Uncovering Misattributed Suicide Causes through Annotation Inconsistency Detection in Death Investigation Notes
This work addresses data accuracy issues in suicide death reporting for researchers and policymakers, but it is incremental as it builds on known annotation problems with a specific NLP solution.
The paper tackled annotation inconsistencies in the National Violent Death Reporting System (NVDRS) data, which can lead to misattributed suicide causes, by developing an NLP approach that improved the F-1 score by 5.4% on a target state's test set while slightly decreasing performance on other states.
Data accuracy is essential for scientific research and policy development. The National Violent Death Reporting System (NVDRS) data is widely used for discovering the patterns and causes of death. Recent studies suggested the annotation inconsistencies within the NVDRS and the potential impact on erroneous suicide-cause attributions. We present an empirical Natural Language Processing (NLP) approach to detect annotation inconsistencies and adopt a cross-validation-like paradigm to identify problematic instances. We analyzed 267,804 suicide death incidents between 2003 and 2020 from the NVDRS. Our results showed that incorporating the target state's data into training the suicide-crisis classifier brought an increase of 5.4% to the F-1 score on the target state's test set and a decrease of 1.1% on other states' test set. To conclude, we demonstrated the annotation inconsistencies in NVDRS's death investigation notes, identified problematic instances, evaluated the effectiveness of correcting problematic instances, and eventually proposed an NLP improvement solution.