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

Machine Learning (1465) Clinical Information and Decision Making (1407) Decision Support for Health Professionals (1185)

Published on in Vol 8, No 1 (2020): January

Developing a Model to Predict Hospital Encounters for Asthma in Asthmatic Patients: Secondary Analysis

Developing a Model to Predict Hospital Encounters for Asthma in Asthmatic Patients: Secondary Analysis

Developing a Model to Predict Hospital Encounters for Asthma in Asthmatic Patients: Secondary Analysis

Authors of this article:

Gang Luo1 Author Orcid Image ;   Shan He2 Author Orcid Image ;   Bryan L Stone3 Author Orcid Image ;   Flory L Nkoy3 Author Orcid Image ;   Michael D Johnson3 Author Orcid Image
Article Authors Cited by (28) Tweetations (5) Metrics

Journals

  1. Xiang Y, Ji H, Zhou Y, Li F, Du J, Rasmy L, Wu S, Zheng W, Xu H, Zhi D, Zhang Y, Tao C. Asthma Exacerbation Prediction and Risk Factor Analysis Based on a Time-Sensitive, Attentive Neural Network: Retrospective Cohort Study. Journal of Medical Internet Research 2020;22(7):e16981 View
  2. Messinger A, Luo G, Deterding R. The doctor will see you now: How machine learning and artificial intelligence can extend our understanding and treatment of asthma. Journal of Allergy and Clinical Immunology 2020;145(2):476 View
  3. Luo G, Johnson M, Nkoy F, He S, Stone B. Automatically Explaining Machine Learning Prediction Results on Asthma Hospital Visits in Patients With Asthma: Secondary Analysis. JMIR Medical Informatics 2020;8(12):e21965 View
  4. Luo G, Nau C, Crawford W, Schatz M, Zeiger R, Rozema E, Koebnick C. Developing a Predictive Model for Asthma-Related Hospital Encounters in Patients With Asthma in a Large, Integrated Health Care System: Secondary Analysis. JMIR Medical Informatics 2020;8(11):e22689 View
  5. Tong Y, Messinger A, Wilcox A, Mooney S, Davidson G, Suri P, Luo G. Forecasting Future Asthma Hospital Encounters of Patients With Asthma in an Academic Health Care System: Predictive Model Development and Secondary Analysis Study. Journal of Medical Internet Research 2021;23(4):e22796 View
  6. Luo G, Nau C, Crawford W, Schatz M, Zeiger R, Koebnick C. Generalizability of an Automatic Explanation Method for Machine Learning Prediction Results on Asthma-Related Hospital Visits in Patients With Asthma: Quantitative Analysis. Journal of Medical Internet Research 2021;23(4):e24153 View
  7. Tong Y, Messinger A, Luo G. Testing the Generalizability of an Automated Method for Explaining Machine Learning Predictions on Asthma Patients’ Asthma Hospital Visits to an Academic Healthcare System. IEEE Access 2020;8:195971 View
  8. Navanandan N, Hatoun J, Celedón J, Liu A. Predicting Severe Asthma Exacerbations in Children: Blueprint for Today and Tomorrow. The Journal of Allergy and Clinical Immunology: In Practice 2021;9(7):2619 View
  9. Luo G, Stone B, Sheng X, He S, Koebnick C, Nkoy F. Using Computational Methods to Improve Integrated Disease Management for Asthma and Chronic Obstructive Pulmonary Disease: Protocol for a Secondary Analysis. JMIR Research Protocols 2021;10(5):e27065 View
  10. Luo G. A Roadmap for Automating Lineage Tracing to Aid Automatically Explaining Machine Learning Predictions for Clinical Decision Support. JMIR Medical Informatics 2021;9(5):e27778 View
  11. Zhang X, Luo G. Ranking Rule-Based Automatic Explanations for Machine Learning Predictions on Asthma Hospital Encounters in Patients With Asthma: Retrospective Cohort Study. JMIR Medical Informatics 2021;9(8):e28287 View
  12. Cilluffo G, Fasola S, Ferrante G, Licari A, Marseglia G, Albarelli A, Marseglia G, La Grutta S, Chiappini E. Machine learning: A modern approach to pediatric asthma. Pediatric Allergy and Immunology 2022;33(S27):34 View
  13. McGowan E, Robinson L, Chen W, Rider N. Seeing the Forest for the Trees: Evaluating Population Data in Allergy-Immunology. The Journal of Allergy and Clinical Immunology: In Practice 2021;9(12):4193 View
  14. Hurst J, Zhao C, Hostetler H, Ghiasi Gorveh M, Lang J, Goldstein B. Environmental and clinical data utility in pediatric asthma exacerbation risk prediction models. BMC Medical Informatics and Decision Making 2022;22(1) View
  15. Tong Y, Lin B, Chen G, Zhang Z. Predicting Continuity of Asthma Care Using a Machine Learning Model: Retrospective Cohort Study. International Journal of Environmental Research and Public Health 2022;19(3):1237 View
  16. Zeng S, Arjomandi M, Tong Y, Liao Z, Luo G. Developing a Machine Learning Model to Predict Severe Chronic Obstructive Pulmonary Disease Exacerbations: Retrospective Cohort Study. Journal of Medical Internet Research 2022;24(1):e28953 View
  17. Luo G. A Roadmap for Boosting Model Generalizability for Predicting Hospital Encounters for Asthma. JMIR Medical Informatics 2022;10(3):e33044 View
  18. Zhang X, Luo G. Error and Timeliness Analysis for Using Machine Learning to Predict Asthma Hospital Visits: Retrospective Cohort Study. JMIR Medical Informatics 2022;10(6):e38220 View
  19. Rodríguez‐Martínez C, Sossa‐Briceño M, Forno E. Composite predictive models for asthma exacerbations or asthma deterioration in pediatric asthmatic patients: A systematic review of the literature. Pediatric Pulmonology 2023;58(10):2703 View
  20. Hirons N, Allen A, Matsuyoshi N, Su J, Kaye L, Barrett M. Prediction of short-acting beta-agonist usage in patients with asthma using temporal-convolutional neural networks. JAMIA Open 2023;6(4) View
  21. Xiong S, Chen W, Jia X, Jia Y, Liu C. Machine learning for prediction of asthma exacerbations among asthmatic patients: a systematic review and meta-analysis. BMC Pulmonary Medicine 2023;23(1) View
  22. Budiarto A, Tsang K, Wilson A, Sheikh A, Shah S. Machine Learning–Based Asthma Attack Prediction Models From Routinely Collected Electronic Health Records: Systematic Scoping Review. JMIR AI 2023;2:e46717 View
  23. Antão J, de Mast J, Marques A, Franssen F, Spruit M, Deng Q. Demystification of artificial intelligence for respiratory clinicians managing patients with obstructive lung diseases. Expert Review of Respiratory Medicine 2023;17(12):1207 View
  24. Bhargava H, Salomon C, Suresh S, Chang A, Kilian R, Stijn D, Oriol A, Low D, Knebel A, Taraman S. Promises, Pitfalls, and Clinical Applications of Artificial Intelligence in Pediatrics. Journal of Medical Internet Research 2024;26:e49022 View
  25. Ma L, Tibble H. Primary Care Asthma Attack Prediction Models for Adults: A Systematic Review of Reported Methodologies and Outcomes. Journal of Asthma and Allergy 2024;Volume 17:181 View
  26. Nkoy F, Stone B, Zhang Y, Luo G. A Roadmap for Using Causal Inference and Machine Learning to Personalize Asthma Medication Selection. JMIR Medical Informatics 2024;12:e56572 View
  27. Aboeleneen A, Abdellatif A, Erbad A, Salem A. ECP: Error-Aware, Cost-Effective and Proactive Network Slicing Framework. IEEE Open Journal of the Communications Society 2024;5:2567 View

Books/Policy Documents

  1. Kasten J. Research Anthology on Big Data Analytics, Architectures, and Applications. View