A recent study addresses the need for unsupervised medical concept annotation to overcome the limited availability of annotated clinical data due to sensitivity and time constraints. The study, which used an unsupervised SapBERT-based bi-encoder model to analyze n-grams from narrative text and measure their similarity to #SNOMEDCT concepts, concluded that the approach demonstrates potential for initial annotation (pre-labeling) in manual annotation tasks. While promising for certain semantic tags, the study says, challenges remain, including false positives, contextual errors, and variability of clinical language, requiring further fine-tuning. More here: https://lnkd.in/gMT-zVFm
