ArticleBMC medical informatics and decision making2020
A comparative study on the prediction of the BP artificial neural network model and the ARIMA model in the incidence of AIDS.
Article in BMC medical informatics and decision making, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 2 of them syntheses that pooled it.
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
33 citing papers in PubMed, 2 syntheses or guidelines pooled it, 65 citations in OpenAlex.
- Machine Learning Used in Communicable Disease Control: A Scoping Review.Public health reviews · 2026Pooled it
- Artificial intelligence for HIV care: a global systematic review of current studies and emerging trends.Journal of the International AIDS Society · 2025Pooled it
- Prediction model for the risk of osteoporosis incorporating factors of disease history and living habits in physical examination of population in Chongqing, Southwest China: based on artificial neural network.BMC public health · 2021Trial
- Comparison of growth, time series, and hybrid models for HIV infectious disease incidence in adults in Saudi Arabia.BMC infectious diseases · 2026Article
- Forecasting monthly AIDS incidence in China via LSTM-CNN parallel fusion: a comparative study of 10 predictive models.Frontiers in public health · 2026Article
- Mathematical modeling and neural network based fitting of HIV/AIDS data in the workingclass population case study from Ethiopia.Scientific reports · 2025Article
- Statistical modelling and forecasting of HIV and anti-retroviral therapy cases by time-series and machine learning models.Scientific reports · 2025Article
- Study on the prediction performance of AIDS monthly incidence in Xinjiang based on time series and deep learning models.BMC public health · 2025Article
- Characterization of RAP Signal Patterns, Temporal Relationships, and Artifact Profiles Derived from Intracranial Pressure Sensors in Acute Traumatic Neural Injury.Sensors (Basel, Switzerland) · 2025Article
- Evaluating the effectiveness of self-attention mechanism in tuberculosis time series forecasting.BMC infectious diseases · 2024Article
- Personality and Health-Related Quality of Life of Older Chinese Adults: Cross-Sectional Study and Moderated Mediation Model Analysis.JMIR public health and surveillance · 2024Article
- Tuberculosis Prevalence Trends from a Predictive Modelling Study - 10 High-Burden Countries, 1980-2035.China CDC weekly · 2024Article
- Relationship between self-efficacy and adherence to antiretroviral therapy in HIV/AIDS patients: An analytical cross-sectional study in southern Iran.Health science reports · 2024Article
- Study on Univariate Modeling and Prediction Methods Using Monthly HIV Incidence and Mortality Cases in China.HIV/AIDS (Auckland, N.Z.) · 2024Article
- A deep learning model for predicting multidrug-resistant organism infection in critically ill patients.Journal of intensive care · 2023Article
- Research of Combined ES-BP Model in Predicting Syphilis Incidence 1982-2020 in Mainland China.Iranian journal of public health · 2023Article
- Recent Advances of Biosensors for Detection of Multiple Antibiotics.Biosensors · 2023Review
- Global incidence, prevalence and disease burden of silicosis: 30 years' overview and forecasted trends.BMC public health · 2023Review
- Application of ARIMA, and hybrid ARIMA Models in predicting and forecasting tuberculosis incidences among children in Homa Bay and Turkana Counties, Kenya.PLOS digital health · 2023Article
- Construction and evaluation of prognostic models of ECMO in elderly patients with cardiogenic shock based on BP neural network, random forest, and decision tree.American journal of translational research · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundAs a kind of widely distributed disease in China, acquired immune deficiency syndrome (AIDS) has been quickly growing each year, become a serious problem and caused serious damage to the life and health of people and the social events of China and the world because of its high fatality rate. It has been much concerned by all aspects of society. Therefore, developing early warning technology and finding the trend of early development are of quite significance to prevent and control human immunodeficiency virus (HIV)/AIDS. This study aimed to explore a suitable model for the morbidity of AIDS in China and establish a professional and feasible disease prediction model for the prevention and control works of AIDS.
methodsAt present, the traditional linear model is still utilized by most scholars to predict the incidence of HIV/AIDS. In addition, some scholars may attempt to use the nonlinear prediction model. Both prediction models showed good fitting and prediction effects. In China, the incidence of AIDS presents linear and nonlinear characteristics. In this research, the nonlinear back propagation artificial neural network (BP-ANN) model and the typical auto-regressive integrated moving average (ARIMA) linear model were applied to predict the incidence of HIV/AIDS and compare their fitting effects.
resultsBoth models were capable of predicting the expected cases of AIDS. It was seen that ARIMA and BP-ANN models could be used to forecast the monthly incidence of HIV/AIDS, but the fitting and forecasting effects of the nonlinear BP neural network model were better than those of the traditional linear ARIMA model.
conclusionsIn summary, it was further concluded that the BP-ANN model was a suitable way to monitor and predict the change trend and morbidity of AIDS in China.
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.