This paper presents a data-driven approach to mitigate the effects of air pollution from industrial plants on nearby cities by linking operational decisions with weather conditions. Our method combines predictive and prescriptive machine learning models to forecast short-term wind speed and direction and recommend operational decisions to reduce or pause the industrial plant's production. We exhibit several trade-offs between reducing environmental impact and maintaining production activities. The predictive component of our framework employs various machine learning models, such as gradient-boosted tree-based models and ensemble methods, for time series forecasting. The prescriptive component utilizes interpretable optimal policy trees to propose multiple trade-offs, such as reducing dangerous emissions by 33-47% and unnecessary costs by 40-63%. Our deployed models significantly reduced forecasting errors, with a range of 38-52% for less than 12-hour lead time and 14-46% for 12 to 48-hour lead time compared to official weather forecasts. We have successfully implemented the predictive component at the OCP Safi site, which is Morocco's largest chemical industrial plant, and are currently in the process of deploying the prescriptive component. Our framework enables sustainable industrial development by eliminating the pollution-industrial activity trade-off through data-driven weather-based operational decisions, significantly enhancing factory optimization and sustainability. This modernizes factory planning and resource allocation while maintaining environmental compliance. The predictive component has boosted production efficiency, leading to cost savings and reduced environmental impact by minimizing air pollution.

Geneia's non-linear model accurately identifies patients' future costs at high-cost thresholds such as between $50,000 and $99,999 in health costs in the next 12 months. Our model requires less data to train, utilizes novel data sources and outperforms well-known commercial tools. The GDI Lab is working to determine whose cost can be most affected or, in other words, the impactable patients who are most likely to benefit from care management intervention. We know targeting only the riskiest patients means lost opportunities. For example, the chart below compares a risky patient with an impactable one, and shows the associated savings opportunity. High-cost and high-needs are not the same as highly impactable. As C. Annette DuBard, MD, MPH and Carlos T. Jackson, PhD, discussed in their paper, Active Redesign of a Medicaid Care Management Strategy for Greater Return on Investment: Predicting Impactability, "Targeting strategies that seek to identify patients based on high current or predicted costs or utilization are likely to identify large numbers of individuals whose healthcare needs will not be meaningfully altered by care management intervention." Their research with Community Care of North Carolina's Medicaid patient led to the creation of an impactability score.

Accurate prediction of healthcare costs is important for optimally managing health costs. However, methods leveraging the medical richness from data such as health insurance claims or electronic health records are missing. Here, we developed a deep neural network to predict future cost from health insurance claims records. We applied the deep network and a ridge regression model to a sample of 1.4 million German insurants to predict total one-year health care costs. Both methods were compared to Morbi-RSA models with various performance measures and were also used to predict patients with a change in costs and to identify relevant codes for this prediction. We showed that the neural network outperformed the ridge regression as well as all Morbi-RSA models for cost prediction. Further, the neural network was superior to ridge regression in predicting patients with cost change and identified more specific codes. In summary, we showed that our deep neural network can leverage the full complexity of the patient records and outperforms standard approaches. We suggest that the better performance is due to the ability to incorporate complex interactions in the model and that the model might also be used for predicting other health phenotypes.