2011/12/07 by Casey Bennett, Thomas Doub · 1 citation
Computer Science · #cs.DB
published as Proceedings of the 6th International Conference on Data Mining. (2010) pp. 313-318 · Keywords: Data Mining; Decision Support Systems, Clinical; Electronic Health Records; Evidence-Based Medicine; Data Warehouse
arxiv created 2011/12/07 · arxiv updated 2011/12/08
Electronic health records (EHR's) are only a first step in capturing and utilizing health-related data - the problem is turning that data into useful information. Models produced via data mining and predictive analysis profile inherited risks and environmental/behavioral factors associated with patient disorders, which can be utilized to generate predictions about treatment outcomes. This can form the backbone of clinical decision support systems driven by live data based on the actual population. The advantage of such an approach based on the actual population is that it is "adaptive". Here, we evaluate the predictive capacity of a clinical EHR of a large mental healthcare provider (~75,000 distinct clients a year) to provide decision support information in a real-world clinical setting. Initial research has achieved a 70% success rate in predicting treatment outcomes using these methods.