Document Type
Conference Presentation
Embargo Period
10-11-2023
Publication Date
Fall 9-2022
College
College of Graduate Studies
Abstract
This talk will demonstrate how NLP combined with a causal analysis approach can be used to predict and understand cancer screening uptake. Screening recommendations for breast, lung and colorectal cancer put together by the U.S. Preventive Services Task Force (USPSTF) are based on reductions in relative risk of mortality by detecting cancers at early stages where more treatment options are available. However, not all those recommended to get screened are being screened, and there are many social and economic barriers to increasing uptake. To maximize the utility of electronic health record (EHR), data in free text problem lists, as well as patient reported family histories of cancer must be extracted. Results from Stanza from (Standford NLP group) can be used with casual analysis approaches to discover underlying variable network structures, and treatment effects. However, named entity recognition (NER) alone is not quite a turnkey solution for concept extraction used in structure learning, and many challenges remain.
Recommended Citation
Utilizing NLP for Structure Learning to Understand Cancer Screening Uptake Utilizing NLP for Structure Learning to Understand Cancer Screening Uptake. NLP Summit. Accessed https://www.nlpsummit.org/utilizing-nlp-for-structure-learning-to-understand-cancer-screening-uptake/
Description
Presentation at the Spark NLP Conference Fall 2022