Microscopic images of cells often contain multiple objects with similar properties. In this study, we collected videos of blood microcirculation in nailfold capillaries to identify correlations between video data and indicators of complete blood count tests. The specific features of these videos include a high number of capillaries and a high variance in their visual quality. Therefore, we proposed a novel loss function and a video processing framework that combines metric learning and classification with uncertainty estimation. We also applied test-time augmentation to enhance the confidence of the predictions. During simulation experiments, we used shallow and deep learning models to compare a set of classifiers for the collected metadata. Both types of models demonstrated good results, with an accuracy of up to 80% in gender classification. Deep learning models outperformed shallow models and also classified the age of the volunteers with an accuracy of 74%. Subsequently, we addressed a series of binary classification tasks to identify conditionally high or low levels of the main complete blood count indicators. The obtained accuracy of the models reached 63%. Although the accuracy of the blood test classifiers is not yet sufficient for clinical purposes, we have demonstrated that video data correlates with some of the indicators.