ArticleScientific reports2017
Developing a Machine Learning System for Identification of Severe Hand, Foot, and Mouth Disease from Electronic Medical Record Data.
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 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Improving Clinical Documentation with Artificial Intelligence: A Systematic Review.Perspectives in health information management · 2024Pooled it
- Harnessing artificial intelligence for pediatric health: Current trends and future opportunities.iScience · 2026Review
- Risk prediction of encephalitis in hand, foot and mouth disease: a nomogram model based on platelet indices for resource-limited settings.Italian journal of pediatrics · 2026Article
- A Machine Learning Model to Predict Length of Stay and Mortality among Diabetes and Hypertension Inpatients.Medicina (Kaunas, Lithuania) · 2022Article
- Risk prediction for delayed clearance of high-dose methotrexate in pediatric hematological malignancies by machine learning.International journal of hematology · 2021Article
- Regional-level risk factors for severe hand-foot-and-mouth disease: an ecological study from mainland China.Environmental health and preventive medicine · 2021Article
- Development and Validation of Diagnostic Models for Hand-Foot-and-Mouth Disease in Children.Disease markers · 2021Article
- Machine learning-enabled multiplexed microfluidic sensors.Biomicrofluidics · 2020Review
- Machine learning for the detection of early immunological markers as predictors of multi-organ dysfunction.Scientific data · 2019Article
- A method for hand-foot-mouth disease prediction using GeoDetector and LSTM model in Guangxi, China.Scientific reports · 2019Article
- Article
- Development of Prediction Models Using Machine Learning Algorithms for Girls with Suspected Central Precocious Puberty: Retrospective Study.JMIR medical informatics · 2019Article
- A Simple Scoring System for Quick, Accurate, and Reliable Early Diagnosis of Hand, Foot, and Mouth Disease.Medical science monitor : international medical journal of experimental and clinical research · 2018Article
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13 authors.
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Abstract
Children of severe hand, foot, and mouth disease (HFMD) often present with same clinical features as those of mild HFMD during the early stage, yet later deteriorate rapidly with a fulminant disease course. Our goal was to: (1) develop a machine learning system to automatically identify cases with high risk of severe HFMD at the time of admission; (2) compare the effectiveness of the new system with the existing risk scoring system. Data on 2,532 HFMD children admitted between March 2012 and July 2015, were collected retrospectively from a medical center in China. By applying a holdout strategy and a 10-fold cross validation method, we developed four models with the random forest algorithm using different variable sets. The prediction system HFMD-RF based on the model of 16 variables from both the structured and unstructured data, achieved 0.824 sensitivity, 0.931 specificity, 0.916 accuracy, and 0.916 area under the curve in the independent test set. Most remarkably, HFMD-RF offers significant gains with respect to the commonly used pediatric critical illness score in clinical practice. As all the selected risk factors can be easily obtained, HFMD-RF might prove to be useful for reductions in mortality and complications of severe HFMD.
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