Forecasting Future Asthma Hospital Encounters of Patients With Asthma in an Academic Health Care System: Predictive Model Development and Secondary Analysis Study

Journals

1. 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
2. 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
3. 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
4. Luo G. A Roadmap for Boosting Model Generalizability for Predicting Hospital Encounters for Asthma. JMIR Medical Informatics 2022;10(3):e33044 View
5. 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
6. 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
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. Mavrogiorgou A, Kiourtis A, Kleftakis S, Mavrogiorgos K, Zafeiropoulos N, Kyriazis D. A Catalogue of Machine Learning Algorithms for Healthcare Risk Predictions. Sensors 2022;22(22):8615 View
9. Wang H, Zhu H, Ding L. Accurate classification of lung nodules on CT images using the TransUnet. Frontiers in Public Health 2022;10 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. Yao H, Wang L, Zhou X, Jia X, Xiang Q, Zhang W. Predicting the therapeutic efficacy of AIT for asthma using clinical characteristics, serum allergen detection metrics, and machine learning techniques. Computers in Biology and Medicine 2023;166:107544 View
12. 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
13. Molfino N, Turcatel G, Riskin D. Machine Learning Approaches to Predict Asthma Exacerbations: A Narrative Review. Advances in Therapy 2023 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
16. Adamu Aliyu D, Akashah Patah Akhir E, Saidu Y, Adamu S, Ismail Umar K, Sadiq Bunu A, Mamman H. Optimization Techniques for Asthma Exacerbation Prediction Models: A Systematic Literature Review. IEEE Access 2024;12:110862 View