MiNerDoc: a Semantically Enriched Text Mining System to Transform Clinical Text into Knowledge

Abstract

Existing systems to support the daily decisiontaking process carried out by health professionals need to be used independently to perform different text mining subtasks. In practice, there are few systems that unify all the subtasks into an unique framework, easing therefore the clinical work by automating complex clinical tasks such as the detection of clinical alerts as well as clinical information coding. In this sense, the MiNerDoc system is proposed, whose main objective is to support clinical decision-taking process by analysing tons of textual clinical reports in an unified framework. MiNerDoc performs two basic functions that are of great importance in the medical field: detection of risk factors based on the recognition of five medical entities (Disease, Pharmacologic, Region/Part Body, Procedure/Test, Finding/Sign), and automatic prediction of standardized diagnostic codes (MeSH descriptors). A major feature of MiNerDoc is it includes external knowledge sources such as MetaMap and UMLS to terminologically and semantically enrich the interpretation of clinical texts. Some study cases are considered in this work to demonstrate the power of MiNerDoc.