Complex methods to assess complex problems: using Natural Language Processing to explore Health Related Qualitiy of Life in endometriosis

Antonio Sarría‐Santamera
In: International Journal of Integrated Care · 2021 · vol. 21(S1) , pp. 325 · doi:10.5334/ijic.icic20467 · W3197384300
Systematic review / meta-analysis OA: gold CC0
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This systematic review of systematic reviews used natural language processing to analyze health-related quality of life in endometriosis, finding that chronic pelvic pain and dyspareunia significantly impair physical, social, sexual, and emotional wellbeing.

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This conference abstract describes a systematic review of systematic reviews that utilized Natural Language Processing to analyze health-related quality of life in women with endometriosis. The researchers extracted free text from seventeen reviews focusing on pain and dyspareunia, employing a word2vec skip-gram model to identify contextual factors associated with quality of life losses. The study found that chronic pelvic pain significantly impacts physical, social, and functional dimensions, while painful intercourse disrupts sexual life and emotional wellbeing, highlighting the need for standardized measurement instruments. This paper is centrally about endometriosis — specifically exploring how NLP can assess the complex impact of clinical symptoms like pain and dyspareunia on patient quality of life.

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Abstract

Background and Objectives: Endometriosis is a benign, although chronic painful condition that represents a major burden in women’s life, affecting all the domains of their health related quality of life (HRQL). The objective of this work is to conduct a Systematic Review of Systematic Reviews assessing HRQL of women with endometriosis, and to test a methodology based on Natural Language Processing (NLP) to assess factors associated with losses of HRQL linked with the most relevant clinical symptoms that women with endometriosis experience: pain and dyspareunia.Methods:We extracted free text from articles included in the Systematic Reviews with 'Pain' and 'dyspareunia' as keywords, and we used NLP to identify their occurrences, grammatical variants and meaningful context words. Our model used a word2vec skip-gram approach to predict context following our keywords.Results: Text data included 6,430 context words corresponding to 1,655 unique words in the 17 different systematic reviews that were included for review.Conclusions: The method we have tested using NLP has demonstrated to be valid to capture the complexity of issues that surround endometriosis and affects women´s HRQL. Among the diversity of symptoms that women with endometriosis experience, chronic pelvic pain has the highest impact on their HRQL. Pain affects their physical health, but also the social and functioning dimensions of women’s life. Painful intercourse has also a significant impact on the sexual life of women with endometriosis, and disturbing the emotional wellbeing of women. HRQL is also affected by the clinical complexities of endometriosis, the difficulties to reach to a diagnosis as well as to provide women with effective medical care. Different measurements, instruments and approaches have been proposed to measure HRQL in endometriosis, but it seems necessary the use of common instruments to facilitate comparisons across studies.
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Background

and Objectives: Endometriosis is a benign, although chronic painful condition that represents a major burden in women’s life, affecting all the domains of their health related quality of life (HRQL). The objective of this work is to conduct a Systematic Review of Systematic Reviews assessing HRQL of women with endometriosis, and to test a methodology based on Natural Language Processing (NLP) to assess factors associated with losses of HRQL linked with the most relevant clinical symptoms that women with endometriosis experience: pain and dyspareunia.

Methods

We extracted free text from articles included in the Systematic Reviews with 'Pain' and 'dyspareunia' as keywords, and we used NLP to identify their occurrences, grammatical variants and meaningful context words. Our model used a word2vec skip-gram approach to predict context following our keywords.

Results

Text data included 6,430 context words corresponding to 1,655 unique words in the 17 different systematic reviews that were included for review.

Conclusions

The method we have tested using NLP has demonstrated to be valid to capture the complexity of issues that surround endometriosis and affects women´s HRQL. Among the diversity of symptoms that women with endometriosis experience, chronic pelvic pain has the highest impact on their HRQL. Pain affects their physical health, but also the social and functioning dimensions of women’s life. Painful intercourse has also a significant impact on the sexual life of women with endometriosis, and disturbing the emotional wellbeing of women. HRQL is also affected by the clinical complexities of endometriosis, the difficulties to reach to a diagnosis as well as to provide women with effective medical care. Different measurements, instruments and approaches have been proposed to measure HRQL in endometriosis, but it seems necessary the use of common instruments to facilitate comparisons across studies.

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endometriosischronic_pelvic_paindyspareunia

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