Abstract
Multiple diagnoses of the patient’s state based on results of successive examinations is one of the most frequent and typical medical diagnosing tasks. Such a task, henceforth called the sequential diagnosis, involves dealing with a complex decision problem. This is caused by the dependence of the patient’s state at a given time on the preceding states and on the already applied treatment. Although there remains no doubt as to the very existence of this dependence, it may be of a diversified nature and range; its simplest instance can be a one-instantbackwards dependence to so complex arrangements as those in which the current state depends on the whole former course of the disease.
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Kurzyński, M.W. (2000). Benchmark of Approaches to Sequential Diagnosis. In: Lisboa, P.J.G., Ifeachor, E.C., Szczepaniak, P.S. (eds) Artificial Neural Networks in Biomedicine. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-0487-2_11
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DOI: https://doi.org/10.1007/978-1-4471-0487-2_11
Publisher Name: Springer, London
Print ISBN: 978-1-85233-005-7
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