Environmental-Health Convergence: A deep learning-oriented decision support system for catalyzing sustainable healthy food systems

To generate evidence to address food system challenges, we developed an adaptable framework for multimodel assessment of the convergence effect of health and environmental drivers in food systems. We achieved this goal by developing a modeling framework that facilitates testing and applying four deep-learning algorithms using a case study of the United States's food system. Among the models tested, the bidirectional and single-layer long short-term memory models outperformed the others with α E (2.75) and α H (3.51) when predicting environmental drivers and health drivers, respectively. All the models tested performed better at predicting environmental than health drivers. The best-performing model for each dimension was deployed into the Food System Rapid Overview Assessment through Scenarios (FS-ROAS) tool. As we approach the endpoint of the transformative 2030 agenda, FS-ROAS can be a timely toolkit that enables stakeholders to explore diverse intervention scenarios in the context of short-medium and long-term goals for future food systems and generate evidence to guide future actions. • We built a framework to test and apply deep learning models for multioutput predictions. • The LSTM model outperformed all other models when predicting health indicators. • The BiLSTM model outperformed all other models when predicting environmental indicators. • The LSTM and BiLSTM models were deployed into a decision support system (DSS). • The DSS generates evidence to guide public health and climate mitigation strategies.