A new approach to medical diagnostic decision support

Data mining is a powerful tool to reduce costs and mitigate errors in the diagnostic analysis and repair of complex engineered system, but it has yet to be applied systematically to the most complex and socially expensive system ? the human body. The currently available approaches of knowledge-based and pattern-based artificial intelligence are unsuited to the iterative and often subjective nature of clinician-patient interactions. Furthermore, current electronic health records generally have poor design and low quality for such data mining. Bayesian methods have been developed to suggest multiple possible diagnoses given a set of clinical findings, but the larger problem is advising the physician on useful next steps. A new approach based on inverting Bayesian inference allows identification of the diagnostic actions that are most likely to disambiguate a differential diagnosis at each point in a patient?s work-up. This can be combined with personalized cost information to suggest a cost-effective path to the clinician. Because the software is tracking the clinician?s decision-making process, it can provide salient suggestions for both diagnoses and diagnostic tests in standard, coded formats that need only to be selected. This would reduce the need to type in free text, which is prone to ambiguities, omissions and errors. As the database of high-quality records grows, the scope, utility and acceptance of the system should also grow automatically, without requiring expert updating or correction.

Automated summary

Data mining is powerful for reducing costs and errors in complex engineered systems, but its application in healthcare, particularly for clinician-patient interactions, is still limited. Current AI methods are not well-suited for the iterative and subjective nature of these interactions. A new Bayesian-based approach can provide cost-effective diagnostic paths for clinicians by tracking their decision-making processes.