⚡ Quick Summary
This study developed a Long Short-Term Memory (LSTM) model to predict the incidence rate of allergic rhinitis outpatient visits in Eastern China, utilizing air pollution and meteorological data. The model demonstrated superior performance compared to traditional methods, offering valuable insights for healthcare management.
🔍 Key Details
- 📊 Dataset: 25,425 outpatient data samples from various medical departments
- 🧩 Features used: Air pollution and meteorological data
- ⚙️ Technology: Long Short-Term Memory (LSTM) model
- 🏆 Performance: NMSE values for males: 0.4675, females: 0.3813, adults: 0.4183, minors: 0.4322
🔑 Key Takeaways
- 🌬️ Allergic rhinitis is a prevalent condition with significant health and economic impacts.
- 🤖 The LSTM model effectively predicts daily outpatient visits based on environmental factors.
- 📈 The model outperformed the ARIMA model in terms of stability and accuracy.
- 👩⚕️ Data stratification by gender and age provided nuanced insights into patient visits.
- 🏥 Valuable for hospital management and societal prevention strategies for allergic rhinitis.
- 📅 Study period: January 2022 to August 2024.
- 🌍 Conducted at: Affiliated Hospital of Hangzhou Normal University.
📚 Background
Allergic rhinitis is a common condition that can significantly affect patients’ quality of life and impose substantial social and economic burdens. Understanding the factors that contribute to its incidence is crucial for effective treatment and prevention strategies. With the rise of artificial intelligence, particularly in healthcare, there is an opportunity to leverage advanced predictive models to enhance patient care.
🗒️ Study
This study focused on developing a predictive model using Long Short-Term Memory (LSTM) networks to forecast the daily outpatient visits of allergic rhinitis patients. The researchers collected data from various departments, including otolaryngology and pediatrics, over a period of two and a half years, ensuring a comprehensive dataset for analysis.
📈 Results
The LSTM model demonstrated impressive performance, with normalized mean squared errors (NMSE) indicating strong predictive capabilities across different demographics. For instance, the NMSE for females was 0.3813, showcasing the model’s ability to accurately forecast outpatient visits compared to traditional methods like ARIMA, which struggled with stability and accuracy.
🌍 Impact and Implications
The findings from this study have significant implications for healthcare management. By accurately predicting outpatient visits for allergic rhinitis, hospitals can better allocate resources, improve patient care, and implement effective prevention strategies. This model not only aids in managing current patient loads but also contributes to broader public health initiatives aimed at reducing the incidence of allergic rhinitis.
🔮 Conclusion
This research highlights the transformative potential of artificial intelligence in healthcare, particularly in predicting patient visits for conditions like allergic rhinitis. The LSTM model serves as a promising tool for enhancing hospital management and improving patient outcomes. Continued exploration in this field could lead to even more innovative solutions for managing chronic conditions.
💬 Your comments
What are your thoughts on the use of AI in predicting healthcare needs? We would love to hear your insights! 💬 Share your comments below or connect with us on social media:
Prediction of outpatient visits for allergic rhinitis using an artificial intelligence LSTM model – a study in Eastern China.
Abstract
BACKGROUND: Allergic rhinitis is a common disease that can affect the health of patients and bring huge social and economic burdens. In this study, we developed a model to predict the incidence rate of allergic rhinitis so as to provide accurate information for the treatment, prevention, and control of allergic rhinitis.
METHODS: We developed a Long Short-Term Memory model for effectively predicting the daily outpatient visits of allergic rhinitis patients based on air pollution and meteorological data. We collected the outpatient data from the departments of otolaryngology, emergency medicine, pediatrics, and respiratory medicine at the Affiliated Hospital of Hangzhou Normal University, from January 2022 to August 2024. The data were stratified by gender and age and were separately input into the model for evaluation. A total of 25,425 outpatient data samples were assessed in this study.
RESULTS: Based on the data obtained from males (n = 13,943), females (n = 11,482), adults (n = 17,473), and minors (n = 7,952), the normalized mean squared errors of the Long Short-Term Memory model were 0.4674976, 0.3812502, 0.418301, and 0.4322124, respectively. By comparing the NMSE prediction results of ARIMA and LSTM models on this dataset, the LSTM model was found to outperform the ARIMA model in terms of stability and accuracy.
CONCLUSIONS: The model presented here could effectively predict the daily outpatient visits for allergic rhinitis patients based on air pollution and meteorological data, thereby offering valuable data-driven support for hospital management and for potentially improving societal management and prevention of allergic rhinitis.
Author: [‘Fan X’, ‘Chen L’, ‘Tang W’, ‘Sun L’, ‘Wang J’, ‘Liu S’, ‘Wang S’, ‘Li K’, ‘Wang M’, ‘Cheng Y’, ‘Dai L’]
Journal: BMC Public Health
Citation: Fan X, et al. Prediction of outpatient visits for allergic rhinitis using an artificial intelligence LSTM model – a study in Eastern China. Prediction of outpatient visits for allergic rhinitis using an artificial intelligence LSTM model – a study in Eastern China. 2025; 25:1328. doi: 10.1186/s12889-025-22430-y