Deep deterministic policy gradient-based automatic negotiation framework for shared decision-making
Abstract Shared Decision-Making (SDM), a patient-centered approach to medical care, improves treatment outcomes and patient satisfaction. However, traditional SDM struggles in handling complex medical scenarios, dynamic patient preferences, and multi-issue negotiations, particularly under incomplete...
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-11001-1 |
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| author | Xin Chen Ping Lu Yong Liu Fei-Ping Hong |
| author_facet | Xin Chen Ping Lu Yong Liu Fei-Ping Hong |
| author_sort | Xin Chen |
| collection | DOAJ |
| description | Abstract Shared Decision-Making (SDM), a patient-centered approach to medical care, improves treatment outcomes and patient satisfaction. However, traditional SDM struggles in handling complex medical scenarios, dynamic patient preferences, and multi-issue negotiations, particularly under incomplete information. The key challenge lies in capturing the fuzzy preferences of doctors and patients while ensuring efficient and fair multi-issue negotiations. This study introduces AutoSDM-DDPG, an automated SDM framework based on the Deep Deterministic Policy Gradient (DDPG) algorithm. Using an Actor-Critic network, the framework dynamically optimizes negotiation strategies to address multidimensional demands and resolve preference inconsistencies in treatment planning. Fuzzy membership functions are applied to model the uncertainty in patient preferences, enhancing representation and improving multi-issue negotiation outcomes. Experimental results show that AutoSDM-DDPG outperforms other models in key indicators, including social welfare, satisfaction disparity, and decision quality. It achieves faster and more equitable negotiations while balancing the needs of both doctors and patients. In scenarios involving multi-issue negotiations and complex preferences, AutoSDM-DDPG demonstrates exceptional adaptability, achieving efficient and fair decision-making. |
| format | Article |
| id | doaj-art-01b24b2d3cf6406dbe8379e1166e1f6f |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-01b24b2d3cf6406dbe8379e1166e1f6f2025-08-20T03:46:08ZengNature PortfolioScientific Reports2045-23222025-07-0115112010.1038/s41598-025-11001-1Deep deterministic policy gradient-based automatic negotiation framework for shared decision-makingXin Chen0Ping Lu1Yong Liu2Fei-Ping Hong3College of Artificial Intelligence, Xiamen Institute of TechnologySchool of Economic and Management, Xiamen University of TechnologyCollege of Artificial Intelligence, Xiamen Institute of TechnologyDepartment of Pediatrics, Women and Children’s Hospital, School of Medicine, Xiamen UniversityAbstract Shared Decision-Making (SDM), a patient-centered approach to medical care, improves treatment outcomes and patient satisfaction. However, traditional SDM struggles in handling complex medical scenarios, dynamic patient preferences, and multi-issue negotiations, particularly under incomplete information. The key challenge lies in capturing the fuzzy preferences of doctors and patients while ensuring efficient and fair multi-issue negotiations. This study introduces AutoSDM-DDPG, an automated SDM framework based on the Deep Deterministic Policy Gradient (DDPG) algorithm. Using an Actor-Critic network, the framework dynamically optimizes negotiation strategies to address multidimensional demands and resolve preference inconsistencies in treatment planning. Fuzzy membership functions are applied to model the uncertainty in patient preferences, enhancing representation and improving multi-issue negotiation outcomes. Experimental results show that AutoSDM-DDPG outperforms other models in key indicators, including social welfare, satisfaction disparity, and decision quality. It achieves faster and more equitable negotiations while balancing the needs of both doctors and patients. In scenarios involving multi-issue negotiations and complex preferences, AutoSDM-DDPG demonstrates exceptional adaptability, achieving efficient and fair decision-making.https://doi.org/10.1038/s41598-025-11001-1Shared decision makingDoctor-patient negotiationAgentFuzzy constraintDeep reinforcement learning |
| spellingShingle | Xin Chen Ping Lu Yong Liu Fei-Ping Hong Deep deterministic policy gradient-based automatic negotiation framework for shared decision-making Scientific Reports Shared decision making Doctor-patient negotiation Agent Fuzzy constraint Deep reinforcement learning |
| title | Deep deterministic policy gradient-based automatic negotiation framework for shared decision-making |
| title_full | Deep deterministic policy gradient-based automatic negotiation framework for shared decision-making |
| title_fullStr | Deep deterministic policy gradient-based automatic negotiation framework for shared decision-making |
| title_full_unstemmed | Deep deterministic policy gradient-based automatic negotiation framework for shared decision-making |
| title_short | Deep deterministic policy gradient-based automatic negotiation framework for shared decision-making |
| title_sort | deep deterministic policy gradient based automatic negotiation framework for shared decision making |
| topic | Shared decision making Doctor-patient negotiation Agent Fuzzy constraint Deep reinforcement learning |
| url | https://doi.org/10.1038/s41598-025-11001-1 |
| work_keys_str_mv | AT xinchen deepdeterministicpolicygradientbasedautomaticnegotiationframeworkforshareddecisionmaking AT pinglu deepdeterministicpolicygradientbasedautomaticnegotiationframeworkforshareddecisionmaking AT yongliu deepdeterministicpolicygradientbasedautomaticnegotiationframeworkforshareddecisionmaking AT feipinghong deepdeterministicpolicygradientbasedautomaticnegotiationframeworkforshareddecisionmaking |