## 4 Discussion  
In this study, we built a model to predict CPRC-treated patients. From this, we were able to draw the following conclusions. Serial medical data is an important factor in predicting the likely outcome of patients with PCa. For example, predicting the likelihood of CRPC helps medical professionals to make appropriate decisions at the right time. Some previous studies have predicted time or variables related to CRPC. For example, [PERSON] et al. used a mathematical algorithm to predict the time until biochemical failure of ADT using data from 83 hormone sensitive prostate cancer patients [30]. Although this study only used data from 83 patients, it had a prediction accuracy of 90%. In this study, we used data from 1592 patients to predict CRPC, which is a large number of patients for building a prediction model. Another study by [PERSON] et al. tried to find a relationship between age at diagnosis and CRPC. They found that those aged between 55 and 75 years have the shortest survival time [31]. We did not use age as an input variable since there was no significant difference (chi-squared test p-value of 0.253) between the groups with and without CRPC treatment (see Table 1). In another study, [PERSON] et al. used DNA hypermethylation profiles to predict CRPC. They found 61 genes related to CRPC [32]. However, DNA sequencing is not commonly used for PCa patients and it incurs extra expenses for the patient. We used typical follow-up data from PCa patients. A model based on commonly collected data make it more applicable in real out-patient scenarios. We built a CRPC prediction model, which can support physicians' decision making and allows them to review more patients in a short period of time. When medical staff do not have enough time to track the status of each patient in need of significant interventions, human errors are more likely to occur and opportunities for treatment may be missed until the condition becomes serious. If prediction support is available, such as the likelihood of a PCa patient developing CPRC, patients in need of treatment can be identified quickly. Therefore, patients will receive the right treatment at the right time. We used the state-of-the-art deep learning method to handle serial medical data. Because it is difficult to predict a patient's outcome with serial data that is collected irregularly, including medications, treatments, and laboratory test results such as blood tests. Typical deep learning methods such as RNN and LSTM can be used to analyze serial data. However, they must be improved in order to handle irregularly-sampled serial datasets. To overcome this, we adopted recently developed deep learning technology. The phased-LSTM [14] deep learning method builds a fast and accurate prediction model using serial medical data. The predictions show high precision recall scores, which means that they are highly accurate. The results of this study can be extended to other areas such as emergency room or intensive care units, which receive a significant amount of serial data from patients. In addition, follow-up data from many patient care organizations can be used with this method to make accurate predictions that support clinicians' decision-making. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**10-Fold Cross Validation**} & \multicolumn{2}{c}{**120-Day Model**} & \multicolumn{2}{c}{**360-Day Model**} \\ \cline{2-5}  & **Loss** & **Accuracy** & **Loss** & **Accuracy** \\ \hline
1 & 0.1067 & 0.9778 & 0.2015 & 0.9937 \\
2 & 0.0596 & 0.9810 & 0.0548 & 0.9873 \\
3 & 0.0750 & 0.9778 & 0.1027 & 0.9714 \\
4 & 0.1263 & 0.9587 & 0.1008 & 0.9778 \\
5 & 0.2235 & 0.9841 & 0.0897 & 0.9746 \\
6 & 0.1063 & 0.9714 & 0.1090 & 0.9746 \\
7 & 0.1342 & 0.9905 & 0.0859 & 0.9683 \\
8 & 0.2189 & 0.9937 & 0.0930 & 0.9746 \\
9 & 0.0863 & 0.9810 & 0.1290 & 0.9651 \\
10 & 0.1190 & 0.9651 & 0.0744 & 0.9810 \\ Average & 0.1256 & 0.9781 & 0.1041 & 0.9768 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: 10-fold cross validation of the model.