Predicción de estados de hipotensión empleando modelos ocultos de Markov

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Autores

Diego Evin
Alejandro Hadad
Mauro Martina
Bartolomé Drozdowicz

Abstract

Se estudia la utilización de modelos ocultos de Markov para predecir estados de hipotensión en pacientes internados en unidades de cuidados intensivos. El procedimiento de predicción desarrollado cuenta con dos modelos de Markov, uno entrenado con datos fisiológicos de pacientes que en un determinado intervalo de tiempo desarrollan estados de hipotensión, y otro entrenado con datos de pacientes en los cuales no se registra dicho cuadro. Ante datos de un nuevo paciente y empleando un marco bayesiano, el sistema estima qué modelo explica mejor las nuevas observaciones, y se establece una asociación del paciente a la clase del modelo seleccionado. Experimentos preliminares empleando el modelo propuesto sobre datos estándar muestran resultados promisorios.

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