This paper is in the following e-collection/theme issue:

Machine Learning (1505) Tools, Programs and Algorithms (275)

Published on in Vol 7, No 1 (2019): Jan-Mar

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/11728, first published .
Development of Prediction Models Using Machine Learning Algorithms for Girls with Suspected Central Precocious Puberty: Retrospective Study

Development of Prediction Models Using Machine Learning Algorithms for Girls with Suspected Central Precocious Puberty: Retrospective Study

Development of Prediction Models Using Machine Learning Algorithms for Girls with Suspected Central Precocious Puberty: Retrospective Study

Authors of this article:

Liyan Pan1 Author Orcid Image ;   Guangjian Liu1 Author Orcid Image ;   Xiaojian Mao2 Author Orcid Image ;   Huixian Li1 Author Orcid Image ;   Jiexin Zhang1 Author Orcid Image ;   Huiying Liang1 Author Orcid Image ;   Xiuzhen Li2 Author Orcid Image
Article Authors Cited by (29) Tweetations (3) Metrics

Journals

  1. Payrovnaziri S, Chen Z, Rengifo-Moreno P, Miller T, Bian J, Chen J, Liu X, He Z. Explainable artificial intelligence models using real-world electronic health record data: a systematic scoping review. Journal of the American Medical Informatics Association 2020;27(7):1173 View
  2. Dai C, Fan Y, Li Y, Bao X, Li Y, Su M, Yao Y, Deng K, Xing B, Feng F, Feng M, Wang R. Development and Interpretation of Multiple Machine Learning Models for Predicting Postoperative Delayed Remission of Acromegaly Patients During Long-Term Follow-Up. Frontiers in Endocrinology 2020;11 View
  3. Yu C, Lin Y, Lin C, Lin S, Wu J, Chang S. Development of an Online Health Care Assessment for Preventive Medicine: A Machine Learning Approach. Journal of Medical Internet Research 2020;22(6):e18585 View
  4. Sinha I, Aluthge D, Chen E, Sarkar I, Ahn S. Machine Learning Offers Exciting Potential for Predicting Postprocedural Outcomes: A Framework for Developing Random Forest Models in IR. Journal of Vascular and Interventional Radiology 2020;31(6):1018 View
  5. Tarekegn A, Ricceri F, Costa G, Ferracin E, Giacobini M. Predictive Modeling for Frailty Conditions in Elderly People: Machine Learning Approaches. JMIR Medical Informatics 2020;8(6):e16678 View
  6. Pan L, Liu G, Mao X, Liang H. Machine learning identifies girls with central precocious puberty based on multisource data. JAMIA Open 2021;3(4):567 View
  7. Markus A, Kors J, Rijnbeek P. The role of explainability in creating trustworthy artificial intelligence for health care: A comprehensive survey of the terminology, design choices, and evaluation strategies. Journal of Biomedical Informatics 2021;113:103655 View
  8. Fan Y, Li Y, Bao X, Zhu H, Lu L, Yao Y, Li Y, Su M, Feng F, Feng S, Feng M, Wang R. Development of Machine Learning Models for Predicting Postoperative Delayed Remission in Patients With Cushing’s Disease. The Journal of Clinical Endocrinology & Metabolism 2021;106(1):e217 View
  9. Yang G, Ye Q, Xia J. Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: A mini-review, two showcases and beyond. Information Fusion 2022;77:29 View
  10. Bas J, Zou Z, Cirillo C. An interpretable machine learning approach to understanding the impacts of attitudinal and ridesourcing factors on electric vehicle adoption. Transportation Letters 2023;15(1):30 View
  11. Xu Y, Liu X, Pan L, Mao X, Liang H, Wang G, Chen T. Explainable Dynamic Multimodal Variational Autoencoder for the Prediction of Patients With Suspected Central Precocious Puberty. IEEE Journal of Biomedical and Health Informatics 2022;26(3):1362 View
  12. Lin S, Zou Y, Hu J, Xiang L, Guo L, Lin X, Zou D, Gao X, Liang H, Zou J, Zhao Z, Dai X. Development and assessment of machine learning models for predicting recurrence risk after endovascular treatment in patients with intracranial aneurysms. Neurosurgical Review 2022;45(2):1521 View
  13. Ding W, Abdel-Basset M, Hawash H, Ali A. Explainability of artificial intelligence methods, applications and challenges: A comprehensive survey. Information Sciences 2022;615:238 View
  14. Huynh Q, Le N, Huang S, Ho B, Vu T, Pham H, Pham A, Hou J, Nguyen N, Chen Y, Deoraj A. Development and Validation of Clinical Diagnostic Model for Girls with Central Precocious Puberty: Machine-learning Approaches. PLOS ONE 2022;17(1):e0261965 View
  15. Saraswat D, Bhattacharya P, Verma A, Prasad V, Tanwar S, Sharma G, Bokoro P, Sharma R. Explainable AI for Healthcare 5.0: Opportunities and Challenges. IEEE Access 2022;10:84486 View
  16. Ling T, Jake L, Adams J, Osinski K, Liu X, Friedland D. Interpretable machine learning text classification for clinical computed tomography reports – a case study of temporal bone fracture. Computer Methods and Programs in Biomedicine Update 2023;3:100104 View
  17. Peper E, Bastiaansen J. Editorial for “Development and Validation of a Combined MRI Radiomics, Imaging and Clinical Parameter Based Machine Learning Model for Identifying Idiopathic Central Precocious Puberty in Girls”. Journal of Magnetic Resonance Imaging 2023;58(6):1988 View
  18. Chen Y, Liu C, Sung M, Lin S, Tsai W. Machine Learning Approach for Prediction of the Test Results of Gonadotropin-Releasing Hormone Stimulation: Model Building and Implementation. Diagnostics 2023;13(9):1550 View
  19. Winkelman J, Nguyen D, vanSonnenberg E, Kirk A, Lieberman S. Artificial Intelligence (AI) in pediatric endocrinology. Journal of Pediatric Endocrinology and Metabolism 2023;36(10):903 View
  20. Liu Z, Song Q. Diagnostic model based on multiple factors for girls with central precocious puberty. Journal of Pediatric Endocrinology and Metabolism 2024;37(2):150 View
  21. Dimitri P, Savage M. Artificial intelligence in paediatric endocrinology: conflict or cooperation. Journal of Pediatric Endocrinology and Metabolism 2024;37(3):209 View
  22. Chen Y, Huang X, Tian L. Meta-analysis of machine learning models for the diagnosis of central precocious puberty based on clinical, hormonal (laboratory) and imaging data. Frontiers in Endocrinology 2024;15 View
  23. Bharati S, Mondal M, Podder P. A Review on Explainable Artificial Intelligence for Healthcare: Why, How, and When?. IEEE Transactions on Artificial Intelligence 2024;5(4):1429 View
  24. 黄 雪. Current Status and Progress of Imaging Research on Central Precocious Puberty in Girls. Advances in Clinical Medicine 2024;14(05):559 View
  25. Nuamah J. Toward User-centered Explainable Displays for Complex Machine Learning Models in Healthcare: A Case Study of Heart Disease Prediction. Proceedings of the Human Factors and Ergonomics Society Annual Meeting 2024;68(1):1819 View
  26. Caterson J, Lewin A, Williamson E. The application of explainable artificial intelligence (XAI) in electronic health record research: A scoping review. DIGITAL HEALTH 2024;10 View
  27. Donmez T, Kutlu M, Mansour M, Yildiz M. Explainable AI in action: a comparative analysis of hypertension risk factors using SHAP and LIME. Neural Computing and Applications 2024 View

Books/Policy Documents

  1. Chang A. Intelligence-Based Medicine. View