Rapid identification of pathogens is required for early diagnosis and treatment of life-threatening bloodstream infections in humans. This requirement is driving the current developments of molecular diagnostic tools identifying pathogens from human whole blood after successful isolation and cultivation. An alternative approach is to determine pathogen-specific signatures from human host immune cells that have been exposed to pathogens. We hypothesise that activated immune cells, such as neutrophils, may exhibit a characteristic behaviour — for instance in terms of their speed, dynamic cell morphology — that allows (i) identifying the type of pathogen indirectly and (ii) providing information on therapeutic efficacy. In this feasibility study, we propose a method for the quantitative assessment of static and morphodynamic features of neutrophils based on label-free time-lapse imaging data. We investigate neutrophil activation phenotypes after confrontation with fungal pathogens and isolation from a human whole-blood assay. In particular, we applied a machine learning supported approach to time-lapse microscopy data from different infection scenarios and were able to distinguish between Candida albicans and C. glabrata infection scenarios with test accuracies well above 75%, and to identify pathogen-free samples with accuracy reaching 100%. These results significantly exceed the test accuracies achieved using state-of-the-art deep neural networks to classify neutrophils by their morphodynamics.