A privacy-preserving framework for distributed clinical decision support

摘要

We propose a framework for distributed knowledge-mining that results in a useful clinical decision support tool in the form of a decision tree. This framework facilitates knowledge building using statistics based on patient data from multiple sites that satisfy a certain filtering condition, without the need for actual data to leave the participating sites. Our information retrieval and diagnostics supporting tool accommodates heterogeneous data schemas associated with participating sites. It also supports prevention of personally identifiable information leakage and preservation of privacy, which are important security concerns in management of clinical data transactions. Results of experiments conducted on 8 and 16 sites with a small number of patients per site (if any) satisfying specific partial diagnostics criteria are presented. The experiments coupled with restricting a fraction of attributes from sharing statistics as well as applying different constraints on privacy at various sites demonstrate the usefulness of the tool.