Development of a Corpus Annotated with Mentions of Pain in Mental Health Records (Preprint)

UNSTRUCTURED Pain is a widespread issue, with 20% of adults suffering globally. A strong association has been demonstrated between pain and mental health conditions, and this association is known to exacerbate disability and impairment. Pain is also also known to be strongly related to emotions, which can lead to damaging consequences. As pain is a common reason for people to access healthcare facilities, electronic health records (EHRs) are a potential source of information on this pain. Mental health EHRs could be particularly beneficial since they can show the overlap of pain with mental health. Most mental health EHRs contain the majority of their information within the free-text sections of the records. However, it is challenging to extract information from free-text. Natural language processing (NLP) methods are therefore required to extract this information from the text. This research describes the development of a corpus of manually labelled mentions of pain and pain-related entities from the documents of a mental health EHR database, for use in the development and evaluation of future NLP methods. The EHR database used, CRIS (Clinical Record Interactive Search), consists of anonymised patient records from The South London and Maudsley (SLaM) NHS Foundation Trust in the UK. The corpus was developed through a process of manual annotation where pain mentions were marked as relevant (i.e., referring to physical pain afflicting the patient), negated (i.e., indicating absence of pain) or not-relevant (i.e. referring to pain affecting someone other than the patient, or metaphorical and hypothetical mentions). Relevant mentions were also annotated with additional attributes such as anatomical location affected by pain, pain character, and pain management measures, if mentioned. Over 70% of the mentions found within the documents were annotated as relevant, and about half of these mentions also included the anatomical location affected by the pain. In future work, the extracted information will be used to develop and evaluate a machine learning based NLP application to automatically extract relevant pain information from EHR databases.