Characterization of clinical patterns of dengue patients using an unsupervised machine learning approach

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.