Canada

We utilized the Behavioral Risk Factor Surveillance System (BRFSS) dataset for the following observational machine learning (ML) study. In contrast to recent literature using ML to predict specific outcomes, we used causal machine learning (CML) algorithms (LRSRegressors, DRLearner, Rlearner, Xregressor, and Tregressor) to quantify the cause-and-effect relationships between adverse childhood experiences (ACEs) and two different mental health targets (number of bad mental health days and whether the respondent had a depressive disorder). Specifically, we report each ACE’s average treatment effect (ATE) on each target. We also used Uplift Random Forest trees to calculate uplift scores. We found that growing up in a household where one parent had a depressive disorder and being forced into unwanted sex had the most significant effects on our chosen mental health targets. Growing up in a household where one parent had a depressive disorder increased the likelihood of being diagnosed with a depressive disorder by 30% and added 5.07 days of bad mental health in the last month. Repeatedly being forced to have sex added 5.77 days of poor mental health and increased the likelihood of being diagnosed with a depressive disorder by 33%. Uplift modeling indicated that individuals over 35 years of age earning under $100,000 USD annually were most susceptible to the effects of a parent diagnosed with depression by a factor of 1.1 compared to the average population. Male college graduates over 44 years of age earning under $100,000 USD were found to be most susceptible to the effects of childhood sexual abuse by a factor of 1.38. Future research should refine these models and employ more complicated algorithms to gain accurate, interpretable measurements and to understand the relationship between ACEs and mental health.

Leveraging Open Data Analytics and Machine Learning to Improve Mental Health Research and Innovation

Quantifying Childhood Trauma: Causal Machine Learning Approaches to Mental Health Outcomes

Truth and Responsibility
“Truth and Responsibility” is a call to reimagine anthropology to meet the demands of the present moment. The imperative to bear witness, take action, and be held accountable to the truths we write and circulate invites us to reflect on our responsibility in reckoning with disciplinary histories, harms, and possibilities. To whom are we giving evidence and toward what ends? For whom are we writing? To whom are we accountable, and in what ways? We are thrilled to announce the theme of the 2021 AAA Annual Meeting to be held in Baltimore, MD: Truth and Responsibility.

Many consider anthropology to be a holistic social science dedicated to cross-cultural understanding as a way to reduce bigotry and more fully comprehend “what it means to be human.” Others push against this readily circulated origin story, arguing that “human” remains a highly contested, political category—evident as the presumed set of rights and protections associated with the “human” continue to be stripped away violently in the communities in which some of us live and work. Which violent histories and epistemologies must anthropologists take responsibility for in telling truth(s) about the discipline’s development? How do the dual histories of settler colonialism and slavery continue to influence anthropological thought and practices? In these political times, how can anthropologists throughout the globe work to secure a capacious, progressive vision of the human? How might movements for anti-racism, decoloniality, queer liberation, and healing such as #BlackLivesMatter, #ProtectMaunaKea, and #MeToo push a future anthropology out of the ashes of the anthropological past? Finally, what are the limits and possibilities of the anthropological imagination?

Across subfields, we find truths in patterns of human behavior, language, evolution, and cultural worlds. Industry-positioned anthropologists do this by extending the boundaries of the discipline not as an administrative solution to job shortages but as a source of intervention and knowledge production. Increasingly, anthropologists who make use of the scientific method question how status and identity help to decide which facts come to represent truth. How might quantitative researchers use our methods to identify patterned truths, and what responsibility do we hold in challenging conventional wisdom about these patterns? What responsibility do we have to both our colleagues and larger publics, to push toward increased transparency and ethical grounding in our data collection, analysis, and presentation?

Pedagogy is a key medium for the communication of anthropological truths. Which (and whose) truths are foregrounded in our curricula? What are the possibilities for a liberatory pedagogy in a precarious, “post-truth” era? Conferences, museums, organizing spaces, and classrooms are sites where pedagogy and various forms of communication can help us interrogate the performativity of truth. How might anthropologists conceive of truth performances? Might visual, aural, and choreographic performances during AAA sessions offer anthropologists alternative genres for truth-telling and truth-making that provoke discussions about responsibility?

In accordance with the urgency of the year’s theme, the questions posed in this call for papers do not have easy answers. The hope is that those who submit proposals will take up these questions and engage in collective thinking and imagining about the truths we hold, the truths we challenge, and the responsibilities we bear in co-creating a free and more equitable world. All panel proposals should have a statement about how the panel has incorporated the goals of equity, diversity, and inclusion, and/or analysis of power.

Executive Session proposals have already been selected and speak directly to the theme “Truth and Responsibility.” To practice the critical reflexivity this call for papers invites, we asked that Executive Session submissions explicitly address the diversity of session participants, such as organizational/institutional affiliation, career stage, race, ethnicity, disability, sexuality, class, religion, national identity, and gender. Proposals for Executive Sessions that demonstrated a commitment to equity and inclusion and/or a clear analysis of power (i.e. anti-imperialist, anti-white supremacist, decolonial, anti-ableist, feminist, anti-transphobic, anti-xenophobic, etc) were given priority. We are excited to be in conversation with residents of the Baltimore region and look forward to the discussions, performances, and actions generated by the dynamic group of anthropologists attending the 2021 AAA Annual Meeting.

AAA Annual Meeting 2021

“Truth and Responsibility” is a call to reimagine anthropology to meet the demands of the present moment. The imperative to bear witness, take action, and be held accountable to the truths we write and circulate invites us to reflect on our responsibility in reckoning with disciplinary histories, harms, and possibilities. To whom are we giving evidence and toward what ends? For whom are we writing? To whom are we accountable, and in what ways? We are thrilled to announce the theme of the 2021 AAA Annual Meeting to be held in Baltimore, MD: Truth and Responsibility.

technical paper

The University Big Data Challenge (IUBDC) successfully concluded its latest edition, bringing together national health industry leaders, policymakers, and dignitaries for a conference centered on mental health research and innovation. Participants had the opportunity to showcase their projects in a poster session, engaging with representatives from industry and academia. The event provided a rich network of industry and academic professionals, offering pathways for scholarly publication, access to curated datasets, learning materials materials, and workshops.

Held at the Microsoft Headquarters in Toronto on July 26, 2024, the challenge featured hands-on data science and AI research projects focused on mental health. For over seven years, IUBDC has fostered collaboration between data science and health professionals and motivated university students. This year’s projects, which spanned topics from dementia to addictions, aimed to enhance our understanding of mental health, improve patient lifestyles, and reduce disease risk. Abstracts from teams were published in the NRC Research Press STEM Fellowship Journal, with all team's works featured by Underline publications.

2024 Inter-University Big Data And AI Challenge -

Thee challenge featured hands-on data science and AI research projects focused on mental health. 

The increasing prevalence of mental health disorders necessitates innovative approaches to early detection and intervention. Social media platforms like Reddit provide a rich source of textual data that can be leveraged to identify mental health issues based on user-generated content. This study aims to classify mental health disorders by analyzing Reddit comments. The dataset used is Reddit SuicideWatch and Mental Health Collection that includes 54,412 posts, which are classified into several mental health disorders. After that, preprocessing steps were done, which includes tokenization, padding, word embeddings, and handling imbalance data. Then, a Custom-CNN (Convolutional Neural Network) and a Custom- RNN (Recurrent Neural Network) were used to classify the data. These evaluation metrics were then used to evaluate the models: accuracy, precision, recall, F1-score, and ROC AUC Score. Notable results are that the RNN has a higher accuracy of 0.65 compared to 0.64 by CNN. In addition, both RNN and CNN also have high ROC AUC scores of 0.88 and 0.87 respectively. In conclusion, the results show that the models are good at differentiating one class from another, however, both models have problems accurately classifying the correct mental health disorder from the texts. This shows promise as the models can definitely be improved with better and more balanced data, and exploration on LLMs (Large Language Models) can also be valuable for this study and issue. In addition to that, those improvements can be applied to the act of intervention by integrating it to technology such as a conversational AI or wearable actuators that have the ability to predict and prevent through real-time monitoring.

Utilizing Deep Learning Techniques for Mental Disorder Prediction and Support Using Reddit Data

The creation and application of a machine learning model to categorise an individual’s degree of depression is presented in this research. The model predicts the depression state (no depression, mild depression, moderate depression, severe depression) based on other characteristics using the Depres- sion.csv dataset which comprises 16 features. Preprocessing the data, scaling the features, and applying strong machine learning methods—such as Support Vector Classifier (SVC), Random Forest, K-Nearest Neighbors, and Gradient Boosting—are all part of the research. To maximise the model’s performance, hyperparameter adjustment is also carried out. The model’s ability to accurately categorise depression levels is demonstrated by the results which may improve mental health support and monitoring.
Index Terms—Machine learning, support vector classifiers, random forests, gradient boosting, K-nearest neighbours, hyper- parameter tuning, mental health monitoring, depression prediction.			


Prediction of Level of Depression of Individuals  Using Machine Learning

Depression and suicidal thoughts are significant mental health concerns frequently expressed on social media platforms like Reddit. This study uses advanced machine learning techniques to analyze and classify posts from the Reddit dataset to identify and categorize high-risk content. By employing Latent Dirichlet Allocation (LDA) for topic modeling, we identify common themes in the posts, such as expressions of despair and cries for help [1]. We then apply clustering algorithms to group similar posts, uncovering underlying patterns and providing insights into frequent triggers and shared experiences [2]. We use classification models to accurately categorize posts and detect those indicating severe distress, including Logistic Regression, Support Vector Machines (SVM), and Neural Networks [3]. This methodology is implemented in a web application designed to provide real-time categorization of new posts, aiding mental health professionals and social media moderators in quickly identifying and responding to high-risk content. The application invites users to interact, processing their inputs via a text box. Visualization tools, such as 2D and 3D scatter plots, clearly display the data, allowing users to understand how posts are categorized and fit into broader trends [4]. This research improves our understanding of how mental health issues are expressed on social media, highlighting the importance of advanced data analytics in creating practical intervention tools. Our approach demonstrates the potential of combining psychological assessment with sophisticated computational methods to develop a comprehensive system that benefits research and individual users.

Advanced Machine Learning Techniques for Early Detection and Classification of Depression and Suicidal Tendencies on Social Media

Mental health issues are increasingly being addressed through machine learning techniques, which offer new possibilities for analyzing and understanding psychological conditions. This study focuses on detecting depression severity using advanced natural language processing (NLP) and sentiment analysis algorithms. Leveraging the capabilities of machine learning, we aim to provide a robust framework for assessing mental health by analyzing social media data. Our approach involves the use of three distinct models: EMOTION ENGLISH DISTILROBERTA-BASE for extracting emotional features, MULTIWD for assessing various dimensions of wellness, and Vader for sentiment analysis. These models generate a comprehensive feature set, which is then used to compute depression severity, categorized into five levels—Minimal, Mild, Moderate, Moderately Severe, and Severe—based on the PHQ-9 questionnaire. To evaluate the effectiveness of our approach, we compared the calculated severity classes with results from Latent Dirichlet Allocation (LDA) for topic modeling and K-Means clustering for numerical feature-based classification. Our findings indicate that K-Means clustering achieved a 76% accuracy in aligning severity classes, which surpasses the LDA accuracy 63%. This suggests that numerical features may offer more reliable clustering for mental health severity than topic-based methods. This study highlights the potential of combining NLP and sentiment analysis techniques in mental health assessments and suggests directions for further refinement and research.


Model Based Severity Analysis of Mental Health Issues Using NLP and Sentiment Analysis On Social Media Data (SENTI-SEV)

Depression is popularly known as major depressive disorder (MDD), which is a common but serious medical sickness that negatively affects how an individual feels, the way they think, and possibly how they act. It potentially leads to many emotional and physical breakdowns and can reduce a person’s capability to perform at work as well as at home. The project employs a robust methodology to analyze the relationships between depression and various risk factors using the National Health and Nutrition Examination Survey (NHANES) dataset. The project involves data acquisition and preprocessing, depression score calculation using the PHQ-9 questionnaire, and technical infrastructure setup. Data aggregation, preprocessing, and exploratory data analysis (EDA) are performed using Python and its libraries. Hypotheses are formulated and tested using statistical methods, and machine learning techniques are applied for predictive modeling. Feature importance and hyperparameter tuning are used to improve the models.The project identifies key factors associated with depression, including socioeconomic features, alcohol and drug consumption, and mental health measures. Predictive models are being developed to predict depression levels based on multiple variables. A web interface with interactive dashboards is being designed to visualize key insights and model predictions. The findings have the potential to inform and shape public health policies and interventions. Also, successfully identifying and analyzing these complex associations will provide better insights into the whole picture of mental health treatment, particularly depression, which ultimately improves the effectiveness of public health strategies and clinical practices. Every finding in this research project might be beneficial not only to patients with depression but also to the academic community and public health policymakers.

Deciphering Depression: A Multivariate Analysis of Influential Factors and Their Relationships

Depression is a widespread mental health disorder affecting millions worldwide. In the age of social media, it is crucial to leverage technology to detect, help, and support individuals displaying symptoms online. Our investigation revealed that social media, particularly Reddit, has a wide demographic of people and hosts a variety of discussion topics, including a significant number of posts on mental health. Using the large amounts of data on Reddit, the goal is to train and develop various machine learning models to recognize manifestations of depression and evaluating their performance. These models include machine learning and deep learning methods, such as Support Vector Machines (SVMs), transformers, and various Recurrent Neural Network (RNN) architectures — simple RNNs, Gated Recurrent Units (GRUs) and Long Short-Term Memory (LSTM) units — for sentiment analysis and depression detection on Reddit. The objective is to evaluate and enhance the models’ accuracy. Early detection facilitated by such models could potentially mitigate the impact of mental health disorders through timely intervention and support mechanisms.

Using Machine Learning and Deep Learning for Depression Detection on Social Media

Anxiety disorder (AD) is a condition associated with feelings of dread and an incapability to perform daily tasks. Symptoms can be physical, such as a rapid pulse and sweating, and emotional, including constant stress and difficulty concentrating. Anxiety disorders are subjective, variable and often overlap with underlying conditions, complicating detection and treatment. This study focuses on three factors, implicit memory, reaction time, and reaction to a perceived threat—all measured in previous studies and correlated with anxiety levels. Our goal is to determine if anxiety presence can be identified, using three games and one simulation. The first game centers around implicit memory, enabling us to perform tasks subconsciously. Anxious individuals exhibit an implicit memory bias for threat-related information. The second game will associate a level of anxiety to reaction times. The third game first makes the user complete a regular task and progressively introduces a threat to the successful completion of the task. Attentional bias shows that anxious people are more likely to respond to threat-related stimuli rather than stick to their task. Finally, the user will take part in an artificial anxious situation. In the first stage, the user plays a regular game. In the second, distracting stimuli appear. In the third, the user’s available resources decrease. Throughout the stages, both the user’s effectiveness and efficiency will be evaluated. If the user experiences anxiety, efficiency should decrease in stage 2 and effectiveness should decrease in stage 3. Based on their results, they will receive an assessment of their optimal stress level for highest performance. The results from all of three games will be compiled to provide an anxiety assessment. The player will then be prompted to complete the Hamilton Anxiety Rating Scale (HAM-A) which will be used to verify whether the game results match their true anxiety levels.


Using Interactive Games as Tools for Gauging Anxiety

Impostor syndrome is defined as a combination of self-perceived fraudulence, achievement pressure, and negative emotions [3], which creates doubts in one's capacity, leading to unfavorable comparisons with others. Our study represents an approach to evaluate individuals based on a set of questions from the Young Impostor Syndrome (YIS) scale [4] built within a web application. These questions assist users in identifying impostor syndrome symptoms, such as self-doubt, and use Likert-scale responses ranging from ‘agree’ to ‘disagree’ to calculate a more accurate impostor syndrome metric. Individuals are encouraged to provide personal information such as age and years of experience in the field, enabling users to attribute their accomplishments to their own ability. Furthermore, our initial data, from a dataset predicting impostor syndrome in medical students [2], is processed as the basis for our analysis. We then expand this data with online user responses, using a cloud solution to store their inputs. The data is then presented as clusters to the user so they can observe how many other individuals are dealing with similar kinds of issues. This enables the investigation of relationships between impostor syndrome traits and demographics. We use clustering algorithms [1] to place users into groups according to their profiles. Users can see where those clusters fit in relation to others by viewing them in 2D and 3D scatter plots. Visualizations help to better understand the frequency and the variability of this mental health phenomenon among demographic groups, in addition to quantifying it. This study adds to our understanding of the intricacy and effects of impostor syndrome. Users’ experience can be contextualized by the visual representation, which promotes awareness and debate of the psychological phenomenon. Our method emphasizes how sophisticated data analytics and psychological testing may be used to create a comprehensive tool that helps research and individuals. Keywords: impostor syndrome, data analysis, clustering algorithms, visualization, web application, mental health, Likert scale

Imposter Syndrome Comparison Map

With the advent of machine learning becoming more and more integrated into healthcare, this study explores the efficacy of machine learning models in classifying and predicting various brain disorders and diseases such as schizophrenia, Alzheimer’s disease and Parkinson’s disease. Our review highlights the potential that ML has in identifying effective biomarkers, with cortical thickness emerging as a more accurate indicator for schizophrenia. Additionally, the connection between Alzheimer's disease and metabolic syndrome was made much more clear through ML algorithms, identifying eight diagnostic genes with high AUC values. The versatility of ML is further demonstrated through the application of natural language processing to analyze social media data, uncovering mental health issues in patients with diabetes. The challenges associated with ML use in a healthcare setting were also analyzed; the performance of ML models can drop substantially when encountering new data significantly different from the training set. While ML has great promise, it is recommended to use as a supplementary tool in collaboration with clinicians for the greatest efficacy. Continuous improvement in algorithms, data quality, and model validation will be essential to fully realize the potential of ML in improving patient care and outcomes.

Machine Learning in Neurological Disorders: A Comprehensive Review of Diagnostic and Predictive Applications

Stress affects over 79% of individuals globally, presenting a significant challenge for timely and accurate identification within mental health care. Traditional methods, which rely on self-reported data and sporadic clinical assessments, often result in delayed diagnosis and inadequate stress management. To address these challenges, this project employs artificial intelligence (AI) to develop a real-time stress level detection model using unsupervised learning algorithms. By integrating data from wearable devices, such as Apple Watches, with self-reported mood and activity logs, our model provides continuous and comprehensive stress analysis. Achieving an accuracy of 60% upon validation, the model demonstrates a significant step toward more effective stress management. This AI-driven approach shows promise in enabling early stress detection and personalized interventions. The findings underscore the potential for AI to enhance stress monitoring accuracy, thereby contributing to improved individual well-being and broader accessibility to quality mental health care. Future research will focus on refining the model to improve its accuracy and exploring additional data sources to further enhance its predictive capabilities.

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