# Outcome Prediction for Patients with Bipolar Disorder Using Prodromal and Onset Data +
## 1 Introduction  
Outcome prediction can facilitate physician and patient decision making as well as disease management. In the management of serious mental illnesses, the accurate and early identification of patients at high risk of mortality and other adverse event will enable a targeted monitoring and interventions to improve prognosis. While many risk indices and calculators have been developed for a wide range of diseases and conditions, the outcome prediction of mental illness, especially, serious mental illnesses such as bipolar disorder, remains very challenging [1, 2]. A wide array of features has been used in predictive modeling in mental health, ranging from early treatment response to genetics. The AUC of these predictive models, however, rarely reached 80%, which some argued is the threshold of clinical utility [1]. For instance, with data from mood disorder patients with first lifetime episodes of major depression, [PERSON] et al. used multivariate, logistic modeling and Bayesian methods to predict a later diagnosis of unipolar versus bipolar depressive disorder. It reported correct classification rates of 64-67% of cases and an AUC of 0.72 [3]. Challenges faced by outcome prediction in patients with mental illnesses include inadequate sample size, heterogeneity of the patients sharing the same diagnosis, and individual choices and disparities [1, 4]. However, we can potentially overcome these challenges with larger samples, more nuanced feature representations, and state-of-the-art machine learning methods. The key research question in this study was how well (in terms of AUC and accuracy) the adverse outcome (all-cause mortality and number of all-cause hospitalizations) of bipolar disorder (BD) can be predicted after the initial diagnosis. All-cause mortality and all-cause hospitalizations (especially, re-hospitalizations) are of great interest in clinical research. The AUC of the predictive models in various patient populations is often in the lower 70% range, only occasionally reaching 80% [5; 6; 7; 8]. As regards mental health populations, however, it was difficult for us to find any studies that predicted mortality and hospitalization, even though a number of studies identified mental illness as the significant risk factor for all-cause mortality and all-cause hospitalization [9; 10; 11]. One related study predicted a change in diagnosis from major depression to bipolar disorder after antidepressant initiation. It reported a mean AUC of 76% (ranging from 0.73 to 0.80) [12]. A study predicted clinically relevant changes in bipolar disorder outside the clinic walls based on pervasive technology interactions via smartphone typing dynamics. The results showed a predictive accuracy of around 90% [13]. [PERSON] et al. predicted remission using clinical trial data in BP patients with an AUC of 80% at baseline and of 75% at 1-year follow-up. The AUC was reduced to 67% at both baseline and 1-year follow-up when routinely collected electronic data were used [14]. While machine learning is increasingly used in clinical research [15], prediction models for bipolar, especially with hospitalization and mortality as outcomes, have not been previously reported. This makes our study novel and important. In this study, we experimented with adverse outcome prediction in 20,000 BD patients, using data collected during the 1-year prodromal period before the initial BD diagnosis and during the first week after the diagnosis to predict 1-year post-diagnosis outcomes. Different from the traditional predictive models, our study exhibits innovation in two ways: (1) by using a temporal graph of nuanced features to represent clinical data during the prodromal and onset periods and (2) by applying a deep neural network learning algorithm to the temporal images, since deep learning has demonstrated superior performance over traditional machine learning algorithms [16]. The data source for the study was the clinical data warehouse (CDW) of the US VeterA Affairs (VA) health system.