A new algorithmic approach for the extraction of temporal associations from clinical narratives with an application to medical product safety surveillance reports

The sheer volume of textual information that needs to be reviewed and analyzed in many clinical settings requires the automated retrieval of key clinical and temporal information. The existing natural language processing systems are often challenged by the low quality of clinical texts and do not demonstrate the required performance. In this study, we focus on medical product safety report narratives and investigate the association of the clinical events with appropriate time information. We developed a novel algorithm for tagging and extracting temporal information from the narratives, and associating it with related events. The proposed algorithm minimizes the performance dependency on text quality by relying only on shallow syntactic information and primitive properties of the extracted event and time entities. We demonstrated the effectiveness of the proposed algorithm by evaluating its tagging and time assignment capabilities on 140 randomly selected reports from the US Vaccine Adverse Event Reporting System (VAERS) and the FDA (Food and Drug Administration) Adverse Event Reporting System (FAERS). We compared the performance of our tagger with the SUTime and HeidelTime taggers, and our algorithm's event-time associations with the Temporal Awareness and Reasoning Systems for Question Interpretation (TARSQI). We further evaluated the ability of our algorithm to correctly identify the time information for the events in the 2012 Informatics for Integrating Biology and the Bedside (i2b2) Challenge corpus. For the time tagging task, our algorithm performed better than the SUTime and the HeidelTime taggers (F-measure in VAERS and FAERS: Our algorithm: 0.86 and 0.88, SUTime: 0.77 and 0.74, and HeidelTime 0.75 and 0.42, respectively). In the event-time association task, our algorithm assigned an inappropriate timestamp for 25% of the events, while the TARSQI toolkit demonstrated a considerably lower performance, assigning inappropriate timestamps in 61.5% of the same events. Our algorithm also supported the correct calculation of 69% of the event relations to the section time in the i2b2 testing set.