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Probabilistic Prediction of Nonadherence to Psychiatric Disorder Medication from Mental Health Forum Data: Developing and Validating Bayesian Machine Learning Classifiers

BACKGROUND: Medication nonadherence represents a major burden on national health systems. According to the World Health Organization, increasing medication adherence may have a greater impact on public health than any improvement in specific medical treatments. More research is needed to better pred...

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Autores principales: Ji, Meng, Xie, Wenxiu, Zhao, Mengdan, Qian, Xiaobo, Chow, Chi-Yin, Lam, Kam-Yiu, Yan, Jun, Hao, Tianyong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9033323/
https://www.ncbi.nlm.nih.gov/pubmed/35463247
http://dx.doi.org/10.1155/2022/6722321
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author Ji, Meng
Xie, Wenxiu
Zhao, Mengdan
Qian, Xiaobo
Chow, Chi-Yin
Lam, Kam-Yiu
Yan, Jun
Hao, Tianyong
author_facet Ji, Meng
Xie, Wenxiu
Zhao, Mengdan
Qian, Xiaobo
Chow, Chi-Yin
Lam, Kam-Yiu
Yan, Jun
Hao, Tianyong
author_sort Ji, Meng
collection PubMed
description BACKGROUND: Medication nonadherence represents a major burden on national health systems. According to the World Health Organization, increasing medication adherence may have a greater impact on public health than any improvement in specific medical treatments. More research is needed to better predict populations at risk of medication nonadherence. OBJECTIVE: To develop clinically informative, easy-to-interpret machine learning classifiers to predict people with psychiatric disorders at risk of medication nonadherence based on the syntactic and structural features of written posts on health forums. METHODS: All data were collected from posts between 2016 and 2021 on mental health forum, administered by Together 4 Change, a long-running not-for-profit organisation based in Oxford, UK. The original social media data were annotated using the Tool for the Automatic Analysis of Syntactic Sophistication and Complexity (TAASSC) system. Through applying multiple feature optimisation techniques, we developed a best-performing model using relevance vector machine (RVM) for the probabilistic prediction of medication nonadherence among online mental health forum discussants. RESULTS: The best-performing RVM model reached a mean AUC of 0.762, accuracy of 0.763, sensitivity of 0.779, and specificity of 0.742 on the testing dataset. It outperformed competing classifiers with more complex feature sets with statistically significant improvement in sensitivity and specificity, after adjusting the alpha levels with Benjamini–Hochberg correction procedure. Discussion. We used the forest plot of multiple logistic regression to explore the association between written post features in the best-performing RVM model and the binary outcome of medication adherence among online post contributors with psychiatric disorders. We found that increased quantities of 3 syntactic complexity features were negatively associated with psychiatric medication adherence: “dobj_stdev” (standard deviation of dependents per direct object of nonpronouns) (OR, 1.486, 95% CI, 1.202–1.838, P < 0.001), “cl_av_deps” (dependents per clause) (OR, 1.597, 95% CI, 1.202–2.122, P, 0.001), and “VP_T” (verb phrases per T-unit) (OR, 2.23, 95% CI, 1.211–4.104, P, 0.010). Finally, we illustrated the clinical use of the classifier with Bayes' monograph which gives the posterior odds and their 95% CI of positive (nonadherence) versus negative (adherence) cases as predicted by the best-performing classifier. The odds ratio of the posterior probability of positive cases was 3.9, which means that around 10 in every 13 psychiatric patients with a positive result as predicted by our model were following their medication regime. The odds ratio of the posterior probability of true negative cases was 0.4, meaning that around 10 in every 14 psychiatric patients with a negative test result after screening by our classifier were not adhering to their medications. CONCLUSION: Psychiatric medication nonadherence is a large and increasing burden on national health systems. Using Bayesian machine learning techniques and publicly accessible online health forum data, our study illustrates the viability of developing cost-effective, informative decision aids to support the monitoring and prediction of patients at risk of medication nonadherence.
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spelling pubmed-90333232022-04-23 Probabilistic Prediction of Nonadherence to Psychiatric Disorder Medication from Mental Health Forum Data: Developing and Validating Bayesian Machine Learning Classifiers Ji, Meng Xie, Wenxiu Zhao, Mengdan Qian, Xiaobo Chow, Chi-Yin Lam, Kam-Yiu Yan, Jun Hao, Tianyong Comput Intell Neurosci Research Article BACKGROUND: Medication nonadherence represents a major burden on national health systems. According to the World Health Organization, increasing medication adherence may have a greater impact on public health than any improvement in specific medical treatments. More research is needed to better predict populations at risk of medication nonadherence. OBJECTIVE: To develop clinically informative, easy-to-interpret machine learning classifiers to predict people with psychiatric disorders at risk of medication nonadherence based on the syntactic and structural features of written posts on health forums. METHODS: All data were collected from posts between 2016 and 2021 on mental health forum, administered by Together 4 Change, a long-running not-for-profit organisation based in Oxford, UK. The original social media data were annotated using the Tool for the Automatic Analysis of Syntactic Sophistication and Complexity (TAASSC) system. Through applying multiple feature optimisation techniques, we developed a best-performing model using relevance vector machine (RVM) for the probabilistic prediction of medication nonadherence among online mental health forum discussants. RESULTS: The best-performing RVM model reached a mean AUC of 0.762, accuracy of 0.763, sensitivity of 0.779, and specificity of 0.742 on the testing dataset. It outperformed competing classifiers with more complex feature sets with statistically significant improvement in sensitivity and specificity, after adjusting the alpha levels with Benjamini–Hochberg correction procedure. Discussion. We used the forest plot of multiple logistic regression to explore the association between written post features in the best-performing RVM model and the binary outcome of medication adherence among online post contributors with psychiatric disorders. We found that increased quantities of 3 syntactic complexity features were negatively associated with psychiatric medication adherence: “dobj_stdev” (standard deviation of dependents per direct object of nonpronouns) (OR, 1.486, 95% CI, 1.202–1.838, P < 0.001), “cl_av_deps” (dependents per clause) (OR, 1.597, 95% CI, 1.202–2.122, P, 0.001), and “VP_T” (verb phrases per T-unit) (OR, 2.23, 95% CI, 1.211–4.104, P, 0.010). Finally, we illustrated the clinical use of the classifier with Bayes' monograph which gives the posterior odds and their 95% CI of positive (nonadherence) versus negative (adherence) cases as predicted by the best-performing classifier. The odds ratio of the posterior probability of positive cases was 3.9, which means that around 10 in every 13 psychiatric patients with a positive result as predicted by our model were following their medication regime. The odds ratio of the posterior probability of true negative cases was 0.4, meaning that around 10 in every 14 psychiatric patients with a negative test result after screening by our classifier were not adhering to their medications. CONCLUSION: Psychiatric medication nonadherence is a large and increasing burden on national health systems. Using Bayesian machine learning techniques and publicly accessible online health forum data, our study illustrates the viability of developing cost-effective, informative decision aids to support the monitoring and prediction of patients at risk of medication nonadherence. Hindawi 2022-04-15 /pmc/articles/PMC9033323/ /pubmed/35463247 http://dx.doi.org/10.1155/2022/6722321 Text en Copyright © 2022 Meng Ji et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Ji, Meng
Xie, Wenxiu
Zhao, Mengdan
Qian, Xiaobo
Chow, Chi-Yin
Lam, Kam-Yiu
Yan, Jun
Hao, Tianyong
Probabilistic Prediction of Nonadherence to Psychiatric Disorder Medication from Mental Health Forum Data: Developing and Validating Bayesian Machine Learning Classifiers
title Probabilistic Prediction of Nonadherence to Psychiatric Disorder Medication from Mental Health Forum Data: Developing and Validating Bayesian Machine Learning Classifiers
title_full Probabilistic Prediction of Nonadherence to Psychiatric Disorder Medication from Mental Health Forum Data: Developing and Validating Bayesian Machine Learning Classifiers
title_fullStr Probabilistic Prediction of Nonadherence to Psychiatric Disorder Medication from Mental Health Forum Data: Developing and Validating Bayesian Machine Learning Classifiers
title_full_unstemmed Probabilistic Prediction of Nonadherence to Psychiatric Disorder Medication from Mental Health Forum Data: Developing and Validating Bayesian Machine Learning Classifiers
title_short Probabilistic Prediction of Nonadherence to Psychiatric Disorder Medication from Mental Health Forum Data: Developing and Validating Bayesian Machine Learning Classifiers
title_sort probabilistic prediction of nonadherence to psychiatric disorder medication from mental health forum data: developing and validating bayesian machine learning classifiers
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9033323/
https://www.ncbi.nlm.nih.gov/pubmed/35463247
http://dx.doi.org/10.1155/2022/6722321
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