Predicting pain among female survivors of recent interpersonal violence: A proof-of-concept machine-learning approach.

Interpersonal violence (IPV) is highly prevalent in the United States and is a major public health problem. The emergence and/or worsening of chronic pain are known sequelae of IPV; however, not all those who experience IPV develop chronic pain. To mitigate its development, it is critical to identif...

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Autores principales:	Edward Lannon, Francisco Sanchez-Saez, Brooklynn Bailey, Natalie Hellman, Kerry Kinney, Amber Williams, Subodh Nag, Matthew E Kutcher, Burel R Goodin, Uma Rao, Matthew C Morris
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/9d2eb5c7bd784e3ba9815db584c0e6c8
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spelling	oai:doaj.org-article:9d2eb5c7bd784e3ba9815db584c0e6c82021-12-02T20:08:56ZPredicting pain among female survivors of recent interpersonal violence: A proof-of-concept machine-learning approach.1932-620310.1371/journal.pone.0255277https://doaj.org/article/9d2eb5c7bd784e3ba9815db584c0e6c82021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0255277https://doaj.org/toc/1932-6203Interpersonal violence (IPV) is highly prevalent in the United States and is a major public health problem. The emergence and/or worsening of chronic pain are known sequelae of IPV; however, not all those who experience IPV develop chronic pain. To mitigate its development, it is critical to identify the factors that are associated with increased risk of pain after IPV. This proof-of-concept study used machine-learning strategies to predict pain severity and interference in 47 young women, ages 18 to 30, who experienced an incident of IPV (i.e., physical and/or sexual assault) within three months of their baseline assessment. Young women are more likely than men to experience IPV and to subsequently develop posttraumatic stress disorder (PTSD) and chronic pain. Women completed a comprehensive assessment of theory-driven cognitive and neurobiological predictors of pain severity and pain-related interference (e.g., pain, coping, disability, psychiatric diagnosis/symptoms, PTSD/trauma, executive function, neuroendocrine, and physiological stress response). Gradient boosting machine models were used to predict symptoms of pain severity and pain-related interference across time (Baseline, 1-,3-,6- follow-up assessments). Models showed excellent predictive performance for pain severity and adequate predictive performance for pain-related interference. This proof-of-concept study suggests that machine-learning approaches are a useful tool for identifying predictors of pain development in survivors of recent IPV. Baseline measures of pain, family life impairment, neuropsychological function, and trauma history were of greatest importance in predicting pain and pain-related interference across a 6-month follow-up period. Present findings support the use of machine-learning techniques in larger studies of post-IPV pain development and highlight theory-driven predictors that could inform the development of targeted early intervention programs. However, these results should be replicated in a larger dataset with lower levels of missing data.Edward LannonFrancisco Sanchez-SaezBrooklynn BaileyNatalie HellmanKerry KinneyAmber WilliamsSubodh NagMatthew E KutcherBurel R GoodinUma RaoMatthew C MorrisPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 7, p e0255277 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Edward Lannon Francisco Sanchez-Saez Brooklynn Bailey Natalie Hellman Kerry Kinney Amber Williams Subodh Nag Matthew E Kutcher Burel R Goodin Uma Rao Matthew C Morris Predicting pain among female survivors of recent interpersonal violence: A proof-of-concept machine-learning approach.
description	Interpersonal violence (IPV) is highly prevalent in the United States and is a major public health problem. The emergence and/or worsening of chronic pain are known sequelae of IPV; however, not all those who experience IPV develop chronic pain. To mitigate its development, it is critical to identify the factors that are associated with increased risk of pain after IPV. This proof-of-concept study used machine-learning strategies to predict pain severity and interference in 47 young women, ages 18 to 30, who experienced an incident of IPV (i.e., physical and/or sexual assault) within three months of their baseline assessment. Young women are more likely than men to experience IPV and to subsequently develop posttraumatic stress disorder (PTSD) and chronic pain. Women completed a comprehensive assessment of theory-driven cognitive and neurobiological predictors of pain severity and pain-related interference (e.g., pain, coping, disability, psychiatric diagnosis/symptoms, PTSD/trauma, executive function, neuroendocrine, and physiological stress response). Gradient boosting machine models were used to predict symptoms of pain severity and pain-related interference across time (Baseline, 1-,3-,6- follow-up assessments). Models showed excellent predictive performance for pain severity and adequate predictive performance for pain-related interference. This proof-of-concept study suggests that machine-learning approaches are a useful tool for identifying predictors of pain development in survivors of recent IPV. Baseline measures of pain, family life impairment, neuropsychological function, and trauma history were of greatest importance in predicting pain and pain-related interference across a 6-month follow-up period. Present findings support the use of machine-learning techniques in larger studies of post-IPV pain development and highlight theory-driven predictors that could inform the development of targeted early intervention programs. However, these results should be replicated in a larger dataset with lower levels of missing data.
format	article
author	Edward Lannon Francisco Sanchez-Saez Brooklynn Bailey Natalie Hellman Kerry Kinney Amber Williams Subodh Nag Matthew E Kutcher Burel R Goodin Uma Rao Matthew C Morris
author_facet	Edward Lannon Francisco Sanchez-Saez Brooklynn Bailey Natalie Hellman Kerry Kinney Amber Williams Subodh Nag Matthew E Kutcher Burel R Goodin Uma Rao Matthew C Morris
author_sort	Edward Lannon
title	Predicting pain among female survivors of recent interpersonal violence: A proof-of-concept machine-learning approach.
title_short	Predicting pain among female survivors of recent interpersonal violence: A proof-of-concept machine-learning approach.
title_full	Predicting pain among female survivors of recent interpersonal violence: A proof-of-concept machine-learning approach.
title_fullStr	Predicting pain among female survivors of recent interpersonal violence: A proof-of-concept machine-learning approach.
title_full_unstemmed	Predicting pain among female survivors of recent interpersonal violence: A proof-of-concept machine-learning approach.
title_sort	predicting pain among female survivors of recent interpersonal violence: a proof-of-concept machine-learning approach.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/9d2eb5c7bd784e3ba9815db584c0e6c8
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Predicting pain among female survivors of recent interpersonal violence: A proof-of-concept machine-learning approach.

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