Artificial intelligence promotes shared decision-making through recommending tests to febrile pediatric outpatients

BACKGROUND:To promote the shared decision-making(SDM)between patients and doctors in pediatric outpatient departments,this study was designed to validate artificial intelligence(AI)-initiated medical tests for children with fever.METHODS:We designed an AI model,named Xiaoyi,to suggest necessary tests for a febrile child before visiting a pediatric outpatient clinic.We calculated the sensitivity,specificity,and F1 score to evaluate the efficacy of Xiaoyi’s recommendations.The patients were divided into the rejection and acceptance groups.Then we analyzed the rejected examination items in order to obtain the corresponding reasons.RESULTS:We recruited a total of 11,867 children with fever who had used Xiaoyi in outpatient clinics.The recommended examinations given by Xiaoyi for 10,636(89.6%)patients were qualified.The average F1 score reached 0.94.A total of 58.4%of the patients accepted Xiaoyi’s suggestions(acceptance group),and 41.6%refused(rejection group).Imaging examinations were rejected by most patients(46.7%).The tests being time-consuming were rejected by 2,133 patients(43.2%),including rejecting pathogen studies in 1,347 patients(68.5%)and image studies in 732 patients(31.8%).The difficulty of sampling was the main reason for rejecting routine tests(41.9%).CONCLUSION:Our model has high accuracy and acceptability in recommending medical tests to febrile pediatric patients,and is worth promoting in facilitating SDM.

Older Patients’ Involvement in Shared Decision-Making—A Systematic Review

Shared decision-making has been described as allowing patients to gain more control over their life situation and feel less helpless. The aim of this systematic review was to describe the involvement of older patients in shared decision-making in community settings. In accordance with the systematic review method, a total of 2468 abstracts were read, after which nine quantitative studies were included. A qualitative thematic analysis was performed and two themes emerged;increased understanding of self-management and a desire to strengthen one’s position in relationship with professionals, both of which were essential for empowering older patients to participate in shared decision-making. Older patients’ shared decision-making was seen as a struggle to maintain their autonomy in different areas of everyday life. Emotional and psychological problems made their position more difficult. In order to empower them in relationships with healthcare professionals, older patients require more knowledge (self-efficacy) and information about their illness, which could strengthen their position in the decision-making process. They also need a greater awareness of decisional conflicts that may arise. Age, gender and health status influence older patients’ chance of being respected and taken seriously in relationship with professionals.

Greater awareness of biosimilars and shared decision-making among patients attending rheumatology practices in Colorado,United States:Real-world data

BACKGROUND There is a lack of studies and educational programs focused on biosimilars and shared decision-making among patients diagnosed with various rheumatic diseases.AIM To improve knowledge and awareness of biosimilars and shared decision-making among patients attending rheumatology practices in Colorado as well as to assess a rheumatology patient’s interest in discussing biosimilars as well as shared decision-making with others(e.g.,medical professionals,family members,friends).METHODS Our goal was to work with 80 rheumatology teams in Colorado.We developed and distributed 2000 multi-page brochures to each participating office and later conducted an online anonymous survey.RESULTS There were a total of 49(2.5%)rheumatology patients who responded to our survey.After reading our educational booklet,many survey respondents identified the correct answer in most questions focused on biosimilars or shared decision-making.Our survey results suggest that patients attending rheumatology practices in Colorado are generally not involved in discussions with their providers regarding treatment plans or options.The improvement in scores after reading our educational materials was statistically significant for biosimilars and shared decision-making.CONCLUSION Overall,the level of knowledge and awareness of biosimilars and shared decisionmaking among patients attending rheumatology practices in Colorado was low.More educational programs as well as follow up trainings to measure changes in knowledge and awareness regarding biosimilars and shared decision-making among patients attending rheumatology practices are recommended.

Shared Decision-Making after Implementation of the Chronic Care Model (CCM)—An Evaluative Approach

Background: In paternalistic models, healthcare providers’ responsibility is to decide what is best for patients. The main concern is that such models fail to respect patient autonomy and do not promote patient responsibility. Aim: To evaluate mental healthcare team members’ perceptions of their own role in encouraging elderly persons to participate in shared decision-making after implementation of the CCM. The CCM is not an explanatory theory, but an evidence-based guideline and synthesis of best available evidence. Methods: Data were collected from two teams that took part in a focus group interview, and the transcript was analysed by means of qualitative thematic analysis. Results: One overall theme emerged—Preventing the violation of human dignity based on three themes, namely, Changing understanding and attitudes, Increasing depressed elderly persons’ autonomy and Clarifying the mental healthcare team coordinator’s role and responsibility. The results of this study reveal that until recently, paternalism has been the dominant decision-making model within healthcare, without any apparent consideration of the patient perspective. Community mental healthcare can be improved by shared decision-making in which team members initiate a dialogue focusing on patient participation to prevent the violation of human dignity. However, in order to determine how best to empower the patient, team members need expert knowledge and intuition.

Shared decision-making in the management of patients with inflammatory bowel disease

The rapid progress of research into inflammatory bowel disease(IBD)has resulted in increasingly more treatment options.Different options have different advantages and disadvantages,and the preferences of patients may also differ.If patients can be invited to the formulation of medical decision-making,their compliance and satisfaction would be improved,thus possibly achieving better therapeutic results.The present review aims to summarize the current literature on shared decision-making(SDM)in the management of IBD,with the goal of promoting the application of SDM.

The concept of narrative evidence-based medicine and shared decision-making in traditional Chinese medical practice

随着精准医疗战略的推进，新兴诊断治疗技术的爆发式发展，共同决策可以提高临床决策的质量，促进中医临床研究向证据的转化。关注患者的叙述需求，加强医学人文关怀，可提高临床疗效和患者满意度。本文介绍了循证医学、叙述医学和共同决策的起源和发展，分析了中医临床决策中存在的问题。进一步，在叙述循证医学理念和方法的指导下，提出了临床医师和患者共同决策的方法。

Evolutionary Decision-Making and Planning for Autonomous Driving Based on Safe and Rational Exploration and Exploitation

Decision-making and motion planning are extremely important in autonomous driving to ensure safe driving in a real-world environment.This study proposes an online evolutionary decision-making and motion planning framework for autonomous driving based on a hybrid data-and model-driven method.First,a data-driven decision-making module based on deep reinforcement learning(DRL)is developed to pursue a rational driving performance as much as possible.Then,model predictive control(MPC)is employed to execute both longitudinal and lateral motion planning tasks.Multiple constraints are defined according to the vehicle’s physical limit to meet the driving task requirements.Finally,two principles of safety and rationality for the self-evolution of autonomous driving are proposed.A motion envelope is established and embedded into a rational exploration and exploitation scheme,which filters out unreasonable experiences by masking unsafe actions so as to collect high-quality training data for the DRL agent.Experiments with a high-fidelity vehicle model and MATLAB/Simulink co-simulation environment are conducted,and the results show that the proposed online-evolution framework is able to generate safer,more rational,and more efficient driving action in a real-world environment.

Toward Trustworthy Decision-Making for Autonomous Vehicles:A Robust Reinforcement Learning Approach with Safety Guarantees

While autonomous vehicles are vital components of intelligent transportation systems,ensuring the trustworthiness of decision-making remains a substantial challenge in realizing autonomous driving.Therefore,we present a novel robust reinforcement learning approach with safety guarantees to attain trustworthy decision-making for autonomous vehicles.The proposed technique ensures decision trustworthiness in terms of policy robustness and collision safety.Specifically,an adversary model is learned online to simulate the worst-case uncertainty by approximating the optimal adversarial perturbations on the observed states and environmental dynamics.In addition,an adversarial robust actor-critic algorithm is developed to enable the agent to learn robust policies against perturbations in observations and dynamics.Moreover,we devise a safety mask to guarantee the collision safety of the autonomous driving agent during both the training and testing processes using an interpretable knowledge model known as the Responsibility-Sensitive Safety Model.Finally,the proposed approach is evaluated through both simulations and experiments.These results indicate that the autonomous driving agent can make trustworthy decisions and drastically reduce the number of collisions through robust safety policies.

Orientation and Decision-Making for Soccer Based on Sports Analytics and AI:A Systematic Review

Due to ever-growing soccer data collection approaches and progressing artificial intelligence(AI) methods, soccer analysis, evaluation, and decision-making have received increasing interest from not only the professional sports analytics realm but also the academic AI research community. AI brings gamechanging approaches for soccer analytics where soccer has been a typical benchmark for AI research. The combination has been an emerging topic. In this paper, soccer match analytics are taken as a complete observation-orientation-decision-action(OODA) loop.In addition, as in AI frameworks such as that for reinforcement learning, interacting with a virtual environment enables an evolving model. Therefore, both soccer analytics in the real world and virtual domains are discussed. With the intersection of the OODA loop and the real-virtual domains, available soccer data, including event and tracking data, and diverse orientation and decisionmaking models for both real-world and virtual soccer matches are comprehensively reviewed. Finally, some promising directions in this interdisciplinary area are pointed out. It is claimed that paradigms for both professional sports analytics and AI research could be combined. Moreover, it is quite promising to bridge the gap between the real and virtual domains for soccer match analysis and decision-making.

Ethical Decision-Making Framework Based on Incremental ILP Considering Conflicts

Humans are experiencing the inclusion of artificial agents in their lives,such as unmanned vehicles,service robots,voice assistants,and intelligent medical care.If the artificial agents cannot align with social values or make ethical decisions,they may not meet the expectations of humans.Traditionally,an ethical decision-making framework is constructed by rule-based or statistical approaches.In this paper,we propose an ethical decision-making framework based on incremental ILP(Inductive Logic Programming),which can overcome the brittleness of rule-based approaches and little interpretability of statistical approaches.As the current incremental ILP makes it difficult to solve conflicts,we propose a novel ethical decision-making framework considering conflicts in this paper,which adopts our proposed incremental ILP system.The framework consists of two processes:the learning process and the deduction process.The first process records bottom clauses with their score functions and learns rules guided by the entailment and the score function.The second process obtains an ethical decision based on the rules.In an ethical scenario about chatbots for teenagers’mental health,we verify that our framework can learn ethical rules and make ethical decisions.Besides,we extract incremental ILP from the framework and compare it with the state-of-the-art ILP systems based on ASP(Answer Set Programming)focusing on conflict resolution.The results of comparisons show that our proposed system can generate better-quality rules than most other systems.

Stroke Risk Assessment Decision-Making Using a Machine Learning Model:Logistic-AdaBoost

Stroke is a chronic cerebrovascular disease that carries a high risk.Stroke risk assessment is of great significance in preventing,reversing and reducing the spread and the health hazards caused by stroke.Aiming to objectively predict and identify strokes,this paper proposes a new stroke risk assessment decision-making model named Logistic-AdaBoost(Logistic-AB)based on machine learning.First,the categorical boosting(CatBoost)method is used to perform feature selection for all features of stroke,and 8 main features are selected to form a new index evaluation system to predict the risk of stroke.Second,the borderline synthetic minority oversampling technique(SMOTE)algorithm is applied to transform the unbalanced stroke dataset into a balanced dataset.Finally,the stroke risk assessment decision-makingmodel Logistic-AB is constructed,and the overall prediction performance of this new model is evaluated by comparing it with ten other similar models.The comparison results show that the new model proposed in this paper performs better than the two single algorithms(logistic regression and AdaBoost)on the four indicators of recall,precision,F1 score,and accuracy,and the overall performance of the proposed model is better than that of common machine learning algorithms.The Logistic-AB model presented in this paper can more accurately predict patients’stroke risk.

A Shared Natural Neighbors Based-Hierarchical Clustering Algorithm for Discovering Arbitrary-Shaped Clusters

In clustering algorithms,the selection of neighbors significantly affects the quality of the final clustering results.While various neighbor relationships exist,such as K-nearest neighbors,natural neighbors,and shared neighbors,most neighbor relationships can only handle single structural relationships,and the identification accuracy is low for datasets with multiple structures.In life,people’s first instinct for complex things is to divide them into multiple parts to complete.Partitioning the dataset into more sub-graphs is a good idea approach to identifying complex structures.Taking inspiration from this,we propose a novel neighbor method:Shared Natural Neighbors(SNaN).To demonstrate the superiority of this neighbor method,we propose a shared natural neighbors-based hierarchical clustering algorithm for discovering arbitrary-shaped clusters(HC-SNaN).Our algorithm excels in identifying both spherical clusters and manifold clusters.Tested on synthetic datasets and real-world datasets,HC-SNaN demonstrates significant advantages over existing clustering algorithms,particularly when dealing with datasets containing arbitrary shapes.

The Spherical q-Linear Diophantine Fuzzy Multiple-Criteria Group Decision-Making Based on Differential Measure

Spherical q-linearDiophantine fuzzy sets(Sq-LDFSs)provedmore effective for handling uncertainty and vagueness in multi-criteria decision-making(MADM).It does not only cover the data in two variable parameters but is also beneficial for three parametric data.By Pythagorean fuzzy sets,the difference is calculated only between two parameters(membership and non-membership).According to human thoughts,fuzzy data can be found in three parameters(membership uncertainty,and non-membership).So,to make a compromise decision,comparing Sq-LDFSs is essential.Existing measures of different fuzzy sets do,however,can have several flaws that can lead to counterintuitive results.For instance,they treat any increase or decrease in the membership degree as the same as the non-membership degree because the uncertainty does not change,even though each parameter has a different implication.In the Sq-LDFSs comparison,this research develops the differentialmeasure(DFM).Themain goal of the DFM is to cover the unfair arguments that come from treating different types of FSs opposing criteria equally.Due to their relative positions in the attribute space and the similarity of their membership and non-membership degrees,two Sq-LDFSs formthis preference connectionwhen the uncertainty remains same in both sets.According to the degree of superiority or inferiority,two Sq-LDFSs are shown as identical,equivalent,superior,or inferior over one another.The suggested DFM’s fundamental characteristics are provided.Based on the newly developed DFM,a unique approach tomultiple criterion group decision-making is offered.Our suggestedmethod verifies the novel way of calculating the expert weights for Sq-LDFSS as in PFSs.Our proposed technique in three parameters is applied to evaluate solid-state drives and choose the optimum photovoltaic cell in two applications by taking uncertainty parameter zero.The method’s applicability and validity shown by the findings are contrasted with those obtained using various other existing approaches.To assess its stability and usefulness,a sensitivity analysis is done.

A NovelMethod for Determining Tourism Carrying Capacity in a Decision-Making Context Using q−Rung Orthopair Fuzzy Hypersoft Environment

Tourism is a popular activity that allows individuals to escape their daily routines and explore new destinations for various reasons,including leisure,pleasure,or business.A recent study has proposed a unique mathematical concept called a q−Rung orthopair fuzzy hypersoft set(q−ROFHS)to enhance the formal representation of human thought processes and evaluate tourism carrying capacity.This approach can capture the imprecision and ambiguity often present in human perception.With the advanced mathematical tools in this field,the study has also incorporated the Einstein aggregation operator and score function into the q−ROFHS values to supportmultiattribute decision-making algorithms.By implementing this technique,effective plans can be developed for social and economic development while avoiding detrimental effects such as overcrowding or environmental damage caused by tourism.A case study of selected tourism carrying capacity will demonstrate the proposed methodology.

UAV maneuvering decision-making algorithm based on deep reinforcement learning under the guidance of expert experience

Autonomous umanned aerial vehicle(UAV) manipulation is necessary for the defense department to execute tactical missions given by commanders in the future unmanned battlefield. A large amount of research has been devoted to improving the autonomous decision-making ability of UAV in an interactive environment, where finding the optimal maneuvering decisionmaking policy became one of the key issues for enabling the intelligence of UAV. In this paper, we propose a maneuvering decision-making algorithm for autonomous air-delivery based on deep reinforcement learning under the guidance of expert experience. Specifically, we refine the guidance towards area and guidance towards specific point tasks for the air-delivery process based on the traditional air-to-surface fire control methods.Moreover, we construct the UAV maneuvering decision-making model based on Markov decision processes(MDPs). Specifically, we present a reward shaping method for the guidance towards area and guidance towards specific point tasks using potential-based function and expert-guided advice. The proposed algorithm could accelerate the convergence of the maneuvering decision-making policy and increase the stability of the policy in terms of the output during the later stage of training process. The effectiveness of the proposed maneuvering decision-making policy is illustrated by the curves of training parameters and extensive experimental results for testing the trained policy.

A study on the influencing factors of rural tourism promoting households'shared prosperity in Yinan County of Shandong Province

This study examines the influencing factors of rural tourism in promoting shared prosperity among households,using the case study of Yinan County in Shandong province,China.The research aims to provide insights into the economic benefits experienced by low-income households in the context of rural tourism development.Based on field research conducted from April 8,2023,to May 17,2023,including interviews and site visits,the study constructs an evaluation index system to assess the influencing factors.Through expert scoring and the application of a weighted set-valued mathematical statistical model,the importance and quantification of each factor are determined.The results indicate that the current stage of promoting shared prosperity through rural tourism in Yinan County is primarily influenced by the scale of tourist consumption of external visitors.This factor outweighs others such as the participation capacity and willingness of low-income households,the capacity of investment enterprises,and the policies and financial allocation of the local government.The findings emphasize the significance of external visitor spending and highlight the need to address the obstacles faced by low-income households in accessing economic benefits.The evaluation index system and measurement model presented in this study offer a scientific basis for assessing and improving the income situation of low-income households in rural tourism development initiatives.However,it is crucial to note that the importance of these influencing factors may evolve with the lifecycle of the tourism destination.Future research should include long-term tracking and consider a broader range of indicators to deepen our understanding of their mechanisms and significance.Furthermore,alternative approaches and theories may provide valuable perspectives for further exploration in this field.

Youth Exchange“Harmony for Shared Prosperity”

Contemporary World Magazine,which is affiliated to the International Department of the CPC Central Committee(IDCPC),held an exchange act ivi ty themed on“Harmony for Shared Prosperity”in Beijing on May 7,2024.

Decision-Making and Management of Self-Care in Persons with Traumatic Spinal Cord Injuries: A Preliminary Study

Patients and physicians understand the importance of self-care following spinal cord injury (SCI), yet many individuals with SCI do not adhere to recommended self-care activities despite logistical supports. Neurobehavioral determinants of SCI self-care behavior, such as impulsivity, are not widely studied, yet understanding them could inform efforts to improve SCI self-care. We explored associations between impulsivity and self-care in an observational study of 35 US adults age 18 - 50 who had traumatic SCI with paraplegia at least six months before assessment. The primary outcome measure was self-reported self-care. In LASSO regression models that included all neurobehavioral measures and demographics as predictors of self-care, dispositional measures of greater impulsivity (negative urgency, lack of premeditation, lack of perseverance), and reduced mindfulness were associated with reduced self-care. Outcome (magnitude) sensitivity, a latent decision-making parameter derived from computationally modeling successive choices in a gambling task, was also associated with self-care behavior. These results are preliminary;more research is needed to demonstrate the utility of these findings in clinical settings. Information about associations between impulsivity and poor self-care in people with SCI could guide the development of interventions to improve SCI self-care and help patients with elevated risks related to self-care and secondary health conditions.

Joint Management of Shared Resources, Issues and Conflicts of the Nso and Oku Tribes in the Ngongbaa Montane Forest Area, NWR of Cameroon

Wherever people live together in or near homelands that harbour joint natural resources such as forest, water, minerals, oil etc., they must collaborate, co-exist and share these resources irrespective of their genetic, social, cultural, and political peculiarities. This is not always the case when self-interest, ethnic divisions, elitist politics, economic interest and power tussle set in. The Nso and Oku people who reside in the Ngongbaa Forest area constitute an example of joint management of resources where at one period, their relationship over the management of the Oku Mountain Forest was cordial and at another, the relationship became conflictive. This study was designed to study the influence of the KIFP forest management policies on the Nso indigenous forest Management system in Ngongbaa Forest, and its implications. The data collected for the study was secured through the administration of a questionnaire to residents of 10 villages adjacent to the Ngongbaa Forest, and forest users who carried out livelihood activities there between 1963 and 2015. These forest user groups include beekeepers, rat trappers, grazers, mushroom gatherers, ground honey/tree honey harvesters, carvers, etc. The investigation also extended to the traditional leaders of Nso and Oku who are in charge of the land tenure systems. The findings show that the Oku Mountain Forest is jointly owned by the Nso and Oku people. The Nso own the eastern half of the forest known as Ngongbaa Forest while the Oku own the western half, known as the Kilum Forest. The cordial relationship that existed between these people ensured the conservation of the forest. But the conflicts that later set in, especially due to the creation of the Kilum Mountain Forest Project in 1987 later changed to the Kilum Ijim Forest Project (KIFP) in 1992 sidelined the local communities which was contrary to the dispositions of the new Cameroon forest law enacted in 1994. Thus, the indigenous conservation practices became ignored which the local population never digested well. This seriously jeopardized the wellbeing of the forest in general.

An Exploration of the Three Dimensions and Epochal Strengths of Building a Human Community With a Shared Future

The idea of a human community with a shared future was proposed by the Communist Party of China(CPC)Central Committee with Comrade Xi Jinping at its core for the future development of human beings to face up to the most important question in today's world:“What is happening to the world and what should we do?”It profoundly answers the question of the world,history,and the times.The theory of a human community with a shared future is an innovative theory with a multidimensional formation logic that guides humanity toward continually seeking common interests and values.This paper dives into the profound motivations behind building a human community with a shared future from historical,cultural,and practical dimensions and analyzes its epochal value from both domestic and international perspectives.This not only helps exert China's role in the international community,contributing Chinese strength to the construction of a peaceful,stable,and prosperous human society,but also enhances the influence of the idea of a human community with a shared future in the international community,accelerating the building of a human community with a shared future that considers the legitimate concerns of all countries,and aiding in solving the crises facing the world.