ArticleScientific reports2017
Machine Learning Algorithms for Risk Prediction of Severe Hand-Foot-Mouth Disease in Children.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis 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
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 18 citations in OpenAlex.
- Pooled it
- Life's Essential 8 score and its association with sperm quality parameters in reproductive-aged men: evidence from the Led-Fertyl cohort.Human reproduction open · 2025Article
- Observational
- Early warning of hand, foot, and mouth disease transmission: A modeling study in mainland, China.PLoS neglected tropical diseases · 2021Article
- Has the Flood Entered the Basement? A Systematic Literature Review about Machine Learning in Laboratory Medicine.Diagnostics (Basel, Switzerland) · 2021Review
- A method for hand-foot-mouth disease prediction using GeoDetector and LSTM model in Guangxi, China.Scientific reports · 2019Article
- Developing a Nomogram for Risk Prediction of Severe Hand-Foot-and-Mouth Disease in Children.Indian journal of pediatrics · 2019Article
- Article
- A Study of Machine-Learning Classifiers for Hypertension Based on Radial Pulse Wave.BioMed research international · 2018Article
- Developing a Machine Learning System for Identification of Severe Hand, Foot, and Mouth Disease from Electronic Medical Record Data.Scientific reports · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
17 authors at 6 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The identification of indicators for severe HFMD is critical for early prevention and control of the disease. With this goal in mind, 185 severe and 345 mild HFMD cases were assessed. Patient demographics, clinical features, MRI findings, and laboratory test results were collected. Gradient boosting tree (GBT) was then used to determine the relative importance (RI) and interaction effects of the variables. Results indicated that elevated white blood cell (WBC) count > 15 × 10
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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.