Measuring agreement in medical informatics reliability studies
Journal of Biomedical Informatics
Journal of Biomedical Informatics - Special issue: Unified medical language system
MPLUS: a probabilistic medical language understanding system
BioMed '02 Proceedings of the ACL-02 workshop on Natural language processing in the biomedical domain - Volume 3
Journal of Biomedical Informatics
A statistical methodology for analyzing co-occurrence data from a large sample
Journal of Biomedical Informatics
A shared task involving multi-label classification of clinical free text
BioNLP '07 Proceedings of the Workshop on BioNLP 2007: Biological, Translational, and Clinical Language Processing
Journal of Biomedical Informatics
Section classification in clinical notes using supervised hidden markov model
Proceedings of the 1st ACM International Health Informatics Symposium
Building an automated SOAP classifier for emergency department reports
Journal of Biomedical Informatics
Data mining methodologies for pharmacovigilance
ACM SIGKDD Explorations Newsletter
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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.