  The introduction of computerized medical records in hospitals has reduced
burdensome activities like manual writing and information fetching. However,
the data contained in medical records are still far underutilized, primarily
because extracting data from unstructured textual medical records takes time
and effort. Information Extraction, a subfield of Natural Language Processing,
can help clinical practitioners overcome this limitation by using automated
text-mining pipelines. In this work, we created the first Italian
neuropsychiatric Named Entity Recognition dataset, PsyNIT, and used it to
develop a Transformers-based model. Moreover, we collected and leveraged three
external independent datasets to implement an effective multicenter model, with
overall F1-score 84.77%, Precision 83.16%, Recall 86.44%. The lessons learned
are: (i) the crucial role of a consistent annotation process and (ii) a
fine-tuning strategy that combines classical methods with a "low-resource"
approach. This allowed us to establish methodological guidelines that pave the
way for Natural Language Processing studies in less-resourced languages.
