Published on in Vol 4, No 4 (2015): Oct-Dec

Using Computational Approaches to Improve Risk-Stratified Patient Management: Rationale and Methods

Using Computational Approaches to Improve Risk-Stratified Patient Management: Rationale and Methods

Using Computational Approaches to Improve Risk-Stratified Patient Management: Rationale and Methods

Journals

  1. Luo G, Stone B, Johnson M, Tarczy-Hornoch P, Wilcox A, Mooney S, Sheng X, Haug P, Nkoy F. Automating Construction of Machine Learning Models With Clinical Big Data: Proposal Rationale and Methods. JMIR Research Protocols 2017;6(8):e175 View
  2. 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
  3. Luo G, Nkoy F, Stone B, Schmick D, Johnson M. A systematic review of predictive models for asthma development in children. BMC Medical Informatics and Decision Making 2015;15(1) View
  4. Luo G, Sward K. A Roadmap for Optimizing Asthma Care Management via Computational Approaches. JMIR Medical Informatics 2017;5(3):e32 View
  5. Luo G. Automatically explaining machine learning prediction results: a demonstration on type 2 diabetes risk prediction. Health Information Science and Systems 2016;4(1) View
  6. Luo G. A review of automatic selection methods for machine learning algorithms and hyper-parameter values. Network Modeling Analysis in Health Informatics and Bioinformatics 2016;5(1) View
  7. Fei X, Tian G, Patnaik S. Attendance automatic recognition and learning behavior of web-based course attendance based on machine learning algorithms. Journal of Intelligent & Fuzzy Systems 2020;39(2):1769 View
  8. 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
  9. Nau C, Adams J, Roblin D, Schmittdiel J, Schroeder E, Steiner J. Considerations for Identifying Social Needs in Health Care Systems. Medical Care 2019;57(9):661 View
  10. 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
  11. Luo G. PredicT-ML: a tool for automating machine learning model building with big clinical data. Health Information Science and Systems 2016;4(1) View
  12. Bhardwaj N, Wodajo B, Spano A, Neal S, Coustasse A. The Impact of Big Data on Chronic Disease Management. The Health Care Manager 2018;37(1):90 View
  13. Luo G, Stone B, Johnson M, Nkoy F. Predicting Appropriate Admission of Bronchiolitis Patients in the Emergency Department: Rationale and Methods. JMIR Research Protocols 2016;5(1):e41 View
  14. 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
  15. Sawrikar V, Stewart E, LaMonica H, Iorfino F, Davenport T, Cross S, Scott E, Naismith S, Mowszowski L, Guastella A, Hickie I. Using Staged Care to Provide “Right Care First Time” to People With Common Affective Disorders. Psychiatric Services 2021;72(6):691 View
  16. 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
  17. Girwar S, Jabroer R, Fiocco M, Sutch S, Numans M, Bruijnzeels M. A systematic review of risk stratification tools internationally used in primary care settings. Health Science Reports 2021;4(3) View

Books/Policy Documents

  1. Khedo K, Baichoo S, Nagowah S, Nagowah L, Mungloo-Dilmohamud Z, Cadersaib Z, Cheerkoot-Jalim S. IoT and ICT for Healthcare Applications. View