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 Feature-Aided Multiple Model Algorithm for Maneuvering Target Tracking

Dear Editor,This letter deals with the tracking problem for non-cooperative maneuvering targets based on the underwater sensor networks. Considering the acoustic intensity feature of underwater targets, a feature-aided multi-model tracking method for maneuvering targets is proposed.

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.

RepBoTNet-CESA:An Alzheimer’s Disease Computer Aided Diagnosis Method Using Structural Reparameterization BoTNet and Cubic Embedding Self Attention

Various deep learning models have been proposed for the accurate assisted diagnosis of early-stage Alzheimer’s disease(AD).Most studies predominantly employ Convolutional Neural Networks(CNNs),which focus solely on local features,thus encountering difficulties in handling global features.In contrast to natural images,Structural Magnetic Resonance Imaging(sMRI)images exhibit a higher number of channel dimensions.However,during the Position Embedding stage ofMulti Head Self Attention(MHSA),the coded information related to the channel dimension is disregarded.To tackle these issues,we propose theRepBoTNet-CESA network,an advanced AD-aided diagnostic model that is capable of learning local and global features simultaneously.It combines the advantages of CNN networks in capturing local information and Transformer networks in integrating global information,reducing computational costs while achieving excellent classification performance.Moreover,it uses the Cubic Embedding Self Attention(CESA)proposed in this paper to incorporate the channel code information,enhancing the classification performance within the Transformer structure.Finally,the RepBoTNet-CESA performs well in various AD-aided diagnosis tasks,with an accuracy of 96.58%,precision of 97.26%,and recall of 96.23%in the AD/NC task;an accuracy of 92.75%,precision of 92.84%,and recall of 93.18%in the EMCI/NC task;and an accuracy of 80.97%,precision of 83.86%,and recall of 80.91%in the AD/EMCI/LMCI/NC task.This demonstrates that RepBoTNet-CESA delivers outstanding outcomes in various AD-aided diagnostic tasks.Furthermore,our study has shown that MHSA exhibits superior performance compared to conventional attention mechanisms in enhancing ResNet performance.Besides,the Deeper RepBoTNet-CESA network fails to make further progress in AD-aided diagnostic tasks.

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.

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.

A Blind Spot in the Reframing of a Universe of Possibles: Towards a Suitable Model for Decision-Making Theory and A.I.

Bayesian inference model is an optimal processing of incomplete information that, more than other models, better captures the way in which any decision-maker learns and updates his degree of rational beliefs about possible states of nature, in order to make a better judgment while taking new evidence into account. Such a scientific model proposed for the general theory of decision-making, like all others in general, whether in statistics, economics, operations research, A.I., data science or applied mathematics, regardless of whether they are time-dependent, have in common a theoretical basis that is axiomatized by relying on related concepts of a universe of possibles, especially the so-called universe (or the world), the state of nature (or the state of the world), when formulated explicitly. The issue of where to stand as an observer or a decision-maker to reframe such a universe of possibles together with a partition structure of knowledge (i.e. semantic formalisms), including a copy of itself as it was initially while generalizing it, is not addressed. Memory being the substratum, whether human or artificial, wherein everything stands, to date, even the theoretical possibility of such an operation of self-inclusion is prohibited by pure mathematics. We make this blind spot come to light through a counter-example (namely Archimedes’ Eureka experiment) and explore novel theoretical foundations, fitting better with a quantum form than with fuzzy modeling, to deal with more than a reference universe of possibles. This could open up a new path of investigation for the general theory of decision-making, as well as for Artificial Intelligence, often considered as the science of the imitation of human abilities, while being also the science of knowledge representation and the science of concept formation and reasoning.

Crossing the Achilles Heel of Algorithms:Identifying the Developmental Dilemma of Artificial Intelligence-Assisted Judicial Decision-Making

In the developmental dilemma of artificial intelligence(AI)-assisted judicial decision-making,the technical architecture of AI determines its inherent lack of transparency and interpretability,which is challenging to fundamentally improve.This can be considered a true challenge in the realm of AI-assisted judicial decision-making.By examining the court’s acceptance,integration,and trade-offs of AI technology embedded in the judicial field,the exploration of potential conflicts,interactions,and even mutual shaping between the two will not only reshape their conceptual connotations and intellectual boundaries but also strengthen the cognition and re-interpretation of the basic principles and core values of the judicial trial system.

Artificial intelligence for characterization of diminutive colorectal polyps:A feasibility study comparing two computer-aided diagnosis systems

BACKGROUND Artificial intelligence(AI)has potential in the optical diagnosis of colorectal polyps.AIM To evaluate the feasibility of the real-time use of the computer-aided diagnosis system(CADx)AI for ColoRectal Polyps(AI4CRP)for the optical diagnosis of diminutive colorectal polyps and to compare the performance with CAD EYE^(TM)(Fujifilm,Tokyo,Japan).CADx influence on the optical diagnosis of an expert endoscopist was also investigated.METHODS AI4CRP was developed in-house and CAD EYE was proprietary software provided by Fujifilm.Both CADxsystems exploit convolutional neural networks.Colorectal polyps were characterized as benign or premalignant and histopathology was used as gold standard.AI4CRP provided an objective assessment of its characterization by presenting a calibrated confidence characterization value(range 0.0-1.0).A predefined cut-off value of 0.6 was set with values<0.6 indicating benign and values≥0.6 indicating premalignant colorectal polyps.Low confidence characterizations were defined as values 40%around the cut-off value of 0.6(<0.36 and>0.76).Self-critical AI4CRP’s diagnostic performances excluded low confidence characterizations.RESULTS AI4CRP use was feasible and performed on 30 patients with 51 colorectal polyps.Self-critical AI4CRP,excluding 14 low confidence characterizations[27.5%(14/51)],had a diagnostic accuracy of 89.2%,sensitivity of 89.7%,and specificity of 87.5%,which was higher compared to AI4CRP.CAD EYE had a 83.7%diagnostic accuracy,74.2%sensitivity,and 100.0%specificity.Diagnostic performances of the endoscopist alone(before AI)increased nonsignificantly after reviewing the CADx characterizations of both AI4CRP and CAD EYE(AI-assisted endoscopist).Diagnostic performances of the AI-assisted endoscopist were higher compared to both CADx-systems,except for specificity for which CAD EYE performed best.CONCLUSION Real-time use of AI4CRP was feasible.Objective confidence values provided by a CADx is novel and self-critical AI4CRP showed higher diagnostic performances compared to AI4CRP.

“零转介”AIDS诊疗管理模式的临床验证

目的探讨“零转介”诊疗管理模式对人类免疫缺陷病毒(HIV)感染患者治疗疗效和满意度的效果。方法选取石家庄市第五医院2022年7月至12月新增HIV感染者100例,随机分为对照组和研究组,每组50例。对照组给予常规认知干预和健康指导、心理干预,研究组在确诊前较对照组提前7~10 d即给予认知干预、生活指导和心理干预。比较对照组和研究组患者的HIV感染患者睡眠[匹兹堡睡眠质量指数表(PSQI)]、服药依从性、焦虑抑郁量表分值。结果对照组患者的PSQⅠ评分高于研究组患者(P<0.01);研究组患者的焦虑抑郁的心理状况要优于对照组(P<0.05);对照组患者服药依从性良好者比例低于研究组患者(P<0.01);研究组患者对疾病的接受度和认知评定量表均好于对照组患者(P<0.01)。结论“零转介”模式,提前给予认知干预、生活指导和心理干预,可提高HIV感染患者满意度,对社会层面的AIDS预防具有积极意义。

HIV/AIDS肺脾气虚证相关心脑血管疾病风险因素探析

目的通过检测血脂四项、血管内皮损伤因子、炎症相关因子,并结合前期的基因芯片结果,初步探讨人类免疫缺陷病毒(HIV)/艾滋病(AIDS)肺脾气虚证相关心脑血管疾病风险因素及机制,为中医药早期干预心脑血管疾病提供依据。方法2020年9月—2020年11月选取河南省某地区确诊的20例HIV/AIDS肺脾气虚证患者作为研究对象,同地区20例HIV抗体阴性作为健康对照组。检测CD_(3)^(+)、CD_(4)^(+)、CD_(8)^(+)计数,并计算CD_(4)^(+)/CD_(8)^(+)比值;检测血脂四项甘油三酯(TG)、胆固醇(TC)、高密度脂蛋白胆固醇(HDL-C)、低密度脂蛋白胆固醇(LDL-C);检测氧化低密度脂蛋白(Ox-LDL)、载脂蛋白A-I(ApoAⅠ)、载脂蛋白A-Ⅱ(ApoAⅡ)、载脂蛋白B(ApoB)、锌-α2-糖蛋白1(AZGP1)、可溶性血栓调节蛋白(sTM)、血管性血友病因子(vWF)、脂联素、瘦素(LEP)、超敏C反应蛋白(Hs-CRP)、单核细胞趋化因子1(MCP-1)、肿瘤坏死因子α(TNF-α)。结果HIV/AIDS肺脾气虚证患者与健康对照组比较CD_(4)^(+)降低,CD_(4)^(+)/CD_(8)^(+)比值倒置,TC降低,sTM、AZGP1、Ox-LDL、Hs-CRP升高。结论HIV/AIDS肺脾气虚证患者存在炎性反应、血脂异常的表现,罹患心脑血管疾病风险增加。为中医药早期干预心脑血管疾病的发生提供新思路。

HIV/AIDS合并肝损伤患者的临床特点

目的总结HIV/AIDS合并肝损伤患者的临床特点。方法选取53例HIV抗体阳性合并肝损伤患者作为研究对象,根据肝脏组织病理结果分为病毒性肝炎组(n=27)、肝细胞肝癌组(n=15)和肝脏弥漫大B细胞淋巴瘤组(n=11)。比较三组患者的临床症状和腹部CT影像学特征,以及肝脏组织病理学和免疫表型。结果三组在消瘦、发热、上腹痛、浅表淋巴结肿大、丙氨酸转氨酶升高、碱性磷酸酶升高、甲胎蛋白升高方面差异均有统计学意义;三组在多发结节、单发结节、病灶不均匀强化、病灶轻度强化、病灶重度强化方面差异均有统计学意义(均P<0.05)。病毒性肝炎组镜下见肝小叶结构破坏,肝细胞肝癌组镜下见凝固性坏死组织,弥漫大B细胞淋巴瘤组镜下见肝小叶结构广泛破坏和星空现象。结论不同类型HIV/AIDS合并肝损伤患者临床症状、实验室检查结果、腹部CT影像学特征有一定差异,但其较难区分肝细胞癌和弥漫大B细胞淋巴瘤,两者鉴别诊断需借助肝脏组织病理学和免疫表型方法。

2018-2022年南阳市HIV/AIDS抗病毒治疗及时性及其影响因素分析

目的 分析南阳市新报告的艾滋病感染者/艾滋病患者(简称HIV/AIDS)抗病毒治疗及时性及其影响因素,为提高其抗病毒治疗及时性提供参考。方法 从“艾滋病防治基本信息系统”中下载截至2023年6月30日的HIV/AIDS病例资料,收集确认时间为2018年1月1日-2022年12月31日、现住址为南阳市的个案信息,分析其基本信息和及时治疗的情况,采用χ^(2)检验和多因素logistic回归模型分析抗病毒治疗及时性的影响因素。结果 2018-2022年共纳入符合条件HIV/AIDS病例2 655例,平均年龄为(41.69±16.17)岁,性传播占99.10%,已婚有配偶占41.21%,样本来源为医疗机构被动检测占60.23%,无症状期占58.15%;抗病毒治疗及时率为65.73%(1 745/2 655),五年间抗病毒治疗及时率无明显变化趋势(χ^(2)=0.308,P=0.579)。χ^(2)检验结果提示,不同确认年龄、职业、婚姻状况、教育程度、样本来源、临床分期、报告地区的HIV/AIDS病例抗病毒治疗及时率的差异均有统计学意义(P值均<0.05)。多因素logistic回归模型结果显示,与职业为农民者相比,职业为家政家务及待业(OR=0.611,95%CI:0.477-0.782)和商业服务(OR=0.736,95%CI:0.546-0.994)的病例抗病毒及时治疗的可能性更小;与外地报告的病例相比,本市报告的病例抗病毒及时治疗的可能性更大(OR=3.650,95%CI:2.966-4.491);与临床分期为艾滋病期的病例相比,无症状期、轻度疾病期和中度疾病期的及时治疗的可能性更大,OR值(95%CI)分别为2.212(1.648-2.969)、2.446(1.767-3.386)和1.496(1.046-2.140)。结论 南阳市HIV/AIDS抗病毒治疗及时性有待提高,需根据职业类型、报告地区以及疾病分期状况等情况采取针对性综合干预措施,确认阳性后及时随访、尽量简化转介治疗流程,提高治疗覆盖面和及时治疗比例。

广州市海珠区HIV/AIDS患者抑郁、焦虑和社会支持状况相关分析

目的了解广州市海珠区人类免疫缺陷病毒(HIV)感染者/艾滋病(AIDS)患者(简称HIV/AIDS)抑郁、焦虑和社会支持现状及相关因素,为群体AIDS的防治工作提供参考。方法采用横断面研究方法,选取2019年10月至2022年6月广州市海珠区HIV/AIDS患者363例为研究对象进行问卷调查。使用自行设计的问卷,收集其人口学、抑郁、焦虑、社会支持、性行为等信息,分析HIV/AIDS患者抑郁、焦虑和社会支持状况。采用独立样本t检验、方差分析、秩和检验。结果本研究共调查HIV/AIDS患者363例,年龄(39.38±11.94)岁,男性占70.25%(255/363)、已婚占46.83%(170/363)、初中文化程度占36.91%(134/363)、工人占30.03%(109/363)。HIV/AIDS患者抑郁自评总分为(52.67±7.84)分,45.07%(160/355)的患者存在抑郁情绪,其中轻度居多[37.18%(132/355)];焦虑自评总分为(51.84±6.64)分,主要为轻度焦虑[47.92%(173/361)]。HIV/AIDS患者社会支持总分为(22.87±7.79)分,其中客观支持得分为(6.45±3.00)分,主观支持得分为(16.42±4.79)分,对支持的利用度得分为(5.74±1.84)分。结论广州市海珠区HIV/AIDS患者存在部分心理健康问题,社会支持普遍较低,应加强该人群的心理健康辅导,增加对该人群的社会支持,尤其是客观支持和主观支持。