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A Knowledge-based Model of Expert System to Assist Exclusive Breastfeeding Mother

机译:基于知识的专家系统协助纯母乳喂养母亲

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Exclusive breastfeeding is crucial in the early newborn nutrition fulfillment with many benefits such as preventing mental and behavioral disorders, supporting brain development and intelligence, and providing more than 100 enzymes. In this study, we develop a knowledge-based model which can assist exclusive breastfeeding mother against clinical finding found during the first six months period. The model takes the information of experienced symptoms/conditions (in both the mother and the newborn), gives the calculation of certain clinical finding’s risk factor based on Certainty Factor (CF) given, and gives recommendation of follow up to the user. Therefore, the newborn's mother can monitor their health condition and use this model as a precaution to get health assistance as soon as possible through health service providers in her surroundings. The knowledge base and its CF are built from domain experts knowledge acquisition and Indonesian related health literature. The model is evaluated based on 25 different random trials against experts’ assessment. The precision from this evaluation reaches 88% by taking given symptom/condition only, which shows that the developed model performs very well in detecting clinical finding through CF-based risk factor calculation. However, in the end of this paper we also point out the way to increase higher precision to close to perfection by drilling the information of symptom/condition given.
机译:纯母乳喂养对新生儿的早期营养至关重要,它具有许多好处,例如预防精神和行为障碍,支持大脑发育和智力,并提供100多种酶。在这项研究中,我们开发了一个基于知识的模型,该模型可以帮助纯母乳喂养的母亲对抗在头六个月内发现的临床发现。该模型将获取经验丰富的症状/状况(在母亲和新生儿中)的信息,并根据给定的确定性因子(CF)来计算某些临床发现的危险因素,并向用户推荐后续行动。因此,新生儿的母亲可以监视他们的健康状况,并使用此模型作为预防措施,以尽快通过其周围的医疗服务提供者获得医疗帮助。知识库及其CF基于领域专家的知识获取和印度尼西亚相关的健康文献而建立。该模型是根据25条针对专家评估的随机试验进行评估的。仅考虑给定的症状/情况,此评估的精度达到88%,这表明开发的模型在通过基于CF的危险因素计算来检测临床发现方面表现非常出色。但是,在本文的最后,我们还指出了通过钻取给定的症状/条件信息来提高更高的精度以接近完美的方法。

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