Laboratório de Engenharia em Sistemas de Saúde, Programa de Engenharia Biomédica/COPPE/UFRJ, Centro de Tecnologia - Bloco H - Sala H327, Caixa Postal (P.O. Box): 68510, Cidade Universitária, Ilha do Fundão, Rio de Janeiro, RJ, 21941-972, Brazil.
Acute Febrile Illnesses Laboratory, Evandro Chagas National Institute of Infectious Diseases; Oswaldo Cruz Foundation (Fiocruz), Rio de Janeiro, RJ, Brazil.
BMC Infect Dis. 2019 Jul 22;19(1):649. doi: 10.1186/s12879-019-4282-y.
Despite the greater sensitivity of the new dengue clinical classification proposed by the World Health Organization (WHO) in 2009, there is a need for a better definition of warning signs and clinical progression of dengue cases. Classic statistical methods have been used to evaluate risk criteria in dengue patients, however they usually cannot access the complexity of dengue clinical profiles. We propose the use of machine learning as an alternative tool to identify the possible characteristics that could be used to develop a risk criterion for severity in dengue patients.
In this study, we analyzed the clinical profiles of 523 confirmed dengue cases using self-organizing maps (SOM) and random forest algorithms to identify clusters of patients with similar patterns.
We identified four natural clusters, two with features of dengue without warning signs or mild disease, one that comprises the severe dengue cases and high frequency of warning signs, and another with intermediate characteristics. Age appeared as the key variable for splitting the data into these four clusters although warning signs such as abdominal pain or tenderness, clinical fluid accumulation, mucosal bleeding, lethargy, restlessness, liver enlargement and increased hematocrit associated with a decrease in platelet counts should also be considered to evaluate severity in dengue patients.
These findings suggest that age must be the first characteristic to be considered in places where dengue is hyperendemic. Our results show that warning signs should be closely monitored, mainly in children. Further studies exploring these results in a longitudinal approach may help to understand the full spectrum of dengue clinical manifestations.
尽管 2009 年世界卫生组织（WHO）提出的新的登革热临床分类具有更高的敏感性，但仍需要更好地定义登革热病例的预警症状和临床进展。经典的统计方法已被用于评估登革热患者的风险标准，但它们通常无法了解登革热临床特征的复杂性。我们建议使用机器学习作为替代工具，以确定可能用于制定登革热患者严重程度风险标准的特征。
在这项研究中，我们使用自组织映射（SOM）和随机森林算法分析了 523 例确诊登革热病例的临床特征，以识别具有相似模式的患者聚类。
我们确定了四个自然聚类，其中两个具有无预警症状或轻度疾病的登革热特征，一个包含严重登革热病例和高频率预警症状的聚类，另一个具有中间特征。年龄似乎是将数据分为这四个聚类的关键变量，但腹痛或压痛、临床体液积聚、黏膜出血、嗜睡、烦躁不安、肝肿大和血小板计数下降相关的血细胞比容增加等预警症状也应考虑在内，以评估登革热患者的严重程度。
这些发现表明，在登革热高度流行的地区，年龄必须是首先要考虑的特征。我们的结果表明，应密切监测预警症状，尤其是在儿童中。进一步的研究以纵向方式探索这些结果可能有助于了解登革热临床表现的全貌。
