Extraction Of Adverse Events From Clinical Documents To Support Decision Making Using Semantic Preprocessing.
Clinical documentation is usually stored in unstructured format in electronic health records (EHR). Processing the information is inconvenient and time consuming and should be enhanced by computer systems. In this paper, a rule-based method is introduced that identifies adverse events documented in the EHR that occurred during treatment. For this purpose, clinical documents are transformed into a semantic structure from which adverse events are extracted. The method is evaluated in a user study with neurosurgeons. In comparison to a bag of word classification using support vector machines, our approach achieved comparably good results of 65% recall and 78% precision. In conclusion, the rule-based method generates promising results that can support physicians' decision making. Because of the structured format the data can be reused for other purposes as well.
Innovation Center Computer Assisted Surgery, Leipzig, Germany.
Jan
J
Gaebel
ID Information und Dokumentation im Gesundheitswesen, Berlin, Germany.
Till
T
Kolter
Clinic for Neurosurgery, University Hospital Leipzig, Leipzig, Germany.
Felix
F
Arlt
Innovation Center Computer Assisted Surgery, Leipzig, Germany.
Kerstin
K
Denecke
D016907:Adverse Drug Reaction Reporting Systems / Q000458:organization & administration*; D057225:Data Mining / Q000379:methods*; D020000:Decision Support Systems, Clinical / Q000458:organization & administration*; D064420:Drug-Related Side Effects and Adverse Reactions / Q000175:diagnosis*; D057286:Electronic Health Records / Q000145:classification*; D005858:Germany; D006801:Humans; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012660:Semantics; D012680:Sensitivity and Specificity; D060388:Support Vector Machine; D018875:Vocabulary, Controlled