AI Article Synopsis
- The n2c2/UW SDOH Challenge focuses on improving natural language processing (NLP) techniques to extract social determinant of health (SDOH) information from clinical notes, presenting details on tasks, data, teams, and future considerations.
- The study used the Social History Annotated Corpus (SHAC) to categorize SDOH events, with participants tackling three subtasks through various advanced techniques, including pretrained language models.
- Results showed that pretrained models performed best, with the top team achieving high F1 scores and the analysis revealing varying extraction performance based on the type of SDOH, particularly struggling with high-risk factors like substance use and homelessness.
Article Abstract
Objective: The n2c2/UW SDOH Challenge explores the extraction of social determinant of health (SDOH) information from clinical notes. The objectives include the advancement of natural language processing (NLP) information extraction techniques for SDOH and clinical information more broadly. This article presents the shared task, data, participating teams, performance results, and considerations for future work.
Materials And Methods: The task used the Social History Annotated Corpus (SHAC), which consists of clinical text with detailed event-based annotations for SDOH events, such as alcohol, drug, tobacco, employment, and living situation. Each SDOH event is characterized through attributes related to status, extent, and temporality. The task includes 3 subtasks related to information extraction (Subtask A), generalizability (Subtask B), and learning transfer (Subtask C). In addressing this task, participants utilized a range of techniques, including rules, knowledge bases, n-grams, word embeddings, and pretrained language models (LM).
Results: A total of 15 teams participated, and the top teams utilized pretrained deep learning LM. The top team across all subtasks used a sequence-to-sequence approach achieving 0.901 F1 for Subtask A, 0.774 F1 Subtask B, and 0.889 F1 for Subtask C.
Conclusions: Similar to many NLP tasks and domains, pretrained LM yielded the best performance, including generalizability and learning transfer. An error analysis indicates extraction performance varies by SDOH, with lower performance achieved for conditions, like substance use and homelessness, which increase health risks (risk factors) and higher performance achieved for conditions, like substance abstinence and living with family, which reduce health risks (protective factors).
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10354764 | PMC |
| http://dx.doi.org/10.1093/jamia/ocad012 | DOI Listing |
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