Moreira Felipe Costa, Aihara André Yui, Lederman Henrique Manoel, Pisa Ivan Torres, Tenório Josceli Maria
Department of Health Informatics, Escola Paulista de Medicina da Universidade Federal de São Paulo (EPM-Unifesp), São Paulo, SP, Brazil.
Department of Diagnostic Imaging, Escola Paulista de Medicina da Universidade Federal de São Paulo (EPM-Unifesp), São Paulo, SP, Brazil.
Radiol Bras. 2018 Sep-Oct;51(5):297-302. doi: 10.1590/0100-3984.2017.0121.
Abstract.
To present a cognitive map to support the radiological diagnosis of solitary bone tumors, as well as to facilitate the determination of the nature of the tumor (benign or malignant), in pediatric patients.
We selected 28 primary lesions in pediatric patients, and we identified the findings typically associated with each of the diagnoses. The method used for the construction of the final cognitive map was the Bayesian belief network model with backward chaining.
We developed a logical, sequential structure, in the form of a cognitive map, based on the Bayesian belief network model, with the intention of simulating the sequence of human thinking, in order to minimize the number of unnecessary interventions and iatrogenic complications arising from the incorrect evaluation of bone lesions.
With this map, it will be possible to develop an application that will provide support to physicians and residents, as well as contributing to training in this area and consequently to a reduction in diagnostic errors in patients with bone lesions.
摘要。
呈现一种认知图谱,以支持儿童孤立性骨肿瘤的放射学诊断,并有助于确定肿瘤的性质(良性或恶性)。
我们选择了28例儿童原发性病变,并确定了与每种诊断相关的典型表现。构建最终认知图谱所采用的方法是具有反向链接的贝叶斯信念网络模型。
我们基于贝叶斯信念网络模型开发了一种逻辑的、有序的结构,以认知图谱的形式呈现,旨在模拟人类思维顺序,以尽量减少因对骨病变评估错误而产生的不必要干预和医源性并发症的数量。
借助此图谱,有可能开发一种应用程序,为医生和住院医师提供支持,有助于该领域的培训,并因此减少骨病变患者的诊断错误。