# Outcome Prediction for Patients with Bipolar Disorder Using Prodromal and Onset Data +
## 4 Discussion  
Prior predictive modeling efforts in the psychiatric domain had difficulty achieving an AUC or accuracy of 80%. Using an image representation of clinical data during the prodromal and onset period and a deep neural network, we were able to achieve accuracies of 0.766-0.949 and AUC of 0.745-0.815. In comparison, using the same features without temporal representation and a traditional machine learning algorithm, lower accuracies (0.757-0.949) and AUC (0.740-0.796) were obtained. This demonstrated the feasibility of predicting the outcomes of serious mental illnesses. The ability to predict a patient outcome at the time of the initial BD diagnosis has several implications. Patients who are predicted to have poor outcomes can be closely monitored and considered for additional interventions by clinicians. The predictive results may be shared with the patients to allow for a shared decision making. The prediction of poor outcomes may also be used to motivate the patients to adhere to the prescribed treatments, since treatment adherence is a frequent problem in BD patients. Hospitalization and mortality, combined or as separate outcomes, are clinically very important. Few prior studies reported bipolar outcome prediction [12; 13; 14]; however, they did not use hospitalization and mortality as outcomes. This prevented us from performing a direct comparison in terms of accuracy or AUC. There is evidence that predicting outcomes using routinely collected clinical data, as we used in this study, is challenging [14]. \begin{table}
\begin{tabular}{l c} \hline \hline \multicolumn{1}{c}{**Hospitalization Type**} & **AUC** \\ \hline
**All-cause** & **0.776** \\
**Mental** & **0.779** \\ Episodic mood disorders (296) & 0.765 \\ Alcohol dependence syndrome (303) & 0.812 \\ Adjustment reaction (309) & 0.762 \\ Schizophrenia disorders (295) & 0.865 \\ Drug dependence (304) & 0.824 \\ Drug-induced mental disorders (292) & 0.821 \\ Alcohol-induced mental disorders (291) & 0.904 \\ Depressive disorder not elsewhere classified (311) & 0.811 \\ Nondependent abuse of drug (305) & 0.834 \\ Other nonorganic psychoses (298) & 0.711 \\ Anxiety dissociative and somatoform disorders (300) & 0.701 \\
**Non-mental** & **0.826** \\ Symptoms signs and ill-defined conditions (780-799) & 0.832 \\ Diseases of the circulatory system (390-459) & 0.841 \\ Diseases of the digestive system (520-579) & 0.855 \\ Supplementary classification of factors influencing health status and & 0.837 \\ contact with health services (v01–v91) & 0.814 \\ Injury and poisoning (800-999) & 0.852 \\ Diseases of the respiratory system (460–519) & 0.822 \\ Diseases of the musculoskeletal system and connective tissue (710–739) & 0.854 \\ Diseases of the genitourinary system (580–629) & 0.854 \\ Endocrine nutritional and metabolic diseases and immunity & 0.902 \\ disorders (240–279) & 0.745 \\ Diseases of the nervous system and sense organs (320–389) & 0.772 \\ Neoplasms (140–239) & 0.772 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance comparison across different hospitalization types. Machine learning generally benefits from a large sample size. The fact that the sample size in our experiment was 20,000 helped to boost the predictive performance. From the VA database, we could pull in several times more BD patients. This suggests that the predictive accuracy and AUC could increase. We will use a larger sample size (e.g., n = 100,000) in a future study. Features and learning algorithms are also important factors for predictive modeling. The features include both clinical features (e.g., diagnoses) and temporal features. Given the prior literature, we included several types of coded data features (i.e., hospitalization, diagnosis, procedures, vitals, and lab results). We did not conduct variable selection but did group the numerous diagnoses and procedures based on existing terminologies. We also only included a small number of common lab tests.