Evidence map›Paper›PMID 39930496›Full record

ArticleBMC medical informatics and decision making2025

Bayesian learning-based agent negotiation model to support doctor-patient shared decision making.

Xin Chen, Yong Liu, Fei-Ping Hong, Ping Lu, Jiang-Tao Lu, Kai-Biao Lin

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC medical informatics and decision making, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Xin ChenDepartment of Computer Science and Technology, Xiamen University of Technology, Xiamen, 361024, China.
Yong LiuDepartment of Computer Science and Technology, Xiamen University of Technology, Xiamen, 361024, China.
Fei-Ping HongDepartment of Pediatrics, Women and Children's Hospital, School of Medicine, Xiamen University, Xiamen, 361102, China.
Ping LuSchool of Economic and Management, Xiamen University of Technology, Xiamen, 361024, China. 2011990101@xmut.edu.cn.
Jiang-Tao LuDepartment of Computer Science and Technology, Xiamen University of Technology, Xiamen, 361024, China.
Kai-Biao LinDepartment of Computer Science and Technology, Xiamen University of Technology, Xiamen, 361024, China.

Funding

Graduate Student Science and Technology Innovation Program Project of Xiamen University of Technology under Grant No.YKJCX2021056High Level Talent Project of Xiamen University of Technology under Grant No.YSK20002RXiamen Overseas Returnees Program under Grant No.XM202017206Xiamen Youth Science and Technology Innovation Project under Grant No.3502Z20206073
6 · The paper itself

Abstract

backgroundAgent negotiation is widely used in e-commerce negotiation, cloud service service-level agreements, and power transactions. However, few studies have adapted alternative negotiation models to negotiation processes between healthcare professionals and patients due to the fuzziness, ethics, and importance of medical decision making.

methodWe propose a Bayesian learning based bilateral fuzzy constraint agent negotiation model (BLFCAN). It support mutually beneficial agreement on treatment between doctors and patients. The proposed model expresses the imprecise preferences and behaviors of doctors and patients through fuzzy constrained agents. To improve negotiation efficiency and social welfare, the Bayesian learning method is adopted in the proposed model to predict the opponent's preference.

resultsThe proposed model achieves 55.4% to 64.2% satisfaction for doctors and 69-74.5% satisfaction for patients in terms of individual satisfaction. In addition, the proposed BLFCAN can increase overall satisfaction by 26.5-29% in fewer rounds, and it can alter the negotiation strategy in a flexible manner for various negotiation scenarios.

conclusionsBLFCAN reduces communication time and cost, helps avoid potential conflicts, and reduces the impact of emotions and biases on decision-making. In addition, the BLFCAN model improves the agreement satisfaction of both parties and the total social welfare.

Indexed as

Decision Making, SharedNegotiatingPhysician-Patient RelationsBayes TheoremFuzzy LogicHumansPatient SatisfactionAgentBayesian learningDoctor-patient negotiationFuzzy constraintShared decision making

Identifiers

PMID39930496
PMCPMC11812200

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.