Through an adequate survey of the history of the disease, Narrative Medicine (NM) aims to allow the definition and implementation of an effective, appropriate, and shared treatment path. In the present study different topic modeling techniques are compared, as Latent Dirichlet Allocation (LDA) and topic modeling based on BERT transformer, to extract meaningful insights in the Italian narration of COVID-19 pandemic. In particular, the main focus was the characterization of Post-acute Sequelae of COVID-19, (i.e., PASC) writings as opposed to writings by health professionals and general reflections on COVID-19, (i.e., non-PASC) writings, modeled as a semi-supervised task. The results show that the BERTopic-based approach outperforms the LDA-base approach by grouping in the same cluster the 97.26% of analyzed documents, and reaching an overall accuracy of 91.97%.

Through an adequate survey of the history of the disease, Narrative Medicine (NM) aims to allow the definition and implementation of an effective, appropriate, and shared treatment path. In the present study, standard text mining (TM) techniques are applied, as a Latent Dirichlet Allocation (LDA) model for topic modeling is used to characterize narrative medicine texts written on COVID-19. In particular, the focus was mainly on the writings of patients with Post-acute Sequelae of COVID-19, i.e., PASC, as opposed to writings by health professionals and general reflections on COVID-19. The results suggest that the testimonies of PASC patients can be used for identifying shared issues to focus on to be followed and supported appropriately, even from a psychological point of view.