Predicting Depression during the COVID-19 Pandemic Using Interpretable TabNet: A Case Study in South Korea

Explainable deep learning models for predicting water pipe failures

Failures within water distribution networks (WDNs) lead to significant environmental and economic impacts. While existing research has established various predictive models for pipe failures, there remains a lack of studies focusing on the probability of leaks and bursts. Addressing this gap, the present study introduces a new approach that harnesses deep learning algorithms - Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), and TabNet for failure prediction. The study enhances these base models by optimising their hyperparameters using Bayesian Optimisation (BO) and further refining the models through data scaling. The Copeland algorithm and SHapley Additive exPlanations (SHAP) are also applied for model ranking and interpretation, respectively. Applying this methodology to Hong Kong's WDN data, the study evaluates the models' predictive performance across several metrics, including accuracy, precision, recall, F1 score, Matthews Correlation Coefficient (MCC), and Cohen's Kappa. Results demonstrate that BO significantly enhances the models' predictive abilities, such that the TabNet model's F1 score for leak prediction increases by 36.2% on standardised data. The Copeland algorithm identifies CNN as the most effective model for predicting both leak and burst probabilities. As indicated by SHAP values, critical features influencing model predictions include pipe diameter, material, and age. The optimised CNN model has been deployed as user-friendly web applications for predicting the probability of leaks and bursts, enabling both single-pipe and batch predictions. This research provides crucial insights for WDN management, equipping water utilities with sophisticated tools to forecast the probability of pipe failure, enabling more effective mitigation of such failures.

Lifestyle factors and other predictors of common mental disorders in diagnostic machine learning studies: A systematic review

BACKGROUND

Machine Learning (ML) models have been used to predict common mental disorders (CMDs) and may provide insights into the key modifiable factors that can identify and predict CMD risk and be targeted through interventions. This systematic review aimed to synthesise evidence from ML studies predicting CMDs, evaluate their performance, and establish the potential benefit of incorporating lifestyle data in ML models alongside biological and/or demographic-environmental factors.

METHODS

This systematic review adheres to the PRISMA statement (Prospero CRD42023401194). Databases searched included MEDLINE, EMBASE, PsycInfo, IEEE Xplore, Engineering Village, Web of Science, and Scopus from database inception to 28/08/24. Included studies used ML methods with feature importance to predict CMDs in adults. Risk of bias (ROB) was assessed using PROBAST. Model performance metrics were compared. The ten most important variables reported by each study were assigned to broader categories to evaluate their frequency across studies.

RESULTS

117 studies were included (111 model development-only, 16 development and validation). Deep learning methods showed best accuracy for predicting CMD cases. Studies commonly incorporated features from multiple categories (n = 56), and frequently identified demographic-environmental predictors in their top ten most important variables (63/69 models). These tended to be in combination with psycho-social and biological variables (n = 15). Lifestyle data were infrequently examined as sole predictors of CMDs across included studies (4.27 %). Studies commonly had high heterogeneity and ROB ratings.

CONCLUSION

This review is the first to evaluate the utility of diagnostic ML for CMDs, assess their ROB, and evaluate predictor types. CMDs were able to be predicted, however studies had high ROB and lifestyle data were underutilised, precluding full identification of a robust predictor set.

Towards Improved XAI-Based Epidemiological Research into the Next Potential Pandemic

By applying AI techniques to a variety of pandemic-relevant data, artificial intelligence (AI) has substantially supported the control of the spread of the SARS-CoV-2 virus. Along with this, epidemiological machine learning studies of SARS-CoV-2 have been frequently published. While these models can be perceived as precise and policy-relevant to guide governments towards optimal containment policies, their black box nature can hamper building trust and relying confidently on the prescriptions proposed. This paper focuses on interpretable AI-based epidemiological models in the context of the recent SARS-CoV-2 pandemic. We systematically review existing studies, which jointly incorporate AI, SARS-CoV-2 epidemiology, and explainable AI approaches (XAI). First, we propose a conceptual framework by synthesizing the main methodological features of the existing AI pipelines of SARS-CoV-2. Upon the proposed conceptual framework and by analyzing the selected epidemiological studies, we reflect on current research gaps in epidemiological AI toolboxes and how to fill these gaps to generate enhanced policy support in the next potential pandemic.