Utilizing Theory of Machine Learning and Signal Processing to Incorporate Physiological Signals, Assessment of Sympathovagal Imbalance, Conventional and Novel Inflammation and Immune Biomarkers to Develop and Validate the Clinical Prediction Models

  • Chen, Kuan-Fu (PI)
  • Chan, Yi Ling (CoPI)
  • Chen, Chun-Hsien (CoPI)
  • Han, Shih Tsung (CoPI)
  • Lin, Chen (CoPI)
  • Lo, Men Tzung (CoPI)
  • Tseng, Yi-Ju (CoPI)
  • Yu, Jau-Song (CoPI)

Project: National Science and Technology CouncilNational Science and Technology Council Academic Grants

Project Details

Project IDs

Project ID:PC10801-0191
External Project ID:MOST107-2314-B182-052-MY2
StatusFinished
Effective start/end date01/08/1931/07/20

Keywords

  • sepsis
  • biomarker
  • clinical prediction rule
  • emergency medicine
  • latent class analysis
  • cohort study
  • procalcitonin
  • c-reactive protein
  • causal relationship
  • recursive partitioning algorithm
  • risk factor
  • heart rate variability
  • non-invasive monitoring
  • signal processing
  • machine learning
  • artificial intelligence
  • support vector machine
  • artificial neural network

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