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

Non-randomized Protocols and Methods (ehealth) (568)

Published on in Vol 8, No 6 (2019): June

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/13783, first published .
Using Temporal Features to Provide Data-Driven Clinical Early Warnings for Chronic Obstructive Pulmonary Disease and Asthma Care Management: Protocol for a Secondary Analysis

Using Temporal Features to Provide Data-Driven Clinical Early Warnings for Chronic Obstructive Pulmonary Disease and Asthma Care Management: Protocol for a Secondary Analysis

Using Temporal Features to Provide Data-Driven Clinical Early Warnings for Chronic Obstructive Pulmonary Disease and Asthma Care Management: Protocol for a Secondary Analysis

Authors of this article:

Gang Luo1 Author Orcid Image ;   Bryan L Stone2 Author Orcid Image ;   Corinna Koebnick3 Author Orcid Image ;   Shan He4 Author Orcid Image ;   David H Au5, 6 Author Orcid Image ;   Xiaoming Sheng7 Author Orcid Image ;   Maureen A Murtaugh8 Author Orcid Image ;   Katherine A Sward7 Author Orcid Image ;   Michael Schatz3, 9 Author Orcid Image ;   Robert S Zeiger3, 9 Author Orcid Image ;   Giana H Davidson10 Author Orcid Image ;   Flory L Nkoy2 Author Orcid Image
Article Authors Cited by (10) Tweetations (11) Metrics

Journals

  1. Luo G, He S, Stone B, Nkoy F, Johnson M. Developing a Model to Predict Hospital Encounters for Asthma in Asthmatic Patients: Secondary Analysis. JMIR Medical Informatics 2020;8(1):e16080 View
  2. 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
  3. 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
  4. 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
  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. Zeng S, Arjomandi M, Luo G. Automatically Explaining Machine Learning Predictions on Severe Chronic Obstructive Pulmonary Disease Exacerbations: Retrospective Cohort Study. JMIR Medical Informatics 2022;10(2):e33043 View
  8. 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
  9. 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
  10. Balakrishnan K, Olson S, Simon G, Pruinelli L. Machine learning for post-liver transplant survival: Bridging the gap for long-term outcomes through temporal variation features. Computer Methods and Programs in Biomedicine 2024;257:108442 View