Published on in Vol 8, No 11 (2020): November

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/22689, first published .
Developing a Predictive Model for Asthma-Related Hospital Encounters in Patients With Asthma in a Large, Integrated Health Care System: Secondary Analysis

Developing a Predictive Model for Asthma-Related Hospital Encounters in Patients With Asthma in a Large, Integrated Health Care System: Secondary Analysis

Developing a Predictive Model for Asthma-Related Hospital Encounters in Patients With Asthma in a Large, Integrated Health Care System: Secondary Analysis

Journals

  1. 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
  2. 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
  3. 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
  4. 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
  5. 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
  6. Awal M, Hossain M, Debjit K, Ahmed N, Nath R, Habib G, Khan M, Islam M, Mahmud M. An Early Detection of Asthma Using BOMLA Detector. IEEE Access 2021;9:58403 View
  7. 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
  8. 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
  9. Luo G. A Roadmap for Boosting Model Generalizability for Predicting Hospital Encounters for Asthma. JMIR Medical Informatics 2022;10(3):e33044 View
  10. 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
  11. 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
  12. Punyadasa D, Kumarapeli V, Senaratne W. Development of a risk prediction model to predict the risk of hospitalization due to exacerbated asthma among adult asthma patients in a lower middle-income country. BMC Pulmonary Medicine 2023;23(1) View
  13. Aguilar R, Knudsen-Robbins C, Ehwerhemuepha L, Feaster W, Kamath S, Heyming T. Pediatric Asthma Exacerbations: 14-Day Emergency Department Return Visit Risk Factors. The Journal of Emergency Medicine 2024 View
  14. 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
  15. 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