Classification based extraction of numeric values from clinical narratives

Abstract

The robust extraction of numeric values from clinical narratives is a well known problem in clinical data warehouses. In this paper we describe a dynamic and domain-independent approach to deliver numerical described values from clinical narratives. In contrast to alternative systems, we neither use manual defined rules nor any kind of ontologies or nomenclatures. Instead we propose a topic-based system, that tackles the information extraction as a text classification problem. Hence we use machine learning to identify the crucial context features of a topicspecific numeric value by a given set of example sentences, so that the manual effort reduces to the selection of appropriate sample sentences. We describe context features of a certain numeric value by term frequency vectors which are generated by multiple document segmentation procedures. Due to this simultaneous segmentation approaches, there can be more than one context vector for a numeric value. In those cases, we choose the context vector with the highest classification confidence and suppress the rest.To test our approach, we used a dataset from a german hospital containing 12 743 narrative reports about laboratory results of Leukemia patients. We used Support Vector Machines (SVM) for classification and achieved an average accuracy of 96% on a manually labeled subset of 2073 documents, using 10-fold cross validation. This is a significant improvement over an alternative rule based system.

The Tools and Resources for Clinical Text Processing

“…Bulgarian clinical texts 100 000 000 [17] Bulgarian outpatient records (diabetic) 500 000 [18] Serbian medical records (B05) 5000 [19] Serbian medical reports 4212 [20] Serbian Italian clinical texts 100 [28] German clinical texts 18 000 [29] German leukemia laboratory results 12 743 [30] German nephrology records 6 817 [31] German discharge reports 118 [31] Chinese medical documents 1100 [32] Table 1 -Non-English corpora…”

Clinical Text Mining

“…• A German clinical corpus from Austria, containing 18,000 patient records from eight different clinical units (surgery, vascular surgery, casualty surgery, internal medicine, neurology, anesthesia and intensive care, radiology and physiotherapy) which has been used for document classification (Spat et al 2008). • Another German corpus of 12,743 clinical narratives describing laboratory results of leukaemia (Zubke 2017). • Yet another German corpus of 6817 clinical notes and 118 discharge summaries in nephrology (Roller et al 2016).…”