Arrest Histories and Co-Occurring Mental Health and Substance Use Disorders Among Women in the USA

Zhao, Qianwei; Kong, Yinfei; Henderson, Domonique; Parrish, Danielle E.

doi:10.1007/s11469-023-01020-2

Cited by 2 publications

(1 citation statement)

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“…By leveraging these technological advancements, we aim to bridge the gap between data-driven innovation and clinical practice. SUDs are often inadequately addressed in women [31,32]. The insight of the study holds the potential to develop a tool for early identification of mental health and SUDs in women.…”

Section: Introductionmentioning

confidence: 99%

Predicting Co-Occurring Mental Health and Substance Use Disorders in Women: An Automated Machine Learning Approach

Acharya,

Kar,

Ally

et al. 2024

Applied Sciences

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Significant clinical overlap exists between mental health and substance use disorders, especially among women. The purpose of this research is to leverage an AutoML (Automated Machine Learning) interface to predict and distinguish co-occurring mental health (MH) and substance use disorders (SUD) among women. By employing various modeling algorithms for binary classification, including Random Forest, Gradient Boosted Trees, XGBoost, Extra Trees, SGD, Deep Neural Network, Single-Layer Perceptron, K Nearest Neighbors (grid), and a super learning model (constructed by combining the predictions of a Random Forest model and an XGBoost model), the research aims to provide healthcare practitioners with a powerful tool for earlier identification, intervention, and personalised support for women at risk. The present research presents a machine learning (ML) methodology for more accurately predicting the co-occurrence of mental health (MH) and substance use disorders (SUD) in women, utilising the Treatment Episode Data Set Admissions (TEDS-A) from the year 2020 (n = 497,175). A super learning model was constructed by combining the predictions of a Random Forest model and an XGBoost model. The model demonstrated promising predictive performance in predicting co-occurring MH and SUD in women with an AUC = 0.817, Accuracy = 0.751, Precision = 0.743, Recall = 0.926 and F1 Score = 0.825. The use of accurate prediction models can substantially facilitate the prompt identification and implementation of intervention strategies.

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Section: Introductionmentioning

confidence: 99%