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Related Concept Videos

Hepatitis01:25

Hepatitis

Hepatitis is an inflammatory condition of the liver most commonly caused by hepatotropic viruses (A–E), though non-infectious causes such as alcohol and drugs also exist.Hepatitis AHepatitis A virus (HAV) is a non-enveloped RNA virus of the Picornaviridae family. It is primarily transmitted via the fecal-oral route, typically through ingestion of contaminated food or water. After ingestion, HAV enters the bloodstream through the oropharynx or intestinal epithelium and reaches the liver. The...
Viral Hepatitis I: Introduction01:28

Viral Hepatitis I: Introduction

Viral hepatitis is an inflammatory condition of the liver caused by infection with hepatotropic viruses, most commonly hepatitis A, B, C, D, and E. Despite variations in structure and transmission, all viruses mentioned infect hepatocytes and provoke immune responses that can hinder liver function. Additionally, some non-hepatotropic viruses can also lead to hepatic inflammation.Hepatitis A VirusHepatitis A virus (HAV) is transmitted through the fecal–oral route, typically by ingestion of food...

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Deep learning models for hepatitis E incidence prediction leveraging Baidu index.

Yanhui Guo1, Li Zhang2, Shengnan Pang3

  • 1School of Data and Computer Science, Shandong Women's University, 2399 Daxue Road, Changqing District, Ji'nan, 250300, Shandong, China.

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|October 31, 2024
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Summary

Internet search data, like the Baidu index, can help predict infectious diseases like hepatitis E. Incorporating this data improved prediction accuracy by 2%, demonstrating its value for public health surveillance.

Keywords:
Baidu indexHepatitis EKANLSTMPrediction

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Area of Science:

  • Epidemiology
  • Computational Biology
  • Public Health

Background:

  • Infectious diseases pose significant 21st-century challenges, necessitating accurate incidence prediction for effective public health interventions.
  • Internet search engine data presents a potential tool for analyzing epidemic trends and enhancing disease forecasting.
  • Hepatitis E incidence prediction is crucial for managing public health resources and preventing outbreaks.

Purpose of the Study:

  • To evaluate the utility of the Baidu search index in predicting Hepatitis E incidence in Shandong province.
  • To compare the performance of various Long Short-Term Memory (LSTM) network architectures for Hepatitis E forecasting.
  • To assess the impact of incorporating the Baidu index and Kernel Approximation Network (KAN) on prediction accuracy.

Main Methods:

  • Collected Hepatitis E incidence data (2009-2022) and Baidu search index data for Shandong province.
  • Utilized Pearson correlation analysis to establish the relationship between Baidu index and Hepatitis E incidence.
  • Developed and compared LSTM, stacked LSTM, attention-based LSTM, and attention-based stacked LSTM models, with and without Baidu index integration, and introduced KAN for enhanced nonlinear learning.

Main Results:

  • The Baidu index showed a weak correlation with Hepatitis E incidence, but its inclusion improved prediction accuracy by approximately 2%.
  • LSTM models incorporating the Baidu index achieved lower Mean Absolute Percentage Error (MAPE), e.g., 15.36% (LSTM) and 15.15% (attention-based stacked LSTM), compared to models without it (17.04% and 17.19%, respectively).
  • The integration of Kernel Approximation Network (KAN) further enhanced model performance by approximately 0.3%.

Conclusions:

  • The Baidu search index is a valuable supplementary data source for predicting Hepatitis E incidence, despite a weak initial correlation.
  • Advanced LSTM architectures, including stacked layers and attention mechanisms, improve predictive capabilities.
  • Kernel Approximation Network (KAN) integration enhances the nonlinear learning capacity of LSTM models for infectious disease forecasting.