Development of a Lexicon for Pain

Chaturvedi, Jaya; Mascio, Aurelie; Velupillai, Sumithra; Roberts, Angus

doi:10.3389/fdgth.2021.778305

Cited by 8 publications

(7 citation statements)

References 30 publications

(38 reference statements)

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“…Pain-related keywords generated from a lexicon of pain terms 42 were used to identify patients in the cohort who had mentions of physical pain (such as ‘worsening back pain’, ‘suffers from headaches’ and ‘complains of pain’) recorded in their clinical notes within the predetermined window. The lexicon contains terms, such as aching muscles, back pain, headache, myalgia, etc, and can be accessed online ( https://docs.google.com/spreadsheets/d/1z-6619UBdvwWrB9Sz4b1rbjDzuslOGCpts2DNc0naCc/edit?usp=sharing ).…”

Section: Methodsmentioning

confidence: 99%

Distributions of recorded pain in mental health records: a natural language processing based study

Chaturvedi,

Stewart,

Ashworth

et al. 2024

BMJ Open

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ObjectiveThe objective of this study is to determine demographic and diagnostic distributions of physical pain recorded in clinical notes of a mental health electronic health records database by using natural language processing and examine the overlap in recorded physical pain between primary and secondary care.Design, setting and participantsThe data were extracted from an anonymised version of the electronic health records of a large secondary mental healthcare provider serving a catchment of 1.3 million residents in south London. These included patients under active referral, aged 18+ at the index date of 1 July 2018 and having at least one clinical document (≥30 characters) between 1 July 2017 and 1 July 2019. This cohort was compared with linked primary care records from one of the four local government areas.OutcomeThe primary outcome of interest was the presence of recorded physical pain within the clinical notes of the patients, not including psychological or metaphorical pain.ResultsA total of 27 211 patients were retrieved. Of these, 52% (14,202) had narrative text containing relevant mentions of physical pain. Older patients (OR 1.17, 95% CI 1.15 to 1.19), females (OR 1.42, 95% CI 1.35 to 1.49), Asians (OR 1.30, 95% CI 1.16 to 1.45) or black (OR 1.49, 95% CI 1.40 to 1.59) ethnicities, living in deprived neighbourhoods (OR 1.64, 95% CI 1.55 to 1.73) showed higher odds of recorded pain. Patients with severe mental illnesses were found to be less likely to report pain (OR 0.43, 95% CI 0.41 to 0.46, p<0.001). 17% of the cohort from secondary care also had records from primary care.ConclusionThe findings of this study show sociodemographic and diagnostic differences in recorded pain. Specifically, lower documentation across certain groups indicates the need for better screening protocols and training on recognising varied pain presentations. Additionally, targeting improved detection of pain for minority and disadvantaged groups by care providers can promote health equity.

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Section: Methodsmentioning

confidence: 99%

Distributions of recorded pain in mental health records: a natural language processing based study

Chaturvedi,

Stewart,

Ashworth

et al. 2024

BMJ Open

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“…To identify documents within these tables that might contain mentions of pain, a lexicon of pain terms was used. This lexicon was developed by combining multiple data sources, as described in full in reference [ 18 ]. The lexicon consists of 382 unique pain-related terms.…”

Section: Methodsmentioning

confidence: 99%

Development of a Corpus Annotated With Mentions of Pain in Mental Health Records: Natural Language Processing Approach

Chaturvedi¹,

Chance²,

Mirza³

et al. 2023

JMIR Form Res

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Background Pain is a widespread issue, with 20% of adults (1 in 5) experiencing it 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 known to be strongly related to emotions, which can lead to damaging consequences. As pain is a common reason for people to access health care 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. Objective This research describes the development of a corpus of manually labeled 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. Methods The EHR database used, Clinical Record Interactive Search, consists of anonymized patient records from The South London and Maudsley National Health Service Foundation Trust in the United Kingdom. The corpus was developed through a process of manual annotation where pain mentions were marked as relevant (ie, referring to physical pain afflicting the patient), negated (ie, indicating absence of pain), or not relevant (ie, 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. Results A total of 5644 annotations were collected from 1985 documents (723 patients). Over 70% (n=4028) 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. The most common pain character was chronic pain, and the most commonly mentioned anatomical location was the chest. Most annotations (n=1857, 33%) were from patients who had a primary diagnosis of mood disorders (International Classification of Diseases—10th edition, chapter F30-39). Conclusions This research has helped better understand how pain is mentioned within the context of mental health EHRs and provided insight into the kind of information that is typically mentioned around pain in such a data source. 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.

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“…Because stress has a biological component (Epel et al 2018), LGBTQ+ adults may talk about their psychophysiological reactions to minority stress (e.g., "discomfort"). Hence, a pain lexicon was used (Chaturvedi et al 2021). The pain lexicon was developed using symptoms from patient-authored text, words relating to biomedical terms for pain, synonyms for pain from biomedical ontologies (e.g., The Unified Medical Language System), and word embeddings to find words that were similar to pain in latent lexicosemantic space.…”

Section: Pain Lexiconmentioning

confidence: 99%

The LGBTQ+ Minority Stress on Social Media (MiSSoM) Dataset: A Labeled Dataset for Natural Language Processing and Machine Learning

Cascalheira,

Chapagain,

Flinn

et al. 2024

ICWSM

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Minority stress is the leading theoretical construct for understanding LGBTQ+ health disparities. As such, there is an urgent need to develop innovative policies and technologies to reduce minority stress. To spur technological innovation, we created the largest labeled datasets on minority stress using natural language from subreddits related to sexual and gender minority people. A team of mental health clinicians, LGBTQ+ health experts, and computer scientists developed two datasets: (1) the publicly available LGBTQ+ Minority Stress on Social Media (MiSSoM) dataset and (2) the advanced request-only version of the dataset, LGBTQ+ MiSSoM+. Both datasets have seven labels related to minority stress, including an overall composite label and six sublabels. LGBTQ+ MiSSoM (N = 27,709) includes both human- and machine-annotated la-bels and comes preprocessed with features (e.g., topic models, psycholinguistic attributes, sentiment, clinical keywords, word embeddings, n-grams, lexicons). LGBTQ+ MiSSoM+ includes all the characteristics of the open-access dataset, but also includes the original Reddit text and sentence-level labeling for a subset of posts (N = 5,772). Benchmark supervised machine learning analyses revealed that features of the LGBTQ+ MiSSoM datasets can predict overall minority stress quite well (F1 = 0.869). Benchmark performance metrics yielded in the prediction of the other labels, namely prejudiced events (F1 = 0.942), expected rejection (F1 = 0.964), internalized stigma (F1 = 0.952), identity concealment (F1 = 0.971), gender dysphoria (F1 = 0.947), and minority coping (F1 = 0.917), were excellent. Descriptive analyses, ethical considerations, limitations, and possible use cases are provided.

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Development of a Lexicon for Pain

Cited by 8 publications

References 30 publications

Distributions of recorded pain in mental health records: a natural language processing based study

Distributions of recorded pain in mental health records: a natural language processing based study

Development of a Corpus Annotated With Mentions of Pain in Mental Health Records: Natural Language Processing Approach

The LGBTQ+ Minority Stress on Social Media (MiSSoM) Dataset: A Labeled Dataset for Natural Language Processing and Machine Learning

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