###### Abstract  
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. mental health; substance use disorder; machine learning; AutoML +
Footnote †: journal:  
Machine learning (ML) has emerged as a promising tool for understanding and addressing these challenges. Previous studies have explored its application in identifying predictors for suicide, treatment success, and more. [PERSON], et al. [11] aimed to investigate disparities in substance use disorder treatment completion in the U.S. using 2017-2019 data from TEDS-D by SAMHSA. Employing a two-stage virtual twins model (random forest + decision tree), the research identified factors influencing completion probability (e.g., race/ethnicity, income source), revealing that those without co-occurring mental health conditions, with job-related income, and white non-Hispanics are more likely to complete treatment. [PERSON], et al. [12] employed deep learning and natural language processing to develop DeepBiomarker2 that accurately predicts alcohol and substance use disorder risk in post-traumatic stress disorder patients and identifies medications and social determinants of health parameters that may reduce this risk. [PERSON], et al. [13] performed a study in Denmark that focused on individuals with substance use disorders (SUDs) and their elevated suicide risk. Using machine learning, the analysis identified key predictors for suicide in men and women with SUDs, highlighting specific factors such as antidepressant use, poisoning diagnoses, age, and comorbid psychiatric disorders. The findings suggest that individuals with prior incidents of poisoning and mental health disorders, especially women, are at increased risk of suicide among those with substance use disorders in Denmark. [PERSON], et al. [14] investigated the use of machine learning, including AutoML and ensemble classifiers, to predict potential cardiovascular diseases by analysing real-time IoT-based healthcare data, highlighting improved accuracy and efficiency in data analytics for healthcare devices. [PERSON], et al. [15] explored the application of machine learning (ML) in investigating mental health and substance use concerns within the LGBTQ2S+ population. Examining 11 recent studies, the findings suggested ML as a promising tool. A lack of studies evaluating substance use treatments in women with severe mental illness who differ in their needs and capacity has been noted [16], there are opportunities to explore the potential application to research in this field of Automated Machine Learning (AutoML) interfaces.