A Knowledge-Based Prototype to Support the Intelligent Diagnosis of High-Risk Pregnancy

Victor FLORES, Brian KEITH, Diego POBLETE, Claudio LEIVA

Abstract


High-risk pregnancy identification (HRP) involves data interpretation and analysis by experts of pregnancy characteristics, and similar prior experiences; this task can be complex depending on the characteristics of the pregnancy. To facilitate this task in Chile, a prototype system based on knowledge that, combining the available information (statistical data, background reported in specific papers for pregnancies in Chile and others worldwide, etc.) with the experience of experts, can support physicians in the task of identifying characteristics of risk pregnancies and can help to estimate morbidity in a neonate is proposed. This prototype of intelligent system uses symbolic representation, rules of inference and knowledge (from the expert and previous cases available in the literature), logic programming and a Java interface to generate interpretations of neonatal morbidity. Knowledge of the system is separated into knowledge bases: (i) factors (pathologies) of the mother that influence a pregnancy and (ii) factors related to the evolution of pregnancy. This paper shows how using the development technology of a knowledge-based system with the statistical analysis of data of the Chilean population and expert knowledge has generated a valid tool that can be useful in in the labor of the specialists working with high risk pregnancies.

Keywords


Knowledge-based system; High-risk pregnancy; Neonatal morbidity; Pregnancy risk factors

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Appl Med Inform is published since 1995.