Modeling and Monitoring Biomedical Time Series
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Summary
A framework for modeling and monitoring medical time series is developed, based on extensions to the linear dynamic model, to provide for on-line, recursive updating and inference for time series that are subject to several forms of potential discontinuous change, and that may have missing values.
- Type
- article
- Published
- 1990-06-01
- Cited by
- 85
- References
- 11
- OpenAlex
- https://openalex.org/W2012941237
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:121770390
Keywords
Series (stratigraphy), Computer science, Inference, Time series, Data mining
References
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- Dynamic Generalized Linear Models and Bayesian Forecasting
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- Maximum Likelihood Fitting of ARMA Models to Time Series With Missing Observations
- Recursive bayesian estimation using gaussian sums
- Detection of renal allograft rejection by computer.
- Monitoring renal transplants: an application of the multiprocess Kalman filter.
- Monitoring Kidney Transplant Patients
- Bayesian Forecasting
- THE QUARTERLY JOURNAL OF MEDICINE.
- The analysis and optimization of stochastic systems
- Fitting Multivariate Models to Unequally Spaced Data
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- Statistical surveillance - Exponentially weighted moving average methods and public health monitoring
- Statistical issues in public health monitoring - A review and discussion
- Some Statistical Methods in Intensive Care Online Monitoring - A Review
- A case against logic.
- Online monitoring of high dimensional physiological time series: A case study
- A method for detecting changes in long time series
- A Method to Derive the Time of Onset of Infection from Serological Findings
- Kalman Filter Bibliography: Agriculture, Biology, and Medicine
- Large datasets: Segmentation, feature extraction, and compression
- Online pattern recognition in intensive care medicine
- Bayesian Inference for Dynamic Models with Dirichlet Process Mixtures
- Statistical Surveillance. Optimality and Methods
- A Monte Carlo Approach to Nonnormal and Nonlinear State-Space Modeling
- The Kalman filter model and Bayesian outlier detection for time series analysis of BOD data
- Models for longitudinal data with censored changepoints
- MODELING DYNAMIC INTERINDUSTRY REGIONAL GROWTH IN THE PRESENCE OF STRUCTURAL SHIFTS AND OUTLIERS
- Alarm Algorithms in Critical Care Monitoring
- A Mixture-Model Approach to Combining Forecasts
- A Signal Extraction Approach to Modeling Hormone Time Series with Pulses and a Changing Baseline
- Detecting change points and monitoring biomedical data
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