## 3 Results  
Figure 2 shows the results of the Analysis-1 (identifying the best feature set) in the form of heatmaps. There are seven types of reports and three different performance metrics. For each type of report, there are three heatmaps, each corresponding to a performance metric. Therefore, there are a total of 21 heatmaps. While describing the results, we will denote each heatmap with a corresponding report-metric pair (such as IR-Precision, MRI-Recall, etc.). The columns of the heatmap represent the PHI items and the rows represent the features. In the figure, LOCATION, DATE, HOSPITAL, PHONE, and NAME, were denoted by LC, DT, HS, PH, and NM. All of the heatmaps were generated based on the Red-Yellow-Green color scale. In each heatmap, the red color represents the highest value, and the green represents the lowest. It can easily be observed from the figure that the section and sentence pattern features failed to identify any of the PHI items as for all the reports their precision, recall, and F\({}_{1}\)-score were N/A, 0.00, and 0.00, respectively. Therefore, we will exclude these two features from the further description of the results. The IR heatmaps show no such feature that could outperform all other features across all of the performance metrics in identifying the PHI items. For example, the IR-Precision heatmap illustrates that the highest value of 0.73 was achieved by n-gram and prefix-suffix features in identifying LOCATION. For DATE, the highest precision was achieved by word shape with a value of 0.96. For HOSPITAL, the highest value of 0.71 was achieved by n-gram, prefix-suffix, and word shape features. Word embedding and prefix-suffix yielded the highest values for NAME (0.85) and ID (0.88), respectively. Therefore, in terms of precision, the best features were n-gram, prefix-suffix, word embedding, and word shape. The IR-Recall heatmap reveals that for LOCATION, n-gram had the highest value of 0.69. For DATE, the highest value of 0.85 was achieved by discrete word embedding, n-gram, prefix-suffix, word embedding, and word shape features. Similar observation reveals that for HOSPITAL, NAME, and ID, the best-performing features were n-gram, word embedding, and word shape. Though discrete word embedding shared the highest value with other features for DATE, it performed poorly for NAME and had a value of 0.00 for ID. Therefore, we excluded it from the set of best features. IR-Recall heatmap showed that the best-performing features were n-gram, prefix-suffix, word embedding, and word shape. Moreover, a similar study of the IR-F\({}_{1}\) Score heatmap gave the same four features as the best-performing ones. Figure 2: Precision, Recall, and F\({}_{1}\)-score achieved by all the features for all the PHI items in all of the reports in the form of a heatmap. We repeated similar observations on each report-metric heatmap. The set of the best-performing features among all of the reports was consistent with the findings from IR heatmaps. While identifying the best features for a type of reports, we excluded any such feature that yielded the best value for one PHI item but performed poorly for multiple items, which was the case with discrete word embedding in the IR-Recall heatmap. We encountered similar cases in other heatmaps as well. For example, brown clustering had the highest value of precision for DATE in MA reports. Since it failed badly in identifying NAME and ID with a very poor value of all the metrics, it was discarded from the best feature set. The performance of these models and their comparison are presented in Table 2. It can be seen from Table 2 that there is no significant difference between the performance of the best 4 features in combination and all 10 features in combination. For example, in IR reports, M10 had a precision value of 0.71 for LOCATION and M4 had a value of 0.72. Although M4 had a negligibly larger value in this case, there were multiple cases where M10 had larger values as can be seen with the recall value of DATE in IR reports.