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Machine Learning for Predicting Risk of Early Dropout in a Recovery Program for Opioid Use Disorder
Background: An increase in opioid use has led to an opioid crisis during the last decade, leading to declarations of a public health emergency. In response to this call, the Houston Emergency Opioid Engagement System (HEROES) was established and created an emergency access pathway into long-term rec...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8871589/ https://www.ncbi.nlm.nih.gov/pubmed/35206838 http://dx.doi.org/10.3390/healthcare10020223 |
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author | Gottlieb, Assaf Yatsco, Andrea Bakos-Block, Christine Langabeer, James R. Champagne-Langabeer, Tiffany |
author_facet | Gottlieb, Assaf Yatsco, Andrea Bakos-Block, Christine Langabeer, James R. Champagne-Langabeer, Tiffany |
author_sort | Gottlieb, Assaf |
collection | PubMed |
description | Background: An increase in opioid use has led to an opioid crisis during the last decade, leading to declarations of a public health emergency. In response to this call, the Houston Emergency Opioid Engagement System (HEROES) was established and created an emergency access pathway into long-term recovery for individuals with an opioid use disorder. A major contributor to the success of the program is retention of the enrolled individuals in the program. Methods: We have identified an increase in dropout from the program after 90 and 120 days. Based on more than 700 program participants, we developed a machine learning approach to predict the individualized risk for dropping out of the program. Results: Our model achieved sensitivity of 0.81 and specificity of 0.65 for dropout at 90 days and improved the performance to sensitivity of 0.86 and specificity of 0.66 for 120 days. Additionally, we identified individual risk factors for dropout, including previous overdose and relapse and improvement in reported quality of life. Conclusions: Our informatics approach provides insight into an area where programs may allocate additional resources in order to retain high-risk individuals and increase the chances of success in recovery. |
format | Online Article Text |
id | pubmed-8871589 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-88715892022-02-25 Machine Learning for Predicting Risk of Early Dropout in a Recovery Program for Opioid Use Disorder Gottlieb, Assaf Yatsco, Andrea Bakos-Block, Christine Langabeer, James R. Champagne-Langabeer, Tiffany Healthcare (Basel) Article Background: An increase in opioid use has led to an opioid crisis during the last decade, leading to declarations of a public health emergency. In response to this call, the Houston Emergency Opioid Engagement System (HEROES) was established and created an emergency access pathway into long-term recovery for individuals with an opioid use disorder. A major contributor to the success of the program is retention of the enrolled individuals in the program. Methods: We have identified an increase in dropout from the program after 90 and 120 days. Based on more than 700 program participants, we developed a machine learning approach to predict the individualized risk for dropping out of the program. Results: Our model achieved sensitivity of 0.81 and specificity of 0.65 for dropout at 90 days and improved the performance to sensitivity of 0.86 and specificity of 0.66 for 120 days. Additionally, we identified individual risk factors for dropout, including previous overdose and relapse and improvement in reported quality of life. Conclusions: Our informatics approach provides insight into an area where programs may allocate additional resources in order to retain high-risk individuals and increase the chances of success in recovery. MDPI 2022-01-25 /pmc/articles/PMC8871589/ /pubmed/35206838 http://dx.doi.org/10.3390/healthcare10020223 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Gottlieb, Assaf Yatsco, Andrea Bakos-Block, Christine Langabeer, James R. Champagne-Langabeer, Tiffany Machine Learning for Predicting Risk of Early Dropout in a Recovery Program for Opioid Use Disorder |
title | Machine Learning for Predicting Risk of Early Dropout in a Recovery Program for Opioid Use Disorder |
title_full | Machine Learning for Predicting Risk of Early Dropout in a Recovery Program for Opioid Use Disorder |
title_fullStr | Machine Learning for Predicting Risk of Early Dropout in a Recovery Program for Opioid Use Disorder |
title_full_unstemmed | Machine Learning for Predicting Risk of Early Dropout in a Recovery Program for Opioid Use Disorder |
title_short | Machine Learning for Predicting Risk of Early Dropout in a Recovery Program for Opioid Use Disorder |
title_sort | machine learning for predicting risk of early dropout in a recovery program for opioid use disorder |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8871589/ https://www.ncbi.nlm.nih.gov/pubmed/35206838 http://dx.doi.org/10.3390/healthcare10020223 |
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