Publications by authors named "C J Gilman"
Article Synopsis
- Ground-glass opacities (GGOs) on CT scans may signal lung cancer, and leveraging electronic health records filled with unstructured notes can aid in managing these nodules effectively.
- Researchers developed an advanced deep learning natural language processing (NLP) tool to extract detailed GGO features from radiology notes of over 13,000 lung cancer patients, achieving high levels of precision and recall in their analysis.
- The longitudinal study of GGO status showed that about 16.8% of patients experienced increased size of GGOs, while 72.3% had stable conditions, indicating the tool's efficacy in monitoring and analyzing GGO progression over time.
The consistent and persuasive evidence illustrating the influence of social determinants on health has prompted a growing realization throughout the health care sector that enhancing health and health equity will likely depend, at least to some extent, on addressing detrimental social determinants. However, detailed social determinants of health (SDoH) information is often buried within clinical narrative text in electronic health records (EHRs), necessitating natural language processing (NLP) methods to automatically extract these details. Most current NLP efforts for SDoH extraction have been limited, investigating on limited types of SDoH elements, deriving data from a single institution, focusing on specific patient cohorts or note types, with reduced focus on generalizability.
View Article and Find Full Text PDFA 15-year-old female presented with headaches and bilateral vision loss. Fundoscopic examination revealed bilateral optic nerve oedema as well as peripheral retinal haemorrhages. Magnetic resonance imaging of the brain showed findings consistent with bilateral optic neuritis.
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