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. 2019 Mar 15;2(3):e190606. doi: 10.1001/jamanetworkopen.2019.0606

Figure 1. Forecasting Performance of the Deep Learning Models in the University Hospital (UH) Cohort.

Figure 1.

The distribution of outcomes from the training cohort at UH was 60% controlled and 40% uncontrolled according to the clinical disease activity index. This was previously used to train the outcome posterior classifier at UH (area under the receiver operating characteristic curve [AUROC], 0.535). The likelihood of switching outcomes between visits within the training cohort was 25%. This was used previously to train the change posterior classifier at UH (AUROC, 0.554). Deep Learning produced the best results (AUROC, 0.912).