@incollection{DANESHVAR2024113,
    title = {Chapter 9 - SOK: Application of machine learning models in child and youth mental health decision-making},
    editor = {Shai Ben-David and Giuseppe Curigliano and David Koff and Barbara Alicja Jereczek-Fossa and Davide {La Torre} and Gabriella Pravettoni},
    booktitle = {Artificial Intelligence for Medicine},
    publisher = {Academic Press},
    pages = {113-132},
    year = {2024},
    series = {Advanced Studies in Complex Systems: Theory and Applications},
    isbn = {978-0-443-13671-9},
    doi = {https://doi.org/10.1016/B978-0-443-13671-9.00003-X},
    url = {https://www.sciencedirect.com/science/article/pii/B978044313671900003X},
    author = {Hirad Daneshvar and Omar Boursalie and Reza Samavi and Thomas E. Doyle and Laura Duncan and Paulo Pires and Roberto Sassi},
    keywords = {Decision-making, Artificial intelligence, Machine learning, Mental health, Literature review, Child and youth mental health},
    abstract = {Artificial intelligence (AI), specifically machine learning, has the potential to augment human decision-making in mental healthcare to improve clinical outcomes. An important challenge is the knowledge gap between AI designers and mental health professionals. We review AI researchers’ and health professionals’ related terminologies, concepts, and perspectives to facilitate interdisciplinary collaborations in AI-assisted decision-making research. We present a case study to understand the decision-making processes at McMaster Children's Hospital's Child and Youth Mental Health Outpatient Services Program. We identified three decision points that can be augmented using AI: diagnosis, prognosis, and assessment. We present a systematic literature review of forty publications using the PRISMA methodology to investigate AI in mental health. Most publications focused on developing classification and prediction models outside the clinical workflow. We report open research challenges investigating how health professionals’ decision-making changes to AI, where AI can assist decision-making, and how the human–AI feedback loop informs model improvement.}
}

@article{Saggu2024,
    author={Saggu, Simran
    and Daneshvar, Hirad
    and Samavi, Reza
    and Pires, Paulo
    and Sassi, Roberto B.
    and Doyle, Thomas E.
    and Zhao, Judy
    and Mauluddin, Ahmad
    and Duncan, Laura},
    title={Prediction of emergency department revisits among child and youth mental health outpatients using deep learning techniques},
    journal={BMC Medical Informatics and Decision Making},
    year={2024},
    month={Feb},
    day={08},
    volume={24},
    number={1},
    pages={42},
    abstract={The proportion of Canadian youth seeking mental health support from an emergency department (ED) has risen in recent years. As EDs typically address urgent mental health crises, revisiting an ED may represent unmet mental health needs. Accurate ED revisit prediction could aid early intervention and ensure efficient healthcare resource allocation. We examine the potential increased accuracy and performance of graph neural network (GNN) machine learning models compared to recurrent neural network (RNN), and baseline conventional machine learning and regression models for predicting ED revisit in electronic health record (EHR) data.},
    issn={1472-6947},
    doi={10.1186/s12911-024-02450-1},
    url={https://doi.org/10.1186/s12911-024-02450-1}
}       

@article{Daneshvar2022Heterogeneous,
    author = {Daneshvar, Hirad and Samavi, Reza},
    journal = {Proceedings of the Canadian Conference on Artificial Intelligence},
    year = {2022},
    month = {may 27},
    note = {https://caiac.pubpub.org/pub/13qyxg9q},
    publisher = {Canadian Artificial Intelligence Association (CAIAC)},
    title = {Heterogeneous {Patient} {Graph} {Embedding} in {Readmission} {Prediction}},
}   

@article{https://doi.org/10.1002/cpe.7015,
    author = {Daneshvar, Hirad and Ravanmehr, Reza},
    title = {A social hybrid recommendation system using LSTM and CNN},
    journal = {Concurrency and Computation: Practice and Experience},
    volume = {34},
    number = {18},
    pages = {e7015},
    keywords = {convolutional neural network, long short-term memory network, movie recommender system, movie visual features, social recommender system},
    doi = {https://doi.org/10.1002/cpe.7015},
    url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/cpe.7015},
    eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/cpe.7015},
    abstract = {Abstract With the ever-increasing use of Internet and social networks that generate a vast amount of information, there is a serious need for recommendation systems. In this article, we propose a recommender system utilizing deep neural networks that simultaneously considers both the users' ratings to the movies and the visual features of the movie poster and trailer. For this purpose, a hybrid movie recommender system, RSLCNet, has been developed using CNN and LSTM architectures. The proposed system considers the dynamics of users' interests in the collaborative filtering engine using the LSTM network that receives user-rating sequences. In the content-based filtering engine, utilizing CNN, the visual features of movie posters and trailers are extracted, and along with the actors and the directors, similar movies are recommended to the user. Moreover, each user's social influence is calculated employing the social information available on the user's Twitter account and used in the average movie rating to improve the effectiveness of the content-based filtering part. The required datasets have been collected from MovieTweetings, Mise-en-scène, and OMDB. The evaluation results show that the accuracy and effectiveness of the proposed approach have been improved in terms of MAE and RMSE compared to the best available methods.},
    year = {2022}
}