Published on in Vol 6, No 8 (2017): August

Automating Construction of Machine Learning Models With Clinical Big Data: Proposal Rationale and Methods

Automating Construction of Machine Learning Models With Clinical Big Data: Proposal Rationale and Methods

Automating Construction of Machine Learning Models With Clinical Big Data: Proposal Rationale and Methods

Journals

  1. Wu J, Tsai C, Ho T, Lai F, Tai H, Lin M. A Unified Framework for Automatic Detection of Wound Infection with Artificial Intelligence. Applied Sciences 2020;10(15):5353 View
  2. Alaa A, van der Schaar M. Prognostication and Risk Factors for Cystic Fibrosis via Automated Machine Learning. Scientific Reports 2018;8(1) View
  3. 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
  4. Nelson C, Ekberg J, Fridell K. Prostate Cancer Detection in Screening Using Magnetic Resonance Imaging and Artificial Intelligence. The Open Artificial Intelligence Journal 2020;6(1):1 View
  5. D’Argenio V. The High-Throughput Analyses Era: Are We Ready for the Data Struggle?. High-Throughput 2018;7(1):8 View
  6. Luo G. Toward a Progress Indicator for Machine Learning Model Building and Data Mining Algorithm Execution. ACM SIGKDD Explorations Newsletter 2017;19(2):13 View
  7. 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
  8. Dong Q, Luo G. Progress Indication for Deep Learning Model Training: A Feasibility Demonstration. IEEE Access 2020;8:79811 View
  9. Wang H, Hsu W, Lee M, Weng H, Chang S, Yang J, Tsai Y. Automatic Machine-Learning-Based Outcome Prediction in Patients With Primary Intracerebral Hemorrhage. Frontiers in Neurology 2019;10 View
  10. Luo G. Progress Indication for Machine Learning Model Building. ACM SIGKDD Explorations Newsletter 2018;20(2):1 View
  11. 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
  12. Luo G, Tarczy-Hornoch P, Wilcox A, Lee E. Identifying Patients Who Are Likely to Receive Most of Their Care From a Specific Health Care System: Demonstration via Secondary Analysis. JMIR Medical Informatics 2018;6(4):e12241 View
  13. Yang F, Elmer J, Zadorozhny V. SmartPrognosis: Automatic ensemble classification for quantitative EEG analysis in patients resuscitated from cardiac arrest. Knowledge-Based Systems 2021;212:106579 View
  14. Mustafa A, Rahimi Azghadi M. Automated Machine Learning for Healthcare and Clinical Notes Analysis. Computers 2021;10(2):24 View
  15. Bang C, Lim H, Jeong H, Hwang S. Use of Endoscopic Images in the Prediction of Submucosal Invasion of Gastric Neoplasms: Automated Deep Learning Model Development and Usability Study. Journal of Medical Internet Research 2021;23(4):e25167 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

Books/Policy Documents

  1. Sacchini D, Spagnolo A. Clinical Ethics At the Crossroads of Genetic and Reproductive Technologies. View