Abstract
Maternal health outcomes are essential indicators of overall health care quality and
societal well-being. However, in the United States, the maternal health surveillance
is often inaccurate, restricting the clinical utility of the data gathered. The limits
imposed by these inaccuracies restrict timely policy responses and hinder effective
innovations, despite the increasing availability of electronic health records. This
paper explores the potential use of natural language processing in improving maternal
health surveillance. By combining rule-based linguistic processing with machine learning,
natural language processing can transform narrative text into structured, analyzable
data, allowing it to be used for predictive purposes, as well as the development of
real-time public health surveillance systems.
Key Points
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Maternal health surveillance is often inaccurate, restricting the clinical utility
of the data.
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Natural language processing can extract key insights from unstructured clinical notes.
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Artificial intelligence-driven surveillance in obstetrics may improve data accuracy
and timeliness.
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Ethical use of natural language processing needs to ensure privacy, bias control,
and validation.
Keywords
natural language processing - artificial intelligence - obstetrics - surveillance
