Selecting information in electronic health records for knowledge acquisition
Knowledge acquisition of relations between biomedical entities is critical for many automated biomedical applications, including pharmacovigilance and decision support. Automated acquisition of statistical associations from biomedical and clinical documents has shown some promise. However, acquisition of clinically meaningful relations (i.e. specific associations) remains challenging because textual information is noisy and co-occurrence does not typically determine specific relations. In this work, we focus on acquisition of two types of relations from clinical reports: disease-manifestation related symptom (MRS) and drug-adverse drug event (ADE), and explore the use of filtering by sections of the reports to improve performance. Evaluation indicated that applying the filters improved recall (disease-MRS: from 0.85 to 0.90; drug-ADE: from 0.43 to 0.75) and precision (disease-MRS: from 0.82 to 0.92; drug-ADE: from 0.16 to 0.31). This preliminary study demonstrates that selecting information in narrative electronic reports based on the sections improves the detection of disease-MRS and drug-ADE types of relations. Further investigation of complementary methods, such as more sophisticated statistical methods, more complex temporal models and use of information from other knowledge sources, is needed.
knowledge acquisition pharmacovigilance decision support natural language processing (nlp) text mining electronic health record (ehr)