Although machine learning methods have been used as an outcome prediction tool in many fields, their utilization in predicting incident outcome in occupational safety is relatively new. This study tests the performance of machine learning techniques in modeling and predicting occupational incidents severity with respect to accessible information of injured workers in agribusiness industries using workers’ compensation claims. More than 33,000 incidents within agribusiness industries in the Midwest of the United States for 2008–2016 were analyzed. The total cost of incidents was extracted and classified from workers’ compensation claims. Supervised machine learning algorithms for classification (support vector machines with linear, quadratic, and RBF kernels, Boosted Trees, and Naïve Bayes) were applied.

The initiative will combine EY's technology consulting experience and its agribusiness knowledge with Microsoft's digital suite of tools and the Microsoft Azure cloud platform to help companies innovate and transform their businesses. For example, EY and Microsoft will collaborate to develop solutions with a focus on data, analytics and artificial intelligence to support farmers and growers in increasing productivity, and help the industry digitize how crop data is aggregated, monitored and analyzed through e-commerce capabilities. This will help farmers increase efficiencies and competitive advantage using the data they collect on crop production, supply chain, machinery operation, and other business-critical data points. EY and Microsoft announced the initiative at Chicago Ideas Week, an annual festival in October that features more than 200 events spread across the city, during a session about data-driven farming and the role that technology will continue to play in increasing yields and field sustainability. EY and Microsoft are among the sponsors of the event.