## 5 Conclusions  
In this paper, we propose a complex medical time recognition method combining PSL and text feedback mechanisms. First, we define seven basic features and eleven extraction rules based on the characteristics of complex medical time. Then these eleven rules are incorporated into the deep learning model through PSL and a text feedback mechanism to improve its ability to recognize complex medical time. Finally, the effectiveness of this method is verified by comparison experiments, and the PSL is proved to have an enhancing effect on text feature recognition using ablation experiments. There are several limitations in this study, and in the future, we will continue our in-depth analysis to address the existing problems:  
1. The data used are not a publicly available normalized dataset. Therefore, we consider how to construct a Chinese medical time dataset and achieve normalized annotation of temporal entities and temporal relationships while protecting privacy. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**BERT+textCNN**} & \multicolumn{3}{c}{**PSL-CTR**} \\  & \(\mathbf{P}\) & \(\mathbf{R}\) & \(\mathbf{F1}\) & \(\mathbf{P}\) & \(\mathbf{R}\) & \(\mathbf{F1}\) \\ \hline \(l_{8}\) & 90.39 & 93.99 & 92.15 & 97.93 (7.54) & 96.08 (2.09) & 97.00 (4.84) \\ \(l_{9}\) & 81.63 & 77.16 & 79.33 & 92.20 (10.57) & 91.58 (14.42) & 91.89 (12.56) \\ \(l_{10}\) & 73.08 & 77.98 & 75.45 & 85.93 (12.85) & 82.96 (4.98) & 84.42 (8.97) \\ \(l_{11}\) & 90.53 & 82.13 & 86.13 & 93.88 (3.35) & 94.64 (12.51) & 94.26 (8.13) \\ \(l_{12}\) & 68.56 & 71.60 & 70.05 & 82.47 (13.91) & 79.74 (8.14) & 81.08 (11.03) \\ \(l_{13}\) & 83.24 & 70.86 & 76.55 & 92.35 (9.11) & 80.10 (9.24) & 85.79 (9.24) \\ \(l_{14}\) & 66.67 & 76.00 & 71.03 & 86.68 (20.01) & 80.91 (4.91) & 83.70 (12.67) \\ \(l_{15}\) & 82.13 & 83.40 & 82.76 & 93.11 (10.98) & 86.77 (3.37) & 89.83 (7.07) \\ \(l_{16}\) & 60.86 & 58.23 & 59.52 & **83.93 (23.07)** & 67.14 (8.91) & 74.60 (15.09) \\ \(l_{17}\) & 47.23 & 47.84 & 47.53 & 63.64 (16.41) & **67.74 (19.90)** & **65.63 (18.09)** \\ \(l_{18}\) & 45.00 & 44.00 & 44.49 & 58.62 (13.62) & 56.67 (12.67) & 57.63 (13.13) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: L-atom statements in private datasets predict the results of controlled experiments. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**BERT+textCNN**} & \multicolumn{3}{c}{**PSL-CTR**} \\  & \(\mathbf{P}\) & \(\mathbf{R}\) & \(\mathbf{F1}\) & \(\mathbf{P}\) & \(\mathbf{R}\) & \(\mathbf{F1}\) \\ \hline \(l_{1}\) & 99.81 & 96.89 & 98.33 & 99.34 (\(-\)0.47) & 94.23 (\(-\)2.66) & 96.72 (\(-\)1.61) \\ \(l_{2}\) & 97.42 & 86.64 & 91.72 & 97.18 (\(-\)0.24) & 87.22 (0.58) & 91.93 (0.21) \\ \(l_{3}\) & 96.97 & 91.10 & 94.65 & 97.75 (0.78) & 89.21 (\(-\)1.89) & 93.29 (\(-\)1.36) \\ \(l_{4}\) & 78.79 & 55.32 & 65.00 & 80.25 (1.46) & 61.41 (6.093) & **69.58 (4.58)** \\ \(l_{5}\) & 96.64 & 93.98 & 95.29 & 98.73 (2.08) & 92.38 (\(-\)1.60) & 95.45 (0.16) \\ \(l_{6}\) & 97.60 & 92.64 & 95.06 & 97.59 (\(-\)0.01) & 92.87 (0.23) & 95.17 (0.11) \\ \(l_{7}\) & 96.84 & 64.04 & 77.09 & 92.97 (\(-\)3.87) & 65.58 (1.54) & 76.91 (\(-\)0.18) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: L-atom statements in private datasets predict the results of controlled experiments. 2. The model in this paper is only for a Chinese dataset. We will verify whether this method can be used for other languages and explore multilingual temporal recognition models in the future. 3. This paper only uses feedback adjustment to adjust the model after the model obtains each training result. Another promising research direction is to explore embedding the rules in the model during the model training. Conceptualization, J.G. and D.W.; methodology, J.G.