Multi-Label Annotation of Chest Abdomen Pelvis Computed Tomography Text Reports Using Deep Learning. (arXiv:2102.02959v5 [cs.AI] UPDATED)

Purpose: To develop high throughput multi-label annotators for body (chest,
abdomen, and pelvis) Computed Tomography (CT) reports that can be applied
across a variety of abnormalities, organs, and disease states.

Approach: We used a dictionary approach to develop rule-based algorithms
(RBA) for extraction of disease labels from radiology text reports. We targeted
three organ systems (lungs/pleura, liver/gallbladder, kidneys/ureters) with
four diseases per system based on their prevalence in our dataset. To expand
the algorithms beyond pre-defined keywords, attention-guided recurrent neural …

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