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

Clinical Information and Decision Making (1407) Decision Support for Health Professionals (1185) Electronic Health Records (1053) Machine Learning (1465)

Published on in Vol 22, No 7 (2020): July

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/16981, first published .
Asthma Exacerbation Prediction and Risk Factor Analysis Based on a Time-Sensitive, Attentive Neural Network: Retrospective Cohort Study

Asthma Exacerbation Prediction and Risk Factor Analysis Based on a Time-Sensitive, Attentive Neural Network: Retrospective Cohort Study

Asthma Exacerbation Prediction and Risk Factor Analysis Based on a Time-Sensitive, Attentive Neural Network: Retrospective Cohort Study

Authors of this article:

Yang Xiang1 Author Orcid Image ;   Hangyu Ji2 Author Orcid Image ;   Yujia Zhou1 Author Orcid Image ;   Fang Li1 Author Orcid Image ;   Jingcheng Du1 Author Orcid Image ;   Laila Rasmy1 Author Orcid Image ;   Stephen Wu1 Author Orcid Image ;   W Jim Zheng1 Author Orcid Image ;   Hua Xu1 Author Orcid Image ;   Degui Zhi1 Author Orcid Image ;   Yaoyun Zhang1 Author Orcid Image ;   Cui Tao1 Author Orcid Image
Article Authors Cited by (30) Tweetations (7) Metrics

Journals

  1. 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
  2. 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
  3. Rasmy L, Xiang Y, Xie Z, Tao C, Zhi D. Med-BERT: pretrained contextualized embeddings on large-scale structured electronic health records for disease prediction. npj Digital Medicine 2021;4(1) View
  4. 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
  5. 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
  6. Subramaniam S, Raju N, Ganesan A, Rajavel N, Chenniappan M, Prakash C, Pramanik A, Basak A, Dixit S. Artificial Intelligence Technologies for Forecasting Air Pollution and Human Health: A Narrative Review. Sustainability 2022;14(16):9951 View
  7. Rasmy L, Nigo M, Kannadath B, Xie Z, Mao B, Patel K, Zhou Y, Zhang W, Ross A, Xu H, Zhi D. Recurrent neural network models (CovRNN) for predicting outcomes of patients with COVID-19 on admission to hospital: model development and validation using electronic health record data. The Lancet Digital Health 2022;4(6):e415 View
  8. Xie F, Yuan H, Ning Y, Ong M, Feng M, Hsu W, Chakraborty B, Liu N. Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologies. Journal of Biomedical Informatics 2022;126:103980 View
  9. Beuther D, Murphy K, Zeiger R, Wise R, McCann W, Reibman J, George M, Gilbert I, Eudicone J, Gandhi H, Ross M, Coyne K, Chipps B. The Asthma Impairment and Risk Questionnaire (AIRQ) Control Level Predicts Future Risk of Asthma Exacerbations. The Journal of Allergy and Clinical Immunology: In Practice 2022;10(12):3204 View
  10. Luo G. A Roadmap for Boosting Model Generalizability for Predicting Hospital Encounters for Asthma. JMIR Medical Informatics 2022;10(3):e33044 View
  11. Morid M, Sheng O, Dunbar J. Time Series Prediction Using Deep Learning Methods in Healthcare. ACM Transactions on Management Information Systems 2023;14(1):1 View
  12. Lee T, Sadatsafavi M, Yadav C, Price D, Beasley R, Janson C, Koh M, Roy R, Chen W. Individualised risk prediction model for exacerbations in patients with severe asthma: protocol for a multicentre real-world risk modelling study. BMJ Open 2023;13(3):e070459 View
  13. Seinen T, Kors J, van Mulligen E, Fridgeirsson E, Rijnbeek P. The added value of text from Dutch general practitioner notes in predictive modeling. Journal of the American Medical Informatics Association 2023;30(12):1973 View
  14. Yan Q, Lin X, Peng C, Zheng W, Liu X, Wen W, Jiang Y, Zhan S, Huang X. Network-based analysis between SARS-CoV-2 receptor ACE2 and common host factors in COVID-19 and asthma: Potential mechanistic insights. Biomedical Signal Processing and Control 2024;87:105502 View
  15. Xu S, Deo R, Soar J, Barua P, Faust O, Homaira N, Jaffe A, Kabir A, Acharya U. Automated detection of airflow obstructive diseases: A systematic review of the last decade (2013-2022). Computer Methods and Programs in Biomedicine 2023;241:107746 View
  16. Pungitore S, Subbian V. Assessment of Prediction Tasks and Time Window Selection in Temporal Modeling of Electronic Health Record Data: a Systematic Review. Journal of Healthcare Informatics Research 2023;7(3):313 View
  17. Kallis C, Calvo R, Schuller B, Quint J. Development of an Asthma Exacerbation Risk Prediction Model for Conversational Use by Adults in England. Pragmatic and Observational Research 2023;Volume 14:111 View
  18. 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
  19. Li D, Abhadiomhen S, Zhou D, Shen X, Shi L, Cui Y. Asthma prediction via affinity graph enhanced classifier: a machine learning approach based on routine blood biomarkers. Journal of Translational Medicine 2024;22(1) View
  20. 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
  21. 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
  22. 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
  23. Murphy K, Beuther D, Chipps B, Wise R, McCann W, Reibman J, George M, Gilbert I, Eudicone J, Gandhi H, Ross M, Coyne K, Zeiger R. Impact of Clinical Characteristics and Biomarkers on Asthma Impairment and Risk Questionnaire Exacerbation Prediction Ability. The Journal of Allergy and Clinical Immunology: In Practice 2024;12(8):2092 View
  24. M R P, Ravi V, Lokesh G, Al Mazroa A, Ravi P. A Prognostic Model to Improve Asthma Prediction Outcomes Using Machine Learning. The Open Bioinformatics Journal 2024;17(1) View

Books/Policy Documents

  1. Haque R, Ho S, Chai I, Teoh C, Abdullah A, Tan C, Dollmat K. Advances and Trends in Artificial Intelligence. Artificial Intelligence Practices. View
  2. Sudha , Sehrawat H, Singh Y, Jaglan V. Advances in Data-Driven Computing and Intelligent Systems. View