Abstract
Biomedical data in the form of series of observations made on a single process at regular intervals constitute a discrete time series and are eligible for time series methods of analysis. The models yielded by this analysis provide the framework within which exponential smoothing methods may operate on the data to provide recurrent forecasts of future states of the process.
Because the forecasts may be made on an individual basis and are sensitive to the past behavior of the individual process, the methods are presented as being potentially of great utility in the management of chronic and progressive illnesses. When incorporated into automated testing and diagnostic systems, the forecasting method will provide the capability of making prognoses for large numbers of individuals, quickly, routinely and reproducibly.
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Hirschfeld, W.J. Forecasting and chronic illness. Bulletin of Mathematical Biophysics 33, 425–437 (1971). https://doi.org/10.1007/BF02476784
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DOI: https://doi.org/10.1007/BF02476784