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Intelligence series: Predictive Analytics for Optimised Rail Operations

Predictive analytics uses historic data to determine patterns and calculate the likelihood of future events. Many industries use this method to determine the risk of detrimental events and apply preventative measures. 

Since 2018, there have been developments in the uses of predictive analytics in the healthcare, aerospace, and oil and gas industries. For example, the Cambridge University Hospitals NHS Foundation Trust (CUH) has started to use predictive models to help make quick decisions, for example using predictive analytics to identify if a patient has sepsis. The model incorporates a support system that assists clinicians when making decisions about appropriate investigations and antibiotics to prescribe. In 2017, 55% of sepsis patients arriving in the emergency department were prescribed the correct medication within 90 minutes and in 2018, this number increased to 100%, demonstrating the value of this using this model. 
Research into different components of predictive analytics such as deep learning has resulted in a wider range of possible implementations for the rail industry. For example, Internet of Things (IoT)-based predictive analytics are used to ensure rail and rolling stock are in the correct condition for efficient use. 

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