Rather than predicting suicide risk, we decided to stratify risk. Our contention was that predicting the riskiest patients would be valuable. We departed from using routinely collected risk assessment data and used Electronic Medical records instead. We solved the problem using novel feature engineering and ordinal classification. Using a large cohort of patients we were able to show that our predictions were at least twice as good as clinicians. We also presented this work at the top data mining conference, The 19th ACM SIGKDD (The Association for Computing Machinery’s Special Interest Group on Knowledge Discovery and Data Mining) International Conference in 2013.