Development of a Lexicon for Pain

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

OBJECTIVE

The 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 PARTICIPANTS

The 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.

OUTCOME

The primary outcome of interest was the presence of recorded physical pain within the clinical notes of the patients, not including psychological or metaphorical pain.

RESULTS

A 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.

CONCLUSION

The 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.

Development of a Knowledge Graph Embeddings Model for Pain

Pain is a complex concept that can interconnect with other concepts such as a disorder that might cause pain, a medication that might relieve pain, and so on. To fully understand the context of pain experienced by either an individual or across a population, we may need to examine all concepts related to pain and the relationships between them. This is especially useful when modeling pain that has been recorded in electronic health records. Knowledge graphs represent concepts and their relations by an interlinked network, enabling semantic and context-based reasoning in a computationally tractable form. These graphs can, however, be too large for efficient computation. Knowledge graph embeddings help to resolve this by representing the graphs in a low-dimensional vector space. These embeddings can then be used in various downstream tasks such as classification and link prediction. The various relations associated with pain which are required to construct such a knowledge graph can be obtained from external medical knowledge bases such as SNOMED CT, a hierarchical systematic nomenclature of medical terms. A knowledge graph built in this way could be further enriched with real-world examples of pain and its relations extracted from electronic health records. This paper describes the construction of such knowledge graph embedding models of pain concepts, extracted from the unstructured text of mental health electronic health records, combined with external knowledge created from relations described in SNOMED CT, and their evaluation on a subject-object link prediction task. The performance of the models was compared with other baseline models.

Development and Evaluation of Linguistic Stimuli for Pain Research

Linguistic stimuli are commonly used in research to investigate the processing of pain. To provide researchers with a dataset of pain-related and non-pain-related linguistic stimuli, this research investigated 1) the associative strength between pain-related words and the pain construct; 2) the pain-relatedness ratings of pain words; and 3) the variability in the relatedness of pain words within pain word classifications (eg, sensory pain words). In Study 1, 194 pain-related and matched non-pain-related words were retrieved by reviewing the pain-related attentional bias literature. In Study 2, adults with (n = 85) and without (n = 48) self-reported chronic pain completed a speeded word categorization paradigm and rated the pain-relatedness of a subset of pain words. Analyses revealed that 1) despite differences in associative strength of 11.3% of the words between chronic and non-chronic pain groups, no overall group difference was found, 2) the chronic pain group rated the pain words as more pain-related compared to the non-chronic pain group, and 3) there was variability in the relatedness of pain words within pain word classifications. The findings highlight the importance of validating linguistic pain stimuli. The resulting dataset is openly accessible and new published sets can be added to the Linguistic Materials for Pain (LMaP) Repository. PERSPECTIVE: This article presents the development and preliminary evaluation of a large pool of pain-related and non-pain-related words in adults with and without self-reported chronic pain. Findings are discussed and guidelines are offered to select the most suitable stimuli for future research.

Artificial Intelligence for Automatic Pain Assessment: Research Methods and Perspectives

Although proper pain evaluation is mandatory for establishing the appropriate therapy, self-reported pain level assessment has several limitations. Data-driven artificial intelligence (AI) methods can be employed for research on automatic pain assessment (APA). The goal is the development of objective, standardized, and generalizable instruments useful for pain assessment in different clinical contexts. The purpose of this article is to discuss the state of the art of research and perspectives on APA applications in both research and clinical scenarios. Principles of AI functioning will be addressed. For narrative purposes, AI-based methods are grouped into behavioral-based approaches and neurophysiology-based pain detection methods. Since pain is generally accompanied by spontaneous facial behaviors, several approaches for APA are based on image classification and feature extraction. Language features through natural language strategies, body postures, and respiratory-derived elements are other investigated behavioral-based approaches. Neurophysiology-based pain detection is obtained through electroencephalography, electromyography, electrodermal activity, and other biosignals. Recent approaches involve multimode strategies by combining behaviors with neurophysiological findings. Concerning methods, early studies were conducted by machine learning algorithms such as support vector machine, decision tree, and random forest classifiers. More recently, artificial neural networks such as convolutional and recurrent neural network algorithms are implemented, even in combination. Collaboration programs involving clinicians and computer scientists must be aimed at structuring and processing robust datasets that can be used in various settings, from acute to different chronic pain conditions. Finally, it is crucial to apply the concepts of explainability and ethics when examining AI applications for pain research and management.

Lexicon of Metaphors: An Innovative Approach to Detect the Aftermath of Traumatic Childbirth

An innovative approach to help mental health providers identify individuals who may be struggling with the aftermath of birth trauma is to become vigilant in listening for metaphors women may use to describe how they have been feeling. Metaphors can provide a safe bridge for individuals to share and work through painful and challenging feelings. This lexicon of metaphors has four sections: impact birth trauma had on breastfeeding, impaired mother-infant interaction, anniversary of birth trauma, and subsequent childbirth. Examples of these metaphors include empty affair, head in a vise, short fuze, severed ties, great pretender, and mental baggage.