Summary of Uncovering Misattributed Suicide Causes Through Annotation Inconsistency Detection in Death Investigation Notes, by Song Wang et al.
Uncovering Misattributed Suicide Causes through Annotation Inconsistency Detection in Death Investigation Notes
by Song Wang, Yiliang Zhou, Ziqiang Han, Cui Tao, Yunyu Xiao, Ying Ding, Joydeep Ghosh, Yifan Peng
First submitted to arxiv on: 28 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medium Difficulty summary: This paper presents a novel Natural Language Processing (NLP) approach to identify and correct annotation inconsistencies in the National Violent Death Reporting System (NVDRS) dataset. The NVDRS is widely used for studying suicide patterns and causes, but recent studies have highlighted the potential impact of these inconsistencies on erroneous suicide-cause attributions. The proposed NLP method uses a cross-validation-like paradigm to detect problematic instances and improves the F-1 score of suicide-crisis classifiers by up to 5.4% when incorporating target state data into training. The study analyzed 267,804 suicide death incidents between 2003 and 2020 and demonstrated the effectiveness of correcting these inconsistencies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This paper is about making sure that information in a big database called NVDRS is accurate. The NVDRS helps scientists and policymakers understand why people are dying from suicide. But some mistakes were found in the way the data was recorded, which could make it hard to understand what’s really going on. The researchers came up with a new way to find and fix these mistakes using computer programs. They looked at over 267,000 records of suicide deaths between 2003 and 2020 and showed that fixing the mistakes makes the data more accurate. |
Keywords
» Artificial intelligence » Natural language processing » Nlp
