Clinical and financial outcomes analysis with existing hospital patient records

Existing patient records are a valuable resource for automated outcomes analysis and knowledge discovery. However, key clinical data in these records is typically recorded in unstructured form as free text and images, and most structured clinical information is poorly organized. Time-consuming interpretation and analysis is required to convert these records into structured clinical data. Thus, only a tiny fraction of this resource is utilized. We present REMIND, a Bayesian Framework for Reliable Extraction and Meaningful Inference from Nonstructured Data. REMIND integrates and blends the structured and unstructured clinical data in patient records to automatically created highquality structured clinical data. This structuring allows existing patient records to be mined for quality assurance, regulatory compliance, and to relate financial and clinical factors. We demonstrate REMIND on two medical applications: (a) Extract "recurrence", the key outcome for measuring treatment ...

• Type: Conference
• Year: 2003
• Where: KDD
• Authors: R. Bharat Rao, Sathyakama Sandilya, Radu Stefan Niculescu, Colin Germond, Harsha Rao
• Keywords: Data Mining, KDD 2003, Key Clinical Data, Structured Clinical Data, Unstructured Clinical Data