chronic disease prediction method based on information systems and capsule network has high clinical value in diagnosis and can help physicians accurately diagnose patients' conditions. In addition, this method has high application value in public health management. Management departments can adjust management strategies in a timely manner through predictive analysis results and propose targeted management measures based on the characteristics of residents in the management community.
观察基于胶囊网络和信息系统的公共卫生慢性病预测方法在临床治疗和公共卫生管理中的作用。
纳入2022年5月至2023年10月收治的高血压、糖尿病和哮喘患者进行研究。将其分为高血压组（n = 341）、糖尿病组（n = 341）和哮喘组（n = 341）。采用已建立的慢性病预测方法对这些公共卫生慢性病类型进行诊断。将预测方法获得的关键影响因素与回归分析结果进行比较。此外，分析其诊断准确性和特异性，探讨该方法的临床诊断价值。将该方法应用于公共卫生管理，并根据慢性病的分布和患病率改进管理方法。比较改进前后公共卫生管理的有效性和居民接受度，探讨该方法在公共卫生管理中的应用价值。
经回归分析发现，应用预测方法得出的影响三种疾病的关键因素与疾病发生显著相关（P < 0.05）。与应用前相比，该方法的诊断准确性、特异性和敏感性值分别为88.6%、89%和92%，高于医生的经验诊断方法（P < 0.05）。与其他现有的基于人工智能的慢性病预测方法相比，该方法的AUC值显著高于它们（P < 0.05）。这表明本研究提出的诊断方法具有更高的准确性。将该方法应用于公共卫生管理后，社区慢性病患者的健康状况得到显著改善，发病率显著降低（P < 0.05）。居民对公共卫生管理部门管理工作的接受程度也显著提高（P < 0.05）。
基于信息系统和胶囊网络的公共卫生慢性病预测方法在诊断方面具有较高的临床价值，可帮助医生准确诊断患者病情。此外，该方法在公共卫生管理中具有较高的应用价值。管理部门可通过预测分析结果及时调整管理策略，并根据管理社区居民的特点提出针对性的管理措施。
