ArticleInternational journal of environmental research and public health2020
The Prediction of Hepatitis E through Ensemble Learning.
Article in International journal of environmental research and public health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- Meteorological determinants of hepatitis E dynamics in Jiangsu Province, China: a pre-COVID-19 era study focusing on multi-route transmission (2005-2018).Frontiers in public health · 2025Article
- Deep learning models for hepatitis E incidence prediction leveraging Baidu index.BMC public health · 2024Article
- Predicting 3-month poor functional outcomes of acute ischemic stroke in young patients using machine learning.European journal of medical research · 2024Article
- Predictive etiological classification of acute ischemic stroke through interpretable machine learning algorithms: a multicenter, prospective cohort study.BMC medical research methodology · 2024Article
- Hepatitis E virus infections.World journal of virology · 2024Article
- Deep learning models for hepatitis E incidence prediction leveraging meteorological factors.PloS one · 2023Article
- Prediction of successful aging using ensemble machine learning algorithms.BMC medical informatics and decision making · 2022Article
- Machine learning is an effective method to predict the 90-day prognosis of patients with transient ischemic attack and minor stroke.BMC medical research methodology · 2022Article
- An objective model for diagnosing comorbid cognitive impairment in patients with epilepsy based on the clinical-EEG functional connectivity features.Frontiers in neuroscience · 2022Article
- Predicting incidence of hepatitis E for thirteen cities in Jiangsu Province, China.Frontiers in public health · 2022Article
- Optimized Neural Network Based on Genetic Algorithm to Construct Hand-Foot-and-Mouth Disease Prediction and Early-Warning Model.International journal of environmental research and public health · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
According to the World Health Organization, about 20 million people are infected with Hepatitis E every year. In 2015, there were 44,000 deaths due to HEV infection worldwide. Food, water and climate are key factors that affect the outbreak of Hepatitis E. This paper presents an ensemble learning model for Hepatitis E prediction by studying the correlation between historical epidemic cases of hepatitis E and environmental factors (water quality and meteorological data). Environmental factors include many features, and ones that are most relevant to HEV are selected and input into the ensemble learning model composed by Gradient Boosting Decision Tree (GBDT) and Random Forest for training and prediction. Three indicators, root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE), are used to evaluate the effectiveness of the ensemble learning model against the classical time series prediction model. It is concluded that the ensemble learning model has a better prediction effect than the classical model, and the prediction effectiveness can be improved by exploiting water quality and meteorological factors (radiation, air pressure, precipitation).
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.