# Identifying Mentions of Pain in Mental Health Records Text: A Natural Language Processing Approach

arXiv: https://arxiv.org/abs/2304.01240 · HTML: https://mlsanity.com/paper/2304.01240/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 26 references that are in the map.
- First posted on arXiv 3 Apr 2023.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing](https://mlsanity.com/paper/2007.15779/) (2020)
- [Publicly Available Clinical BERT Embeddings](https://mlsanity.com/paper/1904.03323/) (2019)
- [ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission](https://mlsanity.com/paper/1904.05342/) (2019)
- [Self-Alignment Pretraining for Biomedical Entity Representations](https://mlsanity.com/paper/2010.11784/) (2020)
- [Deep Learning Based Text Classification: A Comprehensive Review](https://mlsanity.com/paper/2004.03705/) (2020)
- [Multi-domain Clinical Natural Language Processing with MedCAT: the Medical Concept Annotation Toolkit](https://mlsanity.com/paper/2010.01165/) (2020)
- [UmlsBERT: Clinical Domain Knowledge Augmentation of Contextual Embeddings Using the Unified Medical Language System Metathesaurus](https://mlsanity.com/paper/2010.10391/) (2020)
- [Clinical Text Classification with Rule-based Features and Knowledge-guided Convolutional Neural Networks](https://mlsanity.com/paper/1807.07425/) (2018)

## Built on by (most cited first, 2 of 2)

- [MentalQA: An Annotated Arabic Corpus for Questions and Answers of Mental Healthcare](https://mlsanity.com/paper/2405.12619/) (2024)
- [Evaluating the Effectiveness of the Foundational Models for Q&A Classification in Mental Health care](https://mlsanity.com/paper/2406.15966/) (2024)

## Abstract (arXiv)

Pain is a common reason for accessing healthcare resources and is a growing area of research, especially in its overlap with mental health. Mental health electronic health records are a good data source to study this overlap. However, much information on pain is held in the free text of these records, where mentions of pain present a unique natural language processing problem due to its ambiguous nature. This project uses data from an anonymised mental health electronic health records database. The data are used to train a machine learning based classification algorithm to classify sentences as discussing patient pain or not. This will facilitate the extraction of relevant pain information from large databases, and the use of such outputs for further studies on pain and mental health. 1,985 documents were manually triple-annotated for creation of gold standard training data, which was used to train three commonly used classification algorithms. The best performing model achieved an F1-score of 0.98 (95% CI 0.98-0.99).