ReviewScience in One Health2023
A review on the use of machine learning techniques in monkeypox disease prediction.
Review in Science in One Health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed.
- Explainable Artificial Intelligence (XAI) and Molecular Modeling Techniques to Discover Putative HER2 Inhibitors.International journal of molecular sciences · 2026Article
- Analyzing Global Threat of Lumpy Skin Disease Virus Infection: A Comparative Study of a Time Series Model and Machine Learning Models Across 10 High-Risk Countries.Current microbiology · 2026Article
- An AI-enhanced evidence-mapping framework for exhaled breathomics in cancer diagnostics: integrating multiple large language models (2005-early 2025).Discover oncology · 2026Article
- Surveillance and response to emerging and re-emerging zoonotic diseases: advancing One Health from evidence to action.Science in One Health · 2026Article
- Anti-interference labeled SERS lateral flow immunoassay based on Prussian blue for ultra-sensitive quantitative detection of bloodstains in forensic field.Analytical and bioanalytical chemistry · 2026Article
- One-Pot Isothermal Nucleic Acid Amplification Assisted CRISPR/Cas Detection Technology: Challenges, Strategies, and Perspectives.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Environmental, socioeconomic, and sociocultural drivers of monkeypox transmission in the Democratic Republic of the Congo: a One Health perspective.Infectious diseases of poverty · 2025Article
- An optimized ensemble grey wolf-based pipeline for monkeypox diagnosis.Scientific reports · 2025Article
- Schistosomiasis transmission in Zimbabwe: Modelling based on machine learning.Infectious Disease Modelling · 2024Article
- Power of One Health: the first year ofScience in One Health · 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
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Infectious diseases have posed a global threat recently, progressing from endemic to pandemic. Early detection and finding a better cure are methods for curbing the disease and its transmission. Machine learning (ML) has demonstrated to be an ideal approach for early disease diagnosis. This review highlights the use of ML algorithms for monkeypox (MP). Various models, such as CNN, DL, NLP, Naïve Bayes, GRA-TLA, HMD, ARIMA, SEL, Regression analysis, and Twitter posts were built to extract useful information from the dataset. These findings show that detection, classification, forecasting, and sentiment analysis are primarily analyzed. Furthermore, this review will assist researchers in understanding the latest implementations of ML in MP and further progress in the field to discover potent therapeutics.
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.