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Probabilistic Prediction of Nonadherence to Psychiatric Disorder Medication from Mental Health Forum Data: Developing

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Machine learning models can predict psychiatric medication nonadherence using online forum posts. This approach helps identify at-risk patients, improving public health outcomes and reducing healthcare burdens.

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Area of Science:

  • Computational linguistics
  • Psychiatric pharmacotherapy
  • Machine learning applications in healthcare

Background:

  • Medication nonadherence is a significant global health issue, particularly in psychiatric care.
  • Improving medication adherence can yield greater public health benefits than advancements in medical treatments alone.
  • Predicting individuals at risk of nonadherence is crucial for effective intervention strategies.

Purpose of the Study:

  • To develop machine learning classifiers for predicting medication nonadherence in psychiatric patients.
  • To utilize syntactic and structural features of online health forum posts for risk prediction.
  • To create clinically informative and interpretable prediction models.

Main Methods:

  • Data collected from 2016-2021 from a UK-based mental health forum.
  • Posts analyzed for syntactic and structural features using the Tool for the Automatic Analysis of Syntactic Sophistication and Complexity (TAASSC).
  • A relevance vector machine (RVM) model was developed and optimized for predicting medication nonadherence.

Main Results:

  • The best-performing RVM model achieved an AUC of 0.762, accuracy of 0.763, sensitivity of 0.779, and specificity of 0.742.
  • The model demonstrated statistically significant improvements in sensitivity and specificity compared to other classifiers.
  • Increased syntactic complexity features (e.g., 'dobj_stdev', 'cl_av_deps', 'VP_T') were negatively associated with medication adherence.

Conclusions:

  • Machine learning, using online forum data, can effectively predict psychiatric medication nonadherence.
  • The developed classifier serves as a cost-effective decision aid for monitoring and predicting at-risk patients.
  • This approach supports public health initiatives aimed at improving medication adherence in psychiatric populations.