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Purpose: To develop high throughput multi-label annotators for body (chest,abdomen, and pelvis) Computed Tomography (CT) reports that can be appliedacross 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 targetedthree organ systems (lungs/pleura, liver/gallbladder, kidneys/ureters) withfour diseases per system based on their prevalence in our dataset. To expandthe algorithms beyond pre-defined keywords, attention-guided recurrent neuralnetworks (RNN) were trained using the RBA-extracted labels to classify reportsas being positive for one or more diseases or normal for each organ system.Confounding effects on model performance were evaluated using randominitialization or pre-trained embedding as well as different sizes of trainingdatasets. Performance was evaluated using the receiver operating characteristic(ROC) area under the curve (AUC) against 2,158 manually obtained labels.  Results: Our models extracted disease labels from 261,229 radiology reportsof 112,501 unique subjects. Pre-trained models outperformed randominitialization across all diseases. As the training dataset size was reduced,performance was robust except for a few diseases with relatively small numberof cases. Pre-trained classification AUCs achieved > 0.95 for all five diseaseoutcomes across all three organ systems.  Conclusions: Our label-extracting pipeline was able to encompass a variety ofcases and diseases by generalizing beyond strict rules with exceptionalaccuracy. This method can be easily adapted to enable automated labeling ofhospital-scale medical data sets for training image-based disease classifiers.

Multi-Label Annotation of Chest Abdomen Pelvis Computed Tomography Text Reports Using Deep Learning