Clinical narratives are a valuable source of information for both patient care and
biomedical research. Given the unstructured nature of medical reports, specific automatic
techniques are required to extract relevant entities from such texts. In the natural
language processing (NLP) community, this task is often addressed by using supervised
methods. To develop such methods, both reliably-annotated corpora and elaborately
designed features are needed. Despite the recent advances on corpora collection and
annotation, research on multiple domains and languages is still limited. In addition,
to compute the features required for supervised classification, suitable language-
and domain-specific tools are needed. In this work, we propose a novel application
of recurrent neural networks (RNNs) for event extraction from medical reports written
in Italian. To train and evaluate the proposed approach, we annotated a corpus of
75 cardiology reports for a total of 4,365 mentions of relevant events and their attributes
(e.g., the polarity). For the annotation task, we developed specific annotation guidelines,
which are provided together with this paper. The RNN-based classifier was trained
on a training set including 3,335 events (60 documents). The resulting model was integrated
into an NLP pipeline that uses a dictionary lookup approach to search for relevant
concepts inside the text. A test set of 1,030 events (15 documents) was used to evaluate
and compare different pipeline configurations. As a main result, using the RNN-based
classifier instead of the dictionary lookup approach allowed increasing recall from
52.4% to 88.9%, and precision from 81.1% to 88.2%. Further, using the two methods
in combination, we obtained final recall, precision, and F1 score of 91.7%, 88.6%,
and 90.1%, respectively. These experiments indicate that integrating a well-performing
RNN-based classifier with a standard knowledge-based approach can be a good strategy
to extract information from clinical text in non-English languages.