Learning entities from narratives of skin cancer (LENS) is an automatic entity recognition system built on colloquial writings from skin cancer-related Reddit forums. LENS encapsulates a comprehensive set of 24 labels that address clinical, demographic, and psychosocial aspects of skin cancer. Furthermore, we release LENS as a PyPI and pip package, making it easy for developers to download and install, and also provide a web application that allows users to get model predictions interactively, useful for researchers and individuals with minimal programming experience. Additionally, we publish the annotation guidelines designed specifically for spontaneous skin cancer narratives, that can be implemented to better understand and address challenges when developing corpora or systems for similar diseases. The model achieves an overall entity-level F1 score of 0.561, with notable performance for entities such as “CANC_T” (0.747), “STG” (0.788), “POB” (0.714), “GENDER” (0.750), “A/G” (0.714), and “PPL” (0.703). Other entities with significant results include “TRT” (0.625), “MED” (0.606), “AGE” (0.646), “EMO” (0.619), and “MHD” (0.5). We believe that LENS can serve as an essential tool supporting the analysis of patient discussions leading to improvements in the design and development of modern smart healthcare technologies.

Cancer not only affects a patient’s physical health, but it can also elicit a wide spectrum of intense emotions in patients, friends, and family members. People with cancer and their carers (family member, partner, or friend) are increasingly turning to the web for information and support. Despite the expansion of sentiment analysis in the context of social media and healthcare, there is relatively less research on patient narratives, which are longer, more complex texts, and difficult to assess. In this exploratory work, we examine how patients and carers express their feelings about various aspects of cancer (treatments and stages). The objective of this paper is to illustrate with examples the nature of language in the clinical domain, as well as the complexities of language when performing automatic sentiment and emotion analysis. We perform a linguistic analysis of a corpus of cancer narratives collected from Reddit. We examine the performance of five state-of-the-art models (T5, DistilBERT, Roberta, RobertaGo, and NRCLex) to see how well they match with human comparisons separated by linguistic and medical background. The corpus yielded several surprising results that could be useful to sentiment analysis NLP experts. The linguistic issues encountered were classified into four categories: statements expressing a variety of emotions, ambiguous or conflicting statements with contradictory emotions, statements requiring additional context, and statements in which sentiment and emotions can be inferred but are not explicitly mentioned.

Metaphors shape the way we think by enabling the expression of one concept in terms of another one. For instance, cancer can be understood as a place from which one can go in and out, as a journey that one can traverse, or as a battle. Giving patients awareness of the way they refer to cancer and different narratives in which they can reframe it has been proven to be a key aspect when experiencing the disease. In this work, we propose a preliminary identification and representation of Spanish cancer metaphors using MIP (Metaphor Identification Procedure) and MetaNet. The created resource is the first openly available dataset for medical metaphors in Spanish. Thus, in the future, we expect to use it as the gold standard in automatic metaphor processing tasks, which will also serve to further populate the resource and understand how cancer is experienced and narrated.