Published on in Vol 5, No 3 (2017): Jul-Sept

A Roadmap for Optimizing Asthma Care Management via Computational Approaches

A Roadmap for Optimizing Asthma Care Management via Computational Approaches

A Roadmap for Optimizing Asthma Care Management via Computational Approaches

Authors of this article:

Gang Luo 1 Author Orcid Image ;   Katherine Sward 2 Author Orcid Image

Journals

  1. Roe K, Jawa V, Zhang X, Chute C, Epstein J, Matelsky J, Shpitser I, Taylor C, Uzuner O. Feature engineering with clinical expert knowledge: A case study assessment of machine learning model complexity and performance. PLOS ONE 2020;15(4):e0231300 View
  2. Luo G. A roadmap for semi-automatically extracting predictive and clinically meaningful temporal features from medical data for predictive modeling. Global Transitions 2019;1:61 View
  3. Zeng X, Luo G. Progressive sampling-based Bayesian optimization for efficient and automatic machine learning model selection. Health Information Science and Systems 2017;5(1) View
  4. Rankin D, Black M, Bond R, Wallace J, Mulvenna M, Epelde G. Reliability of Supervised Machine Learning Using Synthetic Data in Health Care: Model to Preserve Privacy for Data Sharing. JMIR Medical Informatics 2020;8(7):e18910 View
  5. 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
  6. Luo G, Stone B, Koebnick C, He S, Au D, Sheng X, Murtaugh M, Sward K, Schatz M, Zeiger R, Davidson G, Nkoy F. Using Temporal Features to Provide Data-Driven Clinical Early Warnings for Chronic Obstructive Pulmonary Disease and Asthma Care Management: Protocol for a Secondary Analysis. JMIR Research Protocols 2019;8(6):e13783 View