A natural language processing approach to detect inconsistencies in death investigation notes attributing suicide circumstances.

ABSTRACT:

Background

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 causing factors of death. Recent studies suggested the annotation inconsistencies within the NVDRS and the potential impact on erroneous suicide-circumstance attributions.

Methods

We present an empirical Natural Language Processing (NLP) approach to detect annotation inconsistencies and adopt a cross-validation-like paradigm to identify possible label errors. We analyzed 267,804 suicide death incidents between 2003 and 2020 from the NVDRS. We measured annotation inconsistency by the degree of changes in the F-1 score.

Results

Our results show that incorporating the target state's

SUBMITTER: Wang S 

PROVIDER: S-EPMC11471859 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature