  The presence of detailed clinical information in electronic health record
(EHR) systems presents promising prospects for enhancing patient care through
automated retrieval techniques. Nevertheless, it is widely acknowledged that
accessing data within EHRs is hindered by various methodological challenges.
Specifically, the clinical notes stored in EHRs are composed in a narrative
form, making them prone to ambiguous formulations and highly unstructured data
presentations, while structured reports commonly suffer from missing and/or
erroneous data entries. This inherent complexity poses significant challenges
when attempting automated large-scale medical knowledge extraction tasks,
necessitating the application of advanced tools, such as natural language
processing (NLP), as well as data audit techniques. This work aims to address
these obstacles by creating and validating a novel pipeline designed to extract
relevant data pertaining to prostate cancer patients. The objective is to
exploit the inherent redundancies available within the integrated structured
and unstructured data entries within EHRs in order to generate comprehensive
and reliable medical databases, ready to be used in advanced research studies.
Additionally, the study explores potential opportunities arising from these
data, offering valuable prospects for advancing research in prostate cancer.
