System Architecture for Temporal Information Extraction, Representationand Reasoning in Clinical Narrative Reports

Exploring temporal information in narrative Electronic Medical Records (EMRs) is essential and challenging. We propose an architecture for an integrated approach to process temporal information in clinical narrative reports. The goal is to initiate and build a foundation that supports applications which assist healthcare practice and research by including the ability to determine the time of clinical events (e.g., past vs. present). Key components include: (1) an annotation schema for temporal expressions and the development of an associated tagger; (2) a natural language processing (NLP) system for encoding and extracting medical events and associating them with formalized temporal data; (3) a post-processor, with a knowledge-based subsystem to help discover implicit information, that resolves temporal expressions and deals with issues such as granularity and vagueness; and (4) a reasoning mechanism which models clinical reports as Simple Temporal Problems (STPs).