ArticleEpidemiology and infection2018
Prediction of Shigellosis outcomes in Israel using machine learning classifiers.
Article in Epidemiology and infection, 2018. 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
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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.
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Who cites it
11 citing papers in PubMed, 15 citations in OpenAlex.
- Trial
- Shigella sonnei: epidemiology, evolution, pathogenesis, resistance and host interactions.Nature reviews. Microbiology · 2025Review
- Twenty-five years of sentinel laboratory-based surveillance of shigellosis in a high-income country endemic for the disease, Israel, 1998 to 2022.Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin · 2024Article
- Predictive modeling for infectious diarrheal disease in pediatric populations: A systematic review.Learning health systems · 2024Article
- Advances in machine learning-based bacteria analysis for forensic identification: identity, ethnicity, and site of occurrence.Frontiers in microbiology · 2023Review
- Early Childhood Shigellosis and Attention Deficit Hyperactivity Disorder: A Population-Based Cohort Study with a Prolonged Follow-up.Journal of attention disorders · 2021Article
- Using Deep Learning in a Monocentric Study to Characterize Maternal Immune Environment for Predicting Pregnancy Outcomes in the Recurrent Reproductive Failure Patients.Frontiers in immunology · 2021Article
- Machine learning vs. classic statistics for the prediction of IVF outcomes.Journal of assisted reproduction and genetics · 2020Article
- Digital microbiology.Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases · 2020Review
- Applications of machine learning techniques to predict filariasis using socio-economic factors.Epidemiology and infection · 2019Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Shigellosis causes significant morbidity and mortality in developing and developed countries, mostly among infants and young children. The World Health Organization estimates that more than one million people die from Shigellosis every year. In order to evaluate trends in Shigellosis in Israel in the years 2002-2015, we analysed national notifiable disease reporting data. Shigella sonnei was the most commonly identified Shigella species in Israel. Hospitalisation rates due to Shigella flexenri were higher in comparison with other Shigella species. Shigella morbidity was higher among infants and young children (age 0-5 years old). Incidence of Shigella species differed among various ethnic groups, with significantly high rates of S. flexenri among Muslims, in comparison with Jews, Druze and Christians. In order to improve the current Shigellosis clinical diagnosis, we developed machine learning algorithms to predict the Shigella species and whether a patient will be hospitalised or not, based on available demographic and clinical data. The algorithms' performances yielded an accuracy of 93.2% (Shigella species) and 94.9% (hospitalisation) and may consequently improve the diagnosis and treatment of the disease.
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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.