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.

Factors influencing the continuity of evidence-based practice in perioperative airway management for elderly patients with fractures:A qualitative study

BACKGROUND More and more evidence-based practices are emerging,but researchers mostly focus on short-term effects,resulting in evidence-based practices not being applied in the clinic in the long term.In this study,we took the evidence-based practice of perioperative airway management in elderly fracture patients as an example and adopted a descriptive phenomenological approach to understand the influencing factors of its sustainability to provide a reference basis for promoting the continuity of evidence-based practice in the clinic.AIM To explore factors influencing the persistence of evidence-based practice in perioperative airway management in elderly patients with fractures.METHODS This study was qualitative research.Nine nurses who implemented evidencebased practice in the orthopedic ward of a tertiary comprehensive hospital in Shanghai from September 2023 to October 2023 were selected using purposive sampling as research subjects.Semi-structured interviews were conducted with them,and the data were analyzed using the Colaizzi phenomenological analysis method based on the three dimensions and ten factors of the NHS sustainability model.RESULTS Three main themes and ten subthemes were identified:Process aspects(benefits to patients,benefits to nurses,lack of follow-up,complex processes);staff aspects(insufficient human resources,inadequate training and education,lack of leadership support);and organizational environment aspects(inadequate infrastructure,poor patient compliance,poor doctor cooperation).CONCLUSION Human resources,training and education,leadership support,infrastructure,and patient-physician collaboration are important factors influencing the sustainability of evidence-based practice for perioperative airway management in older patients with fractures.

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.

Determination of the best materials for development and designing product using a multi-criteria decision-making

Purpose–Material selection,driven by wide and often conflicting objectives,is an important,sometimes difficult problem in material engineering.In this context,multi-criteria decision-making(MCDM)methodologies are effective.An approach of MCDM is needed to cater to criteria of material assortment simultaneously.More firms are now concerned about increasing their productivity using mathematical tools.To occupy a gap in the previous literature this research recommends an integrated MCDM and mathematical Bi-objective model for the selection of material.In addition,by using the Technique for Order Preference by Similarity to Ideal Solution(TOPSIS),the inherent ambiguities of decision-makers in paired evaluations are considered in this research.It goes on to construct a mathematical bi-objective model for determining the best item to purchase.Design/methodology/approach–The entropy perspective is implemented in this paper to evaluate the weight parameters,while the TOPSIS technique is used to determine the best and worst intermediate pipe materials for automotive exhaust system.The intermediate pipes are used to join the components of the exhaust systems.The materials usually used to manufacture intermediate pipe are SUS 436LM,SUS 430,SUS 304,SUS 436L,SUH 409 L,SUS 441 L and SUS 439L.These seven materials are evaluated based on tensile strength(TS),hardness(H),elongation(E),yield strength(YS)and cost(C).A hybrid methodology combining entropy-based criteria weighting,with the TOPSIS for alternative ranking,is pursued to identify the optimal design material for an engineered application in this paper.This study aims to help while filling the information gap in selecting the most suitable material for use in the exhaust intermediate pipes.After that,the authors searched for and considered eight materials and evaluated them on the following five criteria:(1)TS,(2)YS,(3)H,(4)E and(5)C.The first two criteria have been chosen because they can have a lot of influence on the behavior of the exhaust intermediate pipes,on their performance and on the cost.In this structure,the weights of the criteria are calculated objectively through the entropy method in order to have an unbiased assessment.This essentially measures the quantity of information each criterion contribution,indicating the relative importance of these criteria better.Subsequently,the materials were ranked using the TOPSIS method in terms of their relative performance by measuring each material from an ideal solution to determine the best alternative.The results show that SUS 309,SUS 432L and SUS 436 LM are the first three materials that the exhaust intermediate pipe optimal design should consider.Findings–The material matrix of the decision presented in Table 3 was normalized through Equation 5,as shown in Table 5,and the matrix was multiplied with weighting criteriaß_j.The obtained weighted normalized matrix V_ij is presented in Table 6.However,the ideal,worst and best value was ascertained by employing Equation 7.This study is based on the selection of material for the development of intermediate pipe using MCDM,and it involves four basic stages,i.e.method of translation criteria,screening process,method of ranking and search for methods.The selection was done through the TOPSIS method,and the criteria weight was obtained by the entropy method.The result showed that the top three materials are SUS 309,SUS 432L and SUS 436 LM,respectively.For the future work,it is suggested to select more alternatives and criteria.The comparison can also be done by using different MCDM techniques like and Choice Expressing Reality(ELECTRE),Decision-Making Trial and Evaluation Laboratory(DEMATEL)and Preference Ranking Organization Method for Enrichment Evaluation(PROMETHEE).Originality/value–The results provide important conclusions for material selection in this targeted application,verifying the employment of mutual entropy-TOPSIS methodology for a series of difficult engineering decisions in material engineering concepts that combine superior capacity with better performance as well as cost-efficiency in various engineering design.

Evidence-based orthobiologic practice:Current evidence review and future directions

The field of orthopedic and regenerative medicine is rapidly evolving with the increasing utilization of orthobiologic.These biologically derived therapies,inc-luding platelet-rich plasma,mesenchymal stem cells,bone marrow aspirate con-centrate,stromal vascular fraction(SVF),and autologous chondrocyte implanta-tion,are gaining traction for their potential to enhance the body's natural healing processes.They offer a promising alternative to traditional surgical interventions for musculoskeletal injuries and degenerative conditions.Current evidence su-ggests significant benefits of orthobiologics in treating conditions like osteoarth-ritis,tendon injuries,and spinal disorders,yet inconsistencies in treatment pro-tocols and outcomes persist.The global market for orthobiologics is projected to grow substantially,driven by advancements in biologic therapies such as adipose-derived stem cells and SVF,and the demand for minimally invasive treatments.Despite their promise,regulatory and ethical challenges,as well as the need for high-quality,standardized research,remain significant obstacles.Future dire-ctions in the field include advancements in delivery systems,personalized medi-cine approaches,and the exploration of novel sources like induced pluripotent stem cells,aiming for more targeted and effective treatments.Collaborative efforts are crucial to overcoming these challenges and ensuring the safe and effective application of orthobiologics in clinical practice.

Effect of cetuximab plus FOLFOX4 regimen on clinical outcomes in advanced gastric carcinoma patients receiving evidence-based care

BACKGROUND Although chemotherapy is effective for treating advanced gastric carcinoma(aGC),it may lead to an adverse prognosis.Establishing a highly effective and low-toxicity chemotherapy regimen is necessary for improving efficacy and outcomes in aGC patients.AIM To determine the efficacy and safety of cetuximab(CET)combined with the FOLFOX4 regimen(infusional fluorouracil,folinic acid,and oxaliplatin)as firstline therapy for patients with aGC,who received evidence-based care(EBC).METHODS A total of 117 aGC patients who received EBC from March 2019 to March 2022 were enrolled.Of these,60 in the research group(RG)received CET+FOLFOX4 as first-line therapy,whereas 57 in the control group(CG)received FOLFOX4.The efficacy[clinical response rate(RR)and disease control rate(DCR)],safety(liver and kidney dysfunction,leukopenia,thrombocytopenia,rash,and diarrhea),serum tumor marker expression[STMs;carbohydrate antigen(CA)19-9,CA72-4,and carcinoembryonic antigen(CEA)],inflammatory indicators[interleukin(IL)-2 and IL-10],and quality of life(QOL)of the two groups were compared.RESULTS A markedly higher RR and DCR were observed in the RG compared with the CG,with an equivalent safety profile between the two groups.RG exhibited notably reduced CA19-9,CA72-4,CEA,and IL-2 levels following treatment,which were lower than the pre-treatment levels and those in the CG.Post-treatment IL-10 was statistically increased in RG,higher than the pre-treatment level and the CG.Moreover,a significantly improved QOL was evident in the RG.CONCLUSION The CET+FOLFOX4 regimen is highly effective as first-line treatment for aGC patients receiving EBC.It facilitates the suppression of STMs,ameliorates the serum inflammatory microenvironment,and enhances QOL,without increased adverse drug effects.

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.

Evidence-Based Nursing Practice of Reducing Immune-Related Skin Toxicity of Tumor Patients Guided by Sensitive Indicators

Purpose research on nursing sensitive indicators in tumor Patients application effect in immune-related skin toxicity management. Method select our hospital April to June, 202360 cases patients with immune therapy settings as the control group. August-October, 2023 60 cases the patients treated with immune therapy were the experimental group. The control group adopted regular nursing methods, while the experimental group sensitive Indicators, evidence-based give preventive care. The social situation, psychological state, physical function, quality of life score, incidence of skin toxicity caused by immune checkpoint inhibitors, moderate and above of the two groups of patients were compared. Incidence of skin toxicity. Result: experience group SAS score, SDS score higher than the control group, the difference was statistically significant (P < 0.05);The incidence of skin toxic reactions caused by immune checkpoint inhibitors and the incidence of moderate and above skin toxic reactions in the experimental group are lower than those in the control group, and the difference is statistically significant (P < 0.05). Conclusion: sensitive indicator guidance evidence-based preventive care can reduce the degree of immune-related skin toxicity, improve the psychological state and quality of life of tumor patients treated with immune therapy and reduce the incidence of adverse reactions, improve nursing quality and patient satisfaction.

Clinical Practice of Evidence-Based PDCA Cycle Management Model in Accelerated Recovery of Lung Cancer Patients

Objective: To explore the nursing effect of evidence-based PDCA cycle management mode in accelerated rehabilitation of patients undergoing thoracoscopic lung cancer radical surgery. Methods: 104 patients who underwent thoracoscopic lung cancer radical surgery in our hospital from June 2022 to February 2023 were randomly divided into control group and intervention group, with 52 cases in each group. The control group implemented evidence-based ERAS clinical pathway management, while the intervention group implemented evidence-based PDCA cycle quality management. The postoperative recovery of the two groups of patients was compared. Results: The postoperative recovery of the intervention group was significantly better than that of the control group. The first time to get out of bed, the first time to eat, the duration of chest drainage tube placement, and the length of hospital stay were significantly shorter than those of the control group. The incidence of postoperative chest complications and hospitalization costs were significantly lower than those of the control group, and patient satisfaction was significantly higher than that of the control group (P Conclusion: Evidence-based PDCA cycle quality management mode can effectively improve the implementation quality of accelerated rehabilitation nursing for patients undergoing thoracoscopic lung cancer radical surgery, and it is worthy of clinical promotion.

Influence of social media on maternal decision-making and breastfeeding practices

Breastfeeding practices are influenced by multifactorial determinants including individual characteristics,external support systems,and media influences.This commentary emphasizes such complex factors influencing breastfeeding practices.Potential methodological limitations and the need for diverse sampling in studying breastfeeding practices are highlighted.Further research must explore the interplay between social influences,cultural norms,government policies,and individual factors in shaping maternal breastfeeding decisions.

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.

Evidence-based Clinical Chinese Medicine:What Has Changed Over the Past 20 Years

Chinese medicine has been used for centuries to treat a range of health conditions.This history has produced a wealth of classical literature,case studies and clinical research data detailing its use and effectiveness.However,high-quality and conclusive evidence that meets modern requirements for clinical decision support is lacking.This evidence gap limits the integration of Chinese medicine with contemporary medicine,which in turn limits global access and acceptance of Chinese medicine as a form of safe and effective health care.Over the past 20 years,researchers and organisations around the world,including the World Health Organization(WHO)and United Nations,have worked to support the integration of traditional medicines,such as Chinese medicine,with conventional medicines to improve global health care.This paper provides an overview of Chinese medicine studies published in the top four general medical journals(BMJ,JAMA,Lancet and New England Journal of Medicine)from February 2005 to February 2024 in the past 20 years to highlight the progress in the development of this evidence base.It also highlights key actions taken to promote evidence-based clinical Chinese medicine,including product and practitioner regulation,formalising education standards,and international collaborations.Research conducted at the China-Australia International Research Centre for Chinese Medicine demonstrates the benefits of such a collaboration.Through development of its unique and inclusive‘whole-evidence’approach,plus clinical studies and systematic reviews,the Centre has significantly contributed to the evidence base for clinical Chinese medicine.In addition,its high-impact papers and groundbreaking monographs have been cited in international conventional medicine guidelines.While progress has certainly been made during the past 20 years to build a stronger evidence base for clinical Chinese medicine,there is still a considerable gap that limits its integration with conventional medicine.Future funding and research are needed to continue this work and achieve to safe,effective and accessible traditional medicine as part of the WHO’s Universal Health Coverage strategy.

Evidence-Based Design Application of Water Landscaping for Rehabilitation Gardens for Patients with Mental Disorders

The aim of the article is to explore the influence of the water landscape design of a rehabilitation garden for patients with mental disorders on the recovery effect of patients,intending to provide a better rehabilitation environment for patients with mental disorders.Based on literature research,this article reviews three aspects of evidence-based design theory,the concept of water landscape in rehabilitation gardens,and the types of water features in rehabilitation gardens.The results show that well-designed water features can significantly improve patients’psychological state and reduce anxiety and stress,and that water landscape design in rehabilitation gardens is an effective rehabilitation tool that can facilitate the recovery process of patients with mental disorders.Future designs should take into full consideration patients’needs and preferences,as well as best practices in waterscape design,to maximize its positive impact on patients’recovery.

Research on Public Engineering Emergency Decision-Making Based on Multi-Granularity Language Information

To effectively deal with fuzzy and uncertain information in public engineering emergencies,an emergency decision-making method based on multi-granularity language information is proposed.Firstly,decision makers select the appropriate language phrase set according to their own situation,give the preference information of the weight of each key indicator,and then transform the multi-granularity language information through consistency.On this basis,the sequential optimization technology of the approximately ideal scheme is introduced to obtain the weight coefficient of each key indicator.Subsequently,the weighted average operator is used to aggregate the preference information of each alternative scheme with the relative importance of decision-makers and the weight of key indicators in sequence,and the comprehensive evaluation value of each scheme is obtained to determine the optimal scheme.Lastly,the effectiveness and practicability of the method are verified by taking the earthwork collapse accident in the construction of a reservoir as an example.

Improving the Selection Model of Essential Medicines in China by Evidence-based Thinking

Objective To sort out the problems in the selection model of essential medicines in China,and to put forward some suggestions to optimize the selection model to ensure the rationality of the medicines in the essential medicines list.Methods The domestic and foreign literature on the selection of essential medicines was reviewed.Then,the opinions on the selection methods and results of essential medicines from foreign literature were investigated to make a comparison with the selection of essential medicines in China.Finally,the general and special laws of the selection process of the essential medicines were explored.Results and Conclusion It is recommended that the selection model of essential medicines in China should be further optimized,and the evidence-based thinking and methods should be utilized to enhance the scientific nature of the selection process.