Multi-Label Annotation of Chest Abdomen Pelvis Computed Tomography Text
Reports Using Deep Learning
- URL: http://arxiv.org/abs/2102.02959v1
- Date: Fri, 5 Feb 2021 02:07:39 GMT
- Title: Multi-Label Annotation of Chest Abdomen Pelvis Computed Tomography Text
Reports Using Deep Learning
- Authors: Vincent M. D'Anniballe, Fakrul I. Tushar, Khrystyna Faryna, Songyue
Han, Maciej A. Mazurowski, Geoffrey D. Rubin, Joseph Y. Lo
- Abstract summary: We developed a multi-label annotator for body Computed Tomography (CT) reports that can be applied to a variety of diseases, organs, and cases.
We used a dictionary approach to develop a rule-based algorithm for extraction of disease labels from radiology text reports.
An attention-guided recurrent neural network (RNN) was trained using the RBA-extracted labels to classify the reports as being positive for one or more diseases or normal for each organ system.
- Score: 1.5701326192371183
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: To develop a high throughput multi-label annotator for body Computed
Tomography (CT) reports that can be applied to a variety of diseases, organs,
and cases. First, we used a dictionary approach to develop a rule-based
algorithm (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 algorithm beyond pre-defined keywords, an
attention-guided recurrent neural network (RNN) was trained using the
RBA-extracted labels to classify the reports as being positive for one or more
diseases or normal for each organ system. Confounding effects on model
performance were evaluated using random or pre-trained embedding as well as
different sizes of training datasets. Performance was evaluated using the
receiver operating characteristic (ROC) area under the curve (AUC) against
2,158 manually obtained labels. Our model extracted disease labels from 261,229
radiology reports of 112,501 unique subjects. Pre-trained models outperformed
random embedding across all diseases. As the training dataset size was reduced,
performance was robust except for a few diseases with relatively small number
of cases. Pre-trained Classification AUCs achieved > 0.95 for all five disease
outcomes across all three organ systems. Our label-extracting pipeline was able
to encompass a variety of cases and diseases by generalizing beyond strict
rules with exceptional accuracy. As a framework, this model can be easily
adapted to enable automated labeling of hospital-scale medical data sets for
training image-based disease classifiers.
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