*6.2. Extension of the Works*

Further extensive studies are needed and some recommendations for future research are given as follows. First, this research is based on the evaluation of factors importance. Future studies can extend the study by adding the number of factors. Second, this paper estimates the importance of different structures to death based on the Random Forest algorithm. Future studies can add the sparse learning to process the data for the classifier getting better results. Third, the deep learning model assesses the human losses with some optimization algorithms. Future studies can add the hidden layers and continue to optimize the algorithms. It will be of great interests to focus on death prediction in the future works.

**Author Contributions:** Conceptualization, H.J. and J.L. (Junqi Lin); methodology, H.J.; software, H.J.; validation, all three authors; formal analysis, H.J.; investigation, H.J.; resources, J.L. (Junqi Lin); data curation, J.L. (Junqi Lin); writing—original draft preparation, H.J.; writing—review and editing, H.J.; visualization, H.J.; supervision, J.L. (Junqi Lin) and J.L. (Jinlong Liu); project administration, J.L. (Junqi Lin) and J.L. (Jinlong Liu); funding acquisition, J.L. (Junqi Lin) and J.L. (Jinlong Liu)

**Funding:** This research was funded by NATIONAL KEY R&D PROGRAM OF CHINA, grant number 2018YFC1504503.

**Acknowledgments:** We would like to thank Chen Zhao for guidance and help with applying machine learning algorithms.

**Conflicts of Interest:** The authors declare no conflict of interest.
