# Outcome Prediction for Patients with Bipolar Disorder Using Prodromal and Onset Data +
## 3 Results  
We identified a total of 346,511 patients with at least two encounters with a BD diagnosis. After we applied the inclusion/exclusion criteria, 207,838 patients remained. Among these patients, 20,000 were randomly selected for the analysis. The baseline characteristics of the randomly selected 20,000 BD patients are described in Table 2. The mean age of the patients was 48.8 (SD 13.5) years. Most of the patients were male, who accounted for 84.2% of them; about 73.4% were Whites, followed by Blacks (17.2%), unknown (7.5%), and others (2.0%); 5.4% were Hispanics.  
The outcome hospitalizations of the cohort had various types as indicated by the admission ICD codes. The distributions of the hospitalization types among the outcome hospitalizations and the patients are summarized in Table 3. To provide a nuanced analysis of the DNN performance, the hospitalizations were first divided into two larger groups, i.e., mental and non-mental, and then divided into smaller groups. The smaller groups were the same as the diagnosis groups listed in Table 1. Here, we only list the smaller groups which had 100 or more associated patients. The result showed that 64% of the outcome hospitalizations were caused by mental disorders, and 69% of the hospitalized patients had such a hospitalization as an outcome.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Demographics** & **Mean/N** & **STD/\%** \\ \hline Age (Years) & 48.8 & 13.5 \\ Male & 16,842 & 84.2\% \\ Race White & 14,673 & 73.4\% \\ Race Black & 3434 & 17.2\% \\ Race Others & 395 & 2.0\% \\ Race Unknown & 1498 & 7.5\% \\ Ethnicity Hispanics & 1072 & 5.4\% \\ Ethnicity Non-Hispanics & 17,896 & 89.5\% \\ Ethnicity Unknown & 1032 & 5.2\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Baseline characteristics of the randomly selected 20,000 BD patients.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Hospitalization Type** &
\begin{tabular}{c} **Number of** \\ **Hospitalizations** \\ \end{tabular} & **Number of Patients** \\ \hline
**All-cause** & **10,961** & **5276** \\
**Mental** & **7035 (64\%)** & **3659 (69\%)** \\ Episodic mood disorders (296) & 2321 (21.2\%) & 1613 (14.7\%) \\ Alcohol dependence syndrome (303) & 1156 (10.5\%) & 737 (6.7\%) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Number of hospitalizations and patients by hospitalization type.  
We trained the DNN models and SVM models for the prediction of the four levels of adverse outcomes. The performance of the models on the testing set is reported in Table 4. In particular, the AUCs of the models are plotted in Figure 3 for direct comparison.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Outcome** & **DNN AUC** & **DNN Accuracy** & **SVM AUC** & **SVM Accuracy** \\ \hline
0 vs. 1+/mort & 0.750 & 0.767 & 0.740 & 0.757 \\
1– vs. 2+/mort & 0.776 & 0.867 & 0.770 & 0.861 \\
2– vs. 3+/mort & 0.794 & 0.921 & 0.780 & 0.920 \\
3– vs. 4+/mort & 0.806 & 0.946 & 0.796 & 0.949 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance of the predictive models on the testing set.  
Figure 3: AUCs of the DNN model and SVM model.  
It was also interesting to ask how the models performed in predicting hospitalizations only vs. mortality, as well as how the models (for predicting \"1- vs. 2+/mort\") performed in predicting different hospitalization types. The results showed that the prediction of mortality was better than that of hospitalizations only (AUC 0.814 vs. 0.774), and the prediction of non-mental illnesses was better than that of mental illnesses (Table 5). However, on the finer-grained level (smaller group), the prediction of hospitalizations caused by alcohol-induced mental disorders was the best among all.