Drug side effect extraction from clinical narratives of psychiatry and psychology patients

Abstract

Objective: To extract physician-asserted drug side effects from electronic medical record clinical narratives.

Materials and methods: Pattern matching rules were manually developed through examining keywords and expression patterns of side effects to discover an individual side effect and causative drug relationship. A combination of machine learning (C4.5) using side effect keyword features and pattern matching rules was used to extract sentences that contain side effect and causative drug pairs, enabling the system to discover most side effect occurrences. Our system was implemented as a module within the clinical Text Analysis and Knowledge Extraction System.

Results: The system was tested in the domain of psychiatry and psychology. The rule-based system extracting side effects and causative drugs produced an F score of 0.80 (0.55 excluding allergy section). The hybrid system identifying side effect sentences had an F score of 0.75 (0.56 excluding allergy section) but covered more side effect and causative drug pairs than individual side effect extraction.

Discussion: The rule-based system was able to identify most side effects expressed by clear indication words. More sophisticated semantic processing is required to handle complex side effect descriptions in the narrative. We demonstrated that our system can be trained to identify sentences with complex side effect descriptions that can be submitted to a human expert for further abstraction.

Conclusion: Our system was able to extract most physician-asserted drug side effects. It can be used in either an automated mode for side effect extraction or semi-automated mode to identify side effect sentences that can significantly simplify abstraction by a human expert.

Figure 1

cTAKES (clinical Text Analysis and Knowledge Extraction System) UIMA (Unstructured Information Management Architecture) annotation flow of side effect pipeline.

Figure 2

Drug side effect annotation visualized through the UIMA's (Unstructured Information Management Architecture) CAS Visual Debugger. The right window shows a clinical narrative snippet processed to populate annotations as they appear in the left window. The bottom left window shows the list of side effects and causative drugs identified by the system and their attributes.