Abstract
In medicine, prognosis is the task of predicting the probable course and outcome of a disease. Questions like, is a patient going to improve?, what is his/her chance of recovery?, and how likely a relapse is? are common and they rely on the concept of state. The feasible states of a disease define a partial order structure with extreme states those of ’cure’ and ’death’; improving, recovering, and survival meaning particular transitions between states of the partial order. In spite of this, it is not usual in medicine to find an explicit representation either of the states or of the states partial order for many diseases. On the contrary, the variables (e.g. signs and symptoms) related to a disease and their normality and abnormality values are broadly agreed. Here, an inductive algorithm is introduced that generates partial orders from a data matrix containing information about the patient-professional encounters, and the normality functions of each one of these disease variables.
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Bohada, J.A., Riaño, D., Real, F. (2007). Induction of Partial Orders to Predict Patient Evolutions in Medicine. In: Bellazzi, R., Abu-Hanna, A., Hunter, J. (eds) Artificial Intelligence in Medicine. AIME 2007. Lecture Notes in Computer Science(), vol 4594. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73599-1_65
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DOI: https://doi.org/10.1007/978-3-540-73599-1_65
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73598-4
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