ArticleJMIR medical informatics2019
Development of Prediction Models Using Machine Learning Algorithms for Girls with Suspected Central Precocious Puberty: Retrospective Study.
Article in JMIR medical informatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Meta-analysis of machine learning models for the diagnosis of central precocious puberty based on clinical, hormonal (laboratory) and imaging data.Frontiers in endocrinology · 2024Pooled it
- Development and Validation of Clinical Diagnostic Model for Girls with Central Precocious Puberty: Machine-learning Approaches.PloS one · 2022Trial
- Development and validation of diagnostic prediction models for central precocious puberty in girls based on machine learning: a multicenter retrospective study.Frontiers in endocrinology · 2026Article
- Article
- [Construction of a diagnostic model and scoring system for central precocious puberty in girls, with external validation].Zhongguo dang dai er ke za zhi = Chinese journal of contemporary pediatrics · 2024Article
- Machine Learning Approach for Prediction of the Test Results of Gonadotropin-Releasing Hormone Stimulation: Model Building and Implementation.Diagnostics (Basel, Switzerland) · 2023Article
- Interpretable machine learning text classification for clinical computed tomography reports - a case study of temporal bone fracture.Computer methods and programs in biomedicine update · 2023Article
- Development and assessment of machine learning models for predicting recurrence risk after endovascular treatment in patients with intracranial aneurysms.Neurosurgical review · 2022Article
- Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: A mini-review, two showcases and beyond.An international journal on information fusion · 2022Article
- Machine learning identifies girls with central precocious puberty based on multisource data.JAMIA open · 2020Article
- Explainable artificial intelligence models using real-world electronic health record data: a systematic scoping review.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- Development of an Online Health Care Assessment for Preventive Medicine: A Machine Learning Approach.Journal of medical Internet research · 2020Article
- Predictive Modeling for Frailty Conditions in Elderly People: Machine Learning Approaches.JMIR medical informatics · 2020Article
- Machine Learning Offers Exciting Potential for Predicting Postprocedural Outcomes: A Framework for Developing Random Forest Models in IR.Journal of vascular and interventional radiology : JVIR · 2020Article
- Development and Interpretation of Multiple Machine Learning Models for Predicting Postoperative Delayed Remission of Acromegaly Patients During Long-Term Follow-Up.Frontiers in endocrinology · 2020Article
- The application of explainable artificial intelligence (XAI) in electronic health record research: A scoping review.Digital healthArticle
- A Glimpse into the Realm that Combines Artificial Intelligence and Endocrinology.Indian journal of endocrinology and metabolismReview
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCentral precocious puberty (CPP) in girls seriously affects their physical and mental development in childhood. The method of diagnosis-gonadotropin-releasing hormone (GnRH)-stimulation test or GnRH analogue (GnRHa)-stimulation test-is expensive and makes patients uncomfortable due to the need for repeated blood sampling.
objectiveWe aimed to combine multiple CPP-related features and construct machine learning models to predict response to the GnRHa-stimulation test.
methodsIn this retrospective study, we analyzed clinical and laboratory data of 1757 girls who underwent a GnRHa test in order to develop XGBoost and random forest classifiers for prediction of response to the GnRHa test. The local interpretable model-agnostic explanations (LIME) algorithm was used with the black-box classifiers to increase their interpretability. We measured sensitivity, specificity, and area under receiver operating characteristic (AUC) of the models.
resultsBoth the XGBoost and random forest models achieved good performance in distinguishing between positive and negative responses, with the AUC ranging from 0.88 to 0.90, sensitivity ranging from 77.91% to 77.94%, and specificity ranging from 84.32% to 87.66%. Basal serum luteinizing hormone, follicle-stimulating hormone, and insulin-like growth factor-I levels were found to be the three most important factors. In the interpretable models of LIME, the abovementioned variables made high contributions to the prediction probability.
conclusionsThe prediction models we developed can help diagnose CPP and may be used as a prescreening tool before the GnRHa-stimulation test.
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