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HIV RNA viral load (VL) is an important outcome variable in studies of HIVinfected persons. There exists only a handful of methods which classifypatients by viral load patterns. Most methods place limits on the use of viralload measurements, are often specific to a particular study design, and do notaccount for complex, temporal variation. To address this issue, we propose aset of four unambiguous computable characteristics (features) of time-varyingHIV viral load patterns, along with a novel centroid-based classificationalgorithm, which we use to classify a population of 1,576 HIV positive clinicpatients into one of five different viral load patterns (clusters) often foundin the literature: durably suppressed viral load (DSVL), sustained low viralload (SLVL), sustained high viral load (SHVL), high viral load suppression(HVLS), and rebounding viral load (RVL). The centroid algorithm summarizesthese clusters in terms of their centroids and radii. We show that this allowsnew viral load patterns to be assigned pattern membership based on the distancefrom the centroid relative to its radius, which we term radial normalizationclassification. This method has the benefit of providing an objective andquantitative method to assign viral load pattern membership with a concise andinterpretable model that aids clinical decision making. This method alsofacilitates meta-analyses by providing computably distinct HIV categories.Finally we propose that this novel centroid algorithm could also be useful inthe areas of cluster comparison for outcomes research and data reduction inmachine learning.

Revealing patterns in HIV viral load data and classifying patients via a novel machine learning cluster summarization method