Abstract
We have been studying two main methods for preventing patient falls and reducing the workload of medical staff at medical and nursing care facilities, using artificial intelligence and robotics technologies. One is a realtime fall risk assessment method of patients and their surrounding environment using deep learning. The other is a nursing assist method to present recommendations and nursing assist to medical staff for optimal interventions for patients using deep reinforcement learning to reduce the risk. In this paper, we summarize the results of our application study of representative nursing theories to deep reinforcement learning, and present the limitations of its application, problems, proposed solutions, and future prospects. We apply the following researchers’ theories: C. Roy，D. E. Johnson, D. E. Orem, V. A. Henderson, H. Peplau, I. Orlando, F. Abdellah, I. King, E. Wiedenbach, J. Travelbee, M. M. Leininger, M. Newman, M. H. Mishel, N. J. Pender, M. E. Rogers, R. R. Parse, J. Watson, K. Kolcaba, F. Nightingale, P. Benner, M. E. Levine, and develop it further. The result systematizes the positioning of each nursing theory, as well as gains new insights into the scope of the environment and the role of the agent, and by incorporating these findings, and this work advances the "harmonization of applicable environments”.
