Across four studies,we explore the impact of solitude on consumers’reliance on feelings versus reasons in decision making,along with the underlying mechanism and boundary conditions.The results indicate that solitude...Across four studies,we explore the impact of solitude on consumers’reliance on feelings versus reasons in decision making,along with the underlying mechanism and boundary conditions.The results indicate that solitude individuals(vs.non-solitude)would prefer feeling-based strategy in decision-making,resulting in a higher intention of choosing the affectively superior option over the cognitively superior option(Study 1).Self-focus plays the underlying mechanism in the solitude effect(Study 2).Moreover,we also examine two boundary conditions:motivation(Study 3)and temporal orientation(Study 4),which indicates that involuntary motivation and future orientation can mitigate the solitude effect on affective processing.These findings provide insights into consumers’judgments of product attributes and selection of decision-making strategies according to their situations.展开更多
Traditional Chinese medicine(TCM)represents a paradigmatic approach to personalized medicine,developed through the systematic accumulation and refinement of clinical empirical data over more than 2000 years,and now en...Traditional Chinese medicine(TCM)represents a paradigmatic approach to personalized medicine,developed through the systematic accumulation and refinement of clinical empirical data over more than 2000 years,and now encompasses large-scale electronic medical records(EMR)and experimental molecular data.Artificial intelligence(AI)has demonstrated its utility in medicine through the development of various expert systems(e.g.,MYCIN)since the 1970s.With the emergence of deep learning and large language models(LLMs),AI’s potential in medicine shows considerable promise.Consequently,the integration of AI and TCM from both clinical and scientific perspectives presents a fundamental and promising research direction.This survey provides an insightful overview of TCM AI research,summarizing related research tasks from three perspectives:systems-level biological mechanism elucidation,real-world clinical evidence inference,and personalized clinical decision support.The review highlights representative AI methodologies alongside their applications in both TCM scientific inquiry and clinical practice.To critically assess the current state of the field,this work identifies major challenges and opportunities that constrain the development of robust research capabilities—particularly in the mechanistic understanding of TCM syndromes and herbal formulations,novel drug discovery,and the delivery of high-quality,patient-centered clinical care.The findings underscore that future advancements in AI-driven TCM research will rely on the development of high-quality,large-scale data repositories;the construction of comprehensive and domain-specific knowledge graphs(KGs);deeper insights into the biological mechanisms underpinning clinical efficacy;rigorous causal inference frameworks;and intelligent,personalized decision support systems.展开更多
Decision Support Tool(DST)enables farmers to make site-specific crop management decisions;however,comprehensive calibration can be both costly and time-consuming.This study assessed the production and economic benefit...Decision Support Tool(DST)enables farmers to make site-specific crop management decisions;however,comprehensive calibration can be both costly and time-consuming.This study assessed the production and economic benefits of two calibrations of the Nutrient Expert(NE)tool for rice in Sri Lanka’s Alfisols:the basic calibration(Nutrient Expert Sri Lanka 1,NESL1)and the comprehensive calibration(Nutrient Expert Sri Lanka 2,NESL2).NESL1 was developed by adapting the South Indian version of NE to local conditions,while NESL2 was an updated version,using three years of data from 71 farmer fields.展开更多
People have been engaged in sports activities both individually and collectively for years.Sports consumption,which refers to the process that covers many issues related to sports in the form of playing,watching,liste...People have been engaged in sports activities both individually and collectively for years.Sports consumption,which refers to the process that covers many issues related to sports in the form of playing,watching,listening or reading,is a form of human behavior.The satisfaction of the four marketing components of product,price,distribution and promotion by using the leisure time of the sports consumer effectively and ensuring its continuity in the future process can be ensured by effective utilization of facilities and quality recreation activities.Consumer behaviors,which have a very complex structure,are seen in the form of choosing,buying,using and obtaining.With this study,it is aimed to determine the mediating role of consumer decision-making styles in determining the effect of marketing components in the consumption of sports activities on the satisfaction of sports consumers.In this direction,data were collected in the province of Istanbul,which was determined as the sample.Data were obtained with a questionnaire form created on Google Form.These data were analyzed in line with the model and hypotheses created with these data and it was determined that the marketing components of sports consumption have an impact on the sports consumer and it was concluded that consumer decision-making styles have a positive mediating effect in this regard.展开更多
The satellite orbital pursuit game focuses on studying spacecraft maneuvering strategies in space.Traditional numerical methods often face real-time inadequacies and adaptability limitations when dealing with highly n...The satellite orbital pursuit game focuses on studying spacecraft maneuvering strategies in space.Traditional numerical methods often face real-time inadequacies and adaptability limitations when dealing with highly nonlinear problems.With the advancement of Deep Reinforcement Learning(DRL)technology,continuous-time orbital control capabilities have significantly improved.Despite this,the existing DRL technologies still need adjustments in action delay and discretization structure to better adapt to practical application scenarios.Combining continuous learning and model planning demonstrates the adaptability of these methods in continuous-time decision problems.Additionally,to more effectively handle action delay issues,a new scheduled action execution technique has been developed.This technique optimizes action execution timing through real-time policy adjustments,thus adapting to the dynamic changes in the orbital environment.A Hierarchical Reinforcement Learning(HRL)strategy was also adopted to simplify the decision-making process for long-distance pursuit tasks by setting phased subgoals to gradually approach the target.The effectiveness of the proposed strategy in practical satellite pursuit scenarios has been verified through simulations of two different tasks.展开更多
Objective:To explore factors influencing decision regret among colorectal cancer patients undergoing intestinal ostomy.Methods:A questionnaire survey was conducted among 102 colorectal cancer patients who underwent in...Objective:To explore factors influencing decision regret among colorectal cancer patients undergoing intestinal ostomy.Methods:A questionnaire survey was conducted among 102 colorectal cancer patients who underwent intestinal ostomy surgery and visited the ostomy clinic at a tertiary hospital in Baoding from July to September 2025.The Chinese version of the Ostomy Adaptation Inventory(OAI-20),Decision Regret Scale(DRS),Decision Conflict Scale(DCS),and Functional Assessment of Cancer Therapy-Colorectal(FACT-C)were used to measure patients’adaptation to stoma,decision regret,decision conflict,and quality of life.The Shared Decision-Making Questionnaire(SDM-Q-9)assessed patient involvement in ostomy surgery decisions,while the SSUK-8 evaluated social support.Additional items explored perceptions related to decision-making,participation,and outcomes.Results:Among 134 eligible patients attending the clinic,120 participated in the questionnaire,with 102 completing all items.Stoma patients reported an average decision regret score of 60.83(SD 28.43),an average coping ability score of 54.26(SD 26.69),an average decision conflict score of 62.55(SD 25.95),and a quality of life score of 56.93(SD 27.46).In the multiple regression analysis,decision regret was associated with decision conflict,poor patient coping ability,low quality of life,and low social support.Conclusion:Decision regret is prevalent among Chinese CRC patients following ostomy surgery.Compared with similar studies in other regions,Chinese CRC patients exhibit a higher rate of regret.This may be related to lower patient involvement in decision-making,generally poorer quality of life,and heavier economic burdens.展开更多
This study focuses on the construction and application of intelligent financial decision-making models driven by generative artificial intelligence(AI).It analyzes the mechanisms by which generative AI empowers financ...This study focuses on the construction and application of intelligent financial decision-making models driven by generative artificial intelligence(AI).It analyzes the mechanisms by which generative AI empowers financial decision-making within a dual framework of dynamic knowledge evolution and risk control.The research reveals that generative AI,with its superior data processing,pattern recognition,and autonomous learning capabilities,can transcend the limitations of traditional decision-making models,facilitating a significant shift from causal inference to probabilistic creation in decision-making paradigms.By systematically constructing an intelligent financial decision-making model that includes data governance,core engine,and decision output layers,the study clarifies the functional roles and collaborative mechanisms of each layer.Additionally,it addresses key challenges in technology application,institutional adaptation,and organizational transformation by proposing systematic strategies for technical risk management,institutional innovation,and organizational capability enhancement,aiming to provide robust theoretical support and practical guidance for the intelligent transformation of corporate financial decision-making.展开更多
BACKGROUND Cancer care faces challenges due to tumor heterogeneity and rapidly evolving therapies,necessitating artificial intelligence(AI)-driven clinical decision support.While general-purpose models like ChatGPT of...BACKGROUND Cancer care faces challenges due to tumor heterogeneity and rapidly evolving therapies,necessitating artificial intelligence(AI)-driven clinical decision support.While general-purpose models like ChatGPT offer adaptability,domain-specific systems(e.g.,DeepSeek)may better align with clinical guidelines.However,their comparative efficacy in oncology remains underexplored.This study hypothesizes that domain-specific AI will outperform general-purpose models in technical accuracy,while the latter will excel in patient-centered communication.AIMS To compare ChatGPT and DeepSeek in oncology decision support for diagnosis,treatment,and patient communication.METHODS A retrospective analysis was conducted using 1200 anonymized oncology cases(2018–2023)from The Cancer Genome Atlas and institutional databases,covering six cancer types.Each case included histopathology,imaging,genomic profiles,and treatment histories.Both models generated diagnostic interpretations,staging assessments,and therapy recommendations.Performance was evaluated against NCCN/ESMO guidelines and expert oncologist panels using F1-scores,Cohen'sκ,Likert-scale ratings,and readability metrics.Statistical significance was assessed via analysis of variance and post-hoc Tukey tests.RESULTS DeepSeek demonstrated superior performance in diagnostic accuracy(F1-score:89.2%vs ChatGPT's 76.5%,P<0.001)and treatment alignment with guidelines(κ=0.82 vs 0.67,P=0.003).ChatGPT exhibited strengths in patient communi-cation,generating layman-friendly explanations(readability score:8.2/10 vs DeepSeek's 6.5/10,P=0.012).Both models showed limitations in rare cancer subtypes(e.g.,cholangiocarcinoma),with accuracy dropping below 60%.Clinicians rated DeepSeek's outputs as more actionable(4.3/5 vs 3.7/5,P=0.021)but highlighted ChatGPT's utility in palliative care discussions.CONCLUSION Domain-specific AI(DeepSeek)excels in technical precision,while general-purpose models(ChatGPT)enhance patient engagement.A hybrid system integrating both approaches may optimize oncology workflows,contingent on expanded training for rare cancers and real-time guideline updates.展开更多
Gastrointestinal and hepatic disorders exhibit significant heterogeneity,charac-terized by complex and diverse clinical phenotypes.Most lesions present without typical symptoms in their early stages,which poses substa...Gastrointestinal and hepatic disorders exhibit significant heterogeneity,charac-terized by complex and diverse clinical phenotypes.Most lesions present without typical symptoms in their early stages,which poses substantial challenges for early clinical identification and intervention.As an interdisciplinary field at the forefront of technology,artificial intelligence(AI)integrates theoretical inno-vation,algorithm development,and engineering applications,triggering para-digm shifts within the medical field.Current research trends indicate that AI technology is progressively permeating the entire diagnostic and therapeutic process for gastrointestinal and hepatic disorders,facilitating intelligent transformations in precise lesion detection,optimization of treatment decisions,and prognosis evaluation through the integration of different modal data,construction of intelligent algorithms,and establishment of clinical verification systems.This article systematically reviews the latest advancements in AI technology concerning the diagnosis and treatment of gastrointestinal diseases(such as inflammatory bowel disease and digestive system tumors)and hepatic diseases(including hepato-cirrhosis and liver cancer),emphasizing its application value and transformative potential in critical areas such as imaging omics analysis,endoscopic intelligent identification,and personalized treatment prediction.展开更多
The high maneuverability of modern fighters in close air combat imposes significant cognitive demands on pilots,making rapid,accurate decision-making challenging.While reinforcement learning(RL)has shown promise in th...The high maneuverability of modern fighters in close air combat imposes significant cognitive demands on pilots,making rapid,accurate decision-making challenging.While reinforcement learning(RL)has shown promise in this domain,the existing methods often lack strategic depth and generalization in complex,high-dimensional environments.To address these limitations,this paper proposes an optimized self-play method enhanced by advancements in fighter modeling,neural network design,and algorithmic frameworks.This study employs a six-degree-of-freedom(6-DOF)F-16 fighter model based on open-source aerodynamic data,featuring airborne equipment and a realistic visual simulation platform,unlike traditional 3-DOF models.To capture temporal dynamics,Long Short-Term Memory(LSTM)layers are integrated into the neural network,complemented by delayed input stacking.The RL environment incorporates expert strategies,curiositydriven rewards,and curriculum learning to improve adaptability and strategic decision-making.Experimental results demonstrate that the proposed approach achieves a winning rate exceeding90%against classical single-agent methods.Additionally,through enhanced 3D visual platforms,we conducted human-agent confrontation experiments,where the agent attained an average winning rate of over 75%.The agent's maneuver trajectories closely align with human pilot strategies,showcasing its potential in decision-making and pilot training applications.This study highlights the effectiveness of integrating advanced modeling and self-play techniques in developing robust air combat decision-making systems.展开更多
To address the confrontation decision-making issues in multi-round air combat,a dynamic game decision method is proposed based on decision tree for the confrontation of unmanned aerial vehicle(UAV)air combat.Based on ...To address the confrontation decision-making issues in multi-round air combat,a dynamic game decision method is proposed based on decision tree for the confrontation of unmanned aerial vehicle(UAV)air combat.Based on game the-ory and the confrontation characteristics of air combat,a dynamic game process is constructed including the strategy sets,the situation information,and the maneuver decisions for both sides of air combat.By analyzing the UAV’s flight dyna-mics and the both sides’information,a payment matrix is estab-lished through the situation advantage function,performance advantage function,and profit function.Furthermore,the dynamic game decision problem is solved based on the linear induction method to obtain the Nash equilibrium solution,where the decision tree method is introduced to obtain the optimal maneuver decision,thereby improving the situation advantage in the next round of confrontation.According to the analysis,the simulation results for the confrontation scenarios of multi-round air combat are presented to verify the effectiveness and advan-tages of the proposed method.展开更多
Background: Clinical decision support tools provide suggestions to support healthcare providers and clinicians, as they attend to patients. Clinicians use these tools to rapidly consult the evidence at the point of ca...Background: Clinical decision support tools provide suggestions to support healthcare providers and clinicians, as they attend to patients. Clinicians use these tools to rapidly consult the evidence at the point of care, a practice which has been found to reduce the time patients spend in hospitals, promote the quality of care and improve healthcare outcomes. Such tools include Medscape, VisualDx, Clinical Key, DynaMed, BMJ Best Practice and UpToDate. However, use of such tools has not yet been fully embraced in low-resource settings such as Uganda. Objective: This paper intends to collate data on the use and uptake of one such tool, UpToDate, which was provided at no cost to five medical schools in Uganda. Methods: Free access to UpToDate was granted through the IP addresses of five medical schools in Uganda in collaboration with Better Evidence at The Global Health Delivery Project at Harvard and Brigham and Women’s Hospital and Wolters Kluwer Health. Following the donation, medical librarians in the respective institutions conducted training sessions and created awareness of the tool. Usage data was aggregated, based on logins and content views, presented and analyzed using Excel tables and graphs. Results: The data shows similar trends in increased usage over the period of August 2022 to August 2023 across the five medical schools. The most common topics viewed, mode of access (using either the computer or the mobile app), total usage by institution, ratio of uses to eligible users by institution and ratio of uses to students by institution are shared. Conclusion: The study revealed that the tool was used by various user categories across the institutions with similar steady improved usage over the year. These results can inform the librarians as they encourage their respective institutions to continue using the tool to support uptake of point-of-care tools in clinical practice.展开更多
System upgrades in unmanned systems have made Unmanned Aerial Vehicle(UAV)-based patrolling and monitoring a preferred solution for ocean surveillance.However,dynamic environments and large-scale deployments pose sign...System upgrades in unmanned systems have made Unmanned Aerial Vehicle(UAV)-based patrolling and monitoring a preferred solution for ocean surveillance.However,dynamic environments and large-scale deployments pose significant challenges for efficient decision-making,necessitating a modular multiagent control system.Deep Reinforcement Learning(DRL)and Decision Tree(DT)have been utilized for these complex decision-making tasks,but each has its limitations:DRL is highly adaptive but lacks interpretability,while DT is inherently interpretable but has limited adaptability.To overcome these challenges,we propose the Adaptive Interpretable Decision Tree(AIDT),an evolutionary-based algorithm that is both adaptable to diverse environmental settings and highly interpretable in its decision-making processes.We first construct a Markov decision process(MDP)-based simulation environment using the Cooperative Submarine Search task as a representative scenario for training and testing the proposed method.Specifically,we use the heat map as a state variable to address the issue of multi-agent input state proliferation.Next,we introduce the curiosity-guiding intrinsic reward to encourage comprehensive exploration and enhance algorithm performance.Additionally,we incorporate decision tree size as an influence factor in the adaptation process to balance task completion with computational efficiency.To further improve the generalization capability of the decision tree,we apply a normalization method to ensure consistent processing of input states.Finally,we validate the proposed algorithm in different environmental settings,and the results demonstrate both its adaptability and interpretability.展开更多
Artificial intelligence(AI)is rapidly transforming radiology and computed tomography(CT)imaging by enabling automated image analysis,improved diagnostic accuracy,and clinical decision-support.We performed a systematic...Artificial intelligence(AI)is rapidly transforming radiology and computed tomography(CT)imaging by enabling automated image analysis,improved diagnostic accuracy,and clinical decision-support.We performed a systematic review of peerreviewed studies published between January 1,2010 and March 31,2025 to quantify reported gains in diagnostic performance and workflow efficiency,to evaluate clinical decision-support benefits and risks,and to identify integration priorities.We searched PubMed,IEEE Xplore,Scopus,ScienceDirect,and Google Scholar and screened 128 records;26 studies met the inclusion criteria.Extracted data included study design,AI architecture,sample size,and quantitative performance metrics;study quality was assessed using Newcastle-Ottawa Scales(NOS),Cochrane RoB 2,or AMSTAR 2 as appropriate.Across included studies,AI applications in CT showed consistent improvements in sensitivity,specificity,and time-to-diagnosis in specific tasks(notably lung-nodule detection and intracranial hemorrhage triage),with reported detection-rate increases up to~20%and reduced turnaround times in several real-world implementations.Barriers include dataset bias,limited external validation,interpretability(“black-box”)concerns,workflow integration challenges,and evolving regulatory issues.Economic analyses suggest potentially favorable return on investment(ROI)in high-volume settings but are sensitive to licensing and infrastructure costs.To realize AI's benefits in CT imaging,rigorous multi-center validation,transparent reporting,humancentered workflow design,and post-deployment surveillance are essential.展开更多
Despite the widespread use of Decision trees (DT) across various applications, their performance tends to suffer when dealing with imbalanced datasets, where the distribution of certain classes significantly outweighs...Despite the widespread use of Decision trees (DT) across various applications, their performance tends to suffer when dealing with imbalanced datasets, where the distribution of certain classes significantly outweighs others. Cost-sensitive learning is a strategy to solve this problem, and several cost-sensitive DT algorithms have been proposed to date. However, existing algorithms, which are heuristic, tried to greedily select either a better splitting point or feature node, leading to local optima for tree nodes and ignoring the cost of the whole tree. In addition, determination of the costs is difficult and often requires domain expertise. This study proposes a DT for imbalanced data, called Swarm-based Cost-sensitive DT (SCDT), using the cost-sensitive learning strategy and an enhanced swarm-based algorithm. The DT is encoded using a hybrid individual representation. A hybrid artificial bee colony approach is designed to optimize rules, considering specified costs in an F-Measure-based fitness function. Experimental results using datasets compared with state-of-the-art DT algorithms show that the SCDT method achieved the highest performance on most datasets. Moreover, SCDT also excels in other critical performance metrics, such as recall, precision, F1-score, and AUC, with notable results with average values of 83%, 87.3%, 85%, and 80.7%, respectively.展开更多
With the growth of the construction industry,risk management in construction projects has garnered significant attention from the academic community.Effective risk management during the decision-making stage can great...With the growth of the construction industry,risk management in construction projects has garnered significant attention from the academic community.Effective risk management during the decision-making stage can greatly enhance project management efficiency.This paper integrates the AHP-entropy value method and constructs a risk management model based on the DPSIR framework for construction projects.The model is applied to evaluate and analyze the risk level of the decision-making stage in a navigation and electricity hub project in Chongqing Municipality.The results demonstrate the scientific validity and effectiveness of the proposed model.展开更多
In this investigation,the Gradient Boosting(GB),Linear Regression(LR),Decision Tree(DT),and Voting algo-rithms were applied to predict the distribution pattern of Au geochemical data.Trace and indicator elements,inclu...In this investigation,the Gradient Boosting(GB),Linear Regression(LR),Decision Tree(DT),and Voting algo-rithms were applied to predict the distribution pattern of Au geochemical data.Trace and indicator elements,including Mo,Cu,Pb,Zn,Ag,Ni,Co,Mn,Fe,and As,were used with these machine learning algorithms(MLAs)to predict Au concentration values in the Doostbigloo porphyry Cu-Au-Mo mineralization area.The performance of the models was evaluated using the Mean Absolute Percentage Error(MAPE)and Root Mean Square Error(RMSE)metrics.The proposed ensemble Voting algorithm outperformed the other models,yielding more ac-curate predictions according to both metrics.The predicted data from the GB,LR,DT,and Voting MLAs were modeled using the Concentration-Area fractal method,and Au geochemical anomalies were mapped.To compare and validate the results,factors such as the location of the mineral deposits,their surface extent,and mineralization trend were considered.The results indicate that integrating hybrid MLAs with fractal modeling signifi-cantly improves geochemical prospectivity mapping.Among the four models,three(DT,GB,Voting)accurately identified both mineral deposits.The LR model,however,only identified Deposit I(central),and its mineralization trend diverged from the field data.The GB and Voting models produced similar results,with their final maps derived from fractal modeling showing the same anomalous areas.The anomaly boundaries identified by these two models are consistent with the two known reserves in the region.The results and plots related to prediction indicators and error rates for these two models also show high similarity,with lower error rates than the other models.Notably,the Voting model demonstrated superior performance in accurately delineating mineral deposit locations and identifying realistic mineralization trends while minimizing false anomalies.展开更多
Secure and automated sharing of medical information among different medical entities/stakeholders like patients,hospitals,doctors,law enforcement agencies,health insurance companies etc.,in a standard format has alway...Secure and automated sharing of medical information among different medical entities/stakeholders like patients,hospitals,doctors,law enforcement agencies,health insurance companies etc.,in a standard format has always been a challenging problem.Current methods for ensuring compliance with medical privacy laws require specialists who are deeply familiar with these laws'complex requirements to verify the lawful exchange of medical information.This article introduces a Smart Medical Data Exchange Engine(SDEE)designed to automate the extracting of logical rules from medical privacy legislation using advanced techniques.These rules facilitate the secure extraction of information,safeguarding patient privacy and confidentiality.In addition,SMDEE can generate standardised clinical documents according to Health Level 7(HL7)standards and also standardise the nomenclature of requested medical data,enabling accurate decision-making when accessing patient data.All access requests to patient information are processed through SMDEE to ensure authorised access.The proposed system's efficacy is evaluated using the Health Insurance Portability and Accountability Act(HIPAA),a fundamental privacy law in the United States.However,SMDEE's flexibility allows its application worldwide,accommodating various medical privacy laws.Beyond facilitating global information exchange,SMDEE aims to enhance international patients'timely and appropriate treatment.展开更多
BACKGROUND Eyelid reconstruction is an intricate process,addressing both aesthetic and functional aspects post-trauma or oncological surgery.Aesthetic concerns and oncological radicality guide personalized approaches....BACKGROUND Eyelid reconstruction is an intricate process,addressing both aesthetic and functional aspects post-trauma or oncological surgery.Aesthetic concerns and oncological radicality guide personalized approaches.The complex anatomy,involving anterior and posterior lamellae,requires tailored reconstruction for optimal functionality.AIM To formulate an eyelid reconstruction algorithm through an extensive literature review and to validate it by juxtaposing surgical outcomes from Cattinara Hos-in dry eye and tears,which may lead to long-term consequences such as chronic conjunctivitis,discomfort,or photo-phobia.To prevent this issue,scars should be oriented vertically or perpendicularly to the free eyelid margin when the size of the tumor allows.In employing a malar flap to repair a lower eyelid defect,the malar incision must ascend diagonally;this facilitates enhanced flap advancement and mitigates ectropion by restricting vertical traction.Conse-quently,it is imperative to maintain that the generated tension remains consistently horizontal and never vertical[9].Lagophthalmos is a disorder characterized by the inability to completely close the eyelids,leading to corneal exposure and an increased risk of keratitis or ulceration;it may arise following upper eyelid surgery.To avert this issue,it is essential to preserve a minimum of 1 cm of skin between the superior edge of the excision and the inferior boundary of the eyebrow.Epiphora may occur in cancers involving the lacrimal puncta,requiring their removal.As previously stated,when employing a glabellar flap to rectify medial canthal abnormalities,it is essential to prevent a trapdoor effect or thickening of the flap relative to the eyelid skin to which it is affixed.Constraints about our proposed algorithm enco-mpass limited sample sizes and possible publication biases in existing studies.Subsequent investigations ought to examine long-term results to further refine the algorithm.Future research should evaluate the algorithm across varied populations and examine the impact of novel graft materials on enhancing reconstructive outcomes.CONCLUSION Eyelid reconstruction remains one of the most intriguing challenges for a plastic surgeon today.The most fascinating aspect of this discipline is the need to restore the functionality of such an essential structure while maintaining its aesthetics.In our opinion,creating decision-making algorithms can facilitate reaching this goal by allowing for the individualization of the reconstructive path while minimizing the incidence of complications.The fact that we have decreased the incidence of severe complications is a sign that the work is moving in the right direction.The fact that there has been no need for reintervention,neither for reconstructive issues nor for inadequate oncological radicality,overall signifies greater patient satisfaction as they do not have to undergo the stress of new surgeries.Even the minor complic-ations recorded are in line with those reported in the literature,and,even more importantly for patients,they are of limited duration.In our experience,after a year of application,we can say that the objective has been achieved,but much more can still be done.Behind every work,a scientific basis must be continually renewed and refreshed to maintain high-quality standards.Therefore,searching for possible alternative solutions to be included in one’s surgical armamentarium is fundamental to providing the patient with a fully personalized option.展开更多
文摘Across four studies,we explore the impact of solitude on consumers’reliance on feelings versus reasons in decision making,along with the underlying mechanism and boundary conditions.The results indicate that solitude individuals(vs.non-solitude)would prefer feeling-based strategy in decision-making,resulting in a higher intention of choosing the affectively superior option over the cognitively superior option(Study 1).Self-focus plays the underlying mechanism in the solitude effect(Study 2).Moreover,we also examine two boundary conditions:motivation(Study 3)and temporal orientation(Study 4),which indicates that involuntary motivation and future orientation can mitigate the solitude effect on affective processing.These findings provide insights into consumers’judgments of product attributes and selection of decision-making strategies according to their situations.
基金supported by the National Key Research and Development Program (No.2023YFC3502604)the National Natural Science Foundation of China (Nos.U23B2062, 82274352,82174533, 82374302, 82204941)+3 种基金the Noncommunicable Chronic Diseases-National Science and Technology Major Project (No.2023ZD0505700)the Beijing-Tianjin-Hebei Basic Research Cooperation Project (No.22JCZXJC00070)the State Key Laboratory on Technologies for Chinese Medicine Pharmaceutical Process Control and Intelligent Manufacture (No.SKL2024Z0102)Key R&D project of Ningxia Autonomous Region (No.2022BEG02036).
文摘Traditional Chinese medicine(TCM)represents a paradigmatic approach to personalized medicine,developed through the systematic accumulation and refinement of clinical empirical data over more than 2000 years,and now encompasses large-scale electronic medical records(EMR)and experimental molecular data.Artificial intelligence(AI)has demonstrated its utility in medicine through the development of various expert systems(e.g.,MYCIN)since the 1970s.With the emergence of deep learning and large language models(LLMs),AI’s potential in medicine shows considerable promise.Consequently,the integration of AI and TCM from both clinical and scientific perspectives presents a fundamental and promising research direction.This survey provides an insightful overview of TCM AI research,summarizing related research tasks from three perspectives:systems-level biological mechanism elucidation,real-world clinical evidence inference,and personalized clinical decision support.The review highlights representative AI methodologies alongside their applications in both TCM scientific inquiry and clinical practice.To critically assess the current state of the field,this work identifies major challenges and opportunities that constrain the development of robust research capabilities—particularly in the mechanistic understanding of TCM syndromes and herbal formulations,novel drug discovery,and the delivery of high-quality,patient-centered clinical care.The findings underscore that future advancements in AI-driven TCM research will rely on the development of high-quality,large-scale data repositories;the construction of comprehensive and domain-specific knowledge graphs(KGs);deeper insights into the biological mechanisms underpinning clinical efficacy;rigorous causal inference frameworks;and intelligent,personalized decision support systems.
基金supported by the National Research Council of Sri Lanka(Grant No.NRC TO 16-07).
文摘Decision Support Tool(DST)enables farmers to make site-specific crop management decisions;however,comprehensive calibration can be both costly and time-consuming.This study assessed the production and economic benefits of two calibrations of the Nutrient Expert(NE)tool for rice in Sri Lanka’s Alfisols:the basic calibration(Nutrient Expert Sri Lanka 1,NESL1)and the comprehensive calibration(Nutrient Expert Sri Lanka 2,NESL2).NESL1 was developed by adapting the South Indian version of NE to local conditions,while NESL2 was an updated version,using three years of data from 71 farmer fields.
文摘People have been engaged in sports activities both individually and collectively for years.Sports consumption,which refers to the process that covers many issues related to sports in the form of playing,watching,listening or reading,is a form of human behavior.The satisfaction of the four marketing components of product,price,distribution and promotion by using the leisure time of the sports consumer effectively and ensuring its continuity in the future process can be ensured by effective utilization of facilities and quality recreation activities.Consumer behaviors,which have a very complex structure,are seen in the form of choosing,buying,using and obtaining.With this study,it is aimed to determine the mediating role of consumer decision-making styles in determining the effect of marketing components in the consumption of sports activities on the satisfaction of sports consumers.In this direction,data were collected in the province of Istanbul,which was determined as the sample.Data were obtained with a questionnaire form created on Google Form.These data were analyzed in line with the model and hypotheses created with these data and it was determined that the marketing components of sports consumption have an impact on the sports consumer and it was concluded that consumer decision-making styles have a positive mediating effect in this regard.
基金supported by the National Natural Science Foundation of China(No.12202281)the Shanghai Natural Science Foundation,China(No.23ZR1461800)the Research Initiation Fund of Northwestern Polytechnical University,China(No.G2024KY05103)。
文摘The satellite orbital pursuit game focuses on studying spacecraft maneuvering strategies in space.Traditional numerical methods often face real-time inadequacies and adaptability limitations when dealing with highly nonlinear problems.With the advancement of Deep Reinforcement Learning(DRL)technology,continuous-time orbital control capabilities have significantly improved.Despite this,the existing DRL technologies still need adjustments in action delay and discretization structure to better adapt to practical application scenarios.Combining continuous learning and model planning demonstrates the adaptability of these methods in continuous-time decision problems.Additionally,to more effectively handle action delay issues,a new scheduled action execution technique has been developed.This technique optimizes action execution timing through real-time policy adjustments,thus adapting to the dynamic changes in the orbital environment.A Hierarchical Reinforcement Learning(HRL)strategy was also adopted to simplify the decision-making process for long-distance pursuit tasks by setting phased subgoals to gradually approach the target.The effectiveness of the proposed strategy in practical satellite pursuit scenarios has been verified through simulations of two different tasks.
文摘Objective:To explore factors influencing decision regret among colorectal cancer patients undergoing intestinal ostomy.Methods:A questionnaire survey was conducted among 102 colorectal cancer patients who underwent intestinal ostomy surgery and visited the ostomy clinic at a tertiary hospital in Baoding from July to September 2025.The Chinese version of the Ostomy Adaptation Inventory(OAI-20),Decision Regret Scale(DRS),Decision Conflict Scale(DCS),and Functional Assessment of Cancer Therapy-Colorectal(FACT-C)were used to measure patients’adaptation to stoma,decision regret,decision conflict,and quality of life.The Shared Decision-Making Questionnaire(SDM-Q-9)assessed patient involvement in ostomy surgery decisions,while the SSUK-8 evaluated social support.Additional items explored perceptions related to decision-making,participation,and outcomes.Results:Among 134 eligible patients attending the clinic,120 participated in the questionnaire,with 102 completing all items.Stoma patients reported an average decision regret score of 60.83(SD 28.43),an average coping ability score of 54.26(SD 26.69),an average decision conflict score of 62.55(SD 25.95),and a quality of life score of 56.93(SD 27.46).In the multiple regression analysis,decision regret was associated with decision conflict,poor patient coping ability,low quality of life,and low social support.Conclusion:Decision regret is prevalent among Chinese CRC patients following ostomy surgery.Compared with similar studies in other regions,Chinese CRC patients exhibit a higher rate of regret.This may be related to lower patient involvement in decision-making,generally poorer quality of life,and heavier economic burdens.
文摘This study focuses on the construction and application of intelligent financial decision-making models driven by generative artificial intelligence(AI).It analyzes the mechanisms by which generative AI empowers financial decision-making within a dual framework of dynamic knowledge evolution and risk control.The research reveals that generative AI,with its superior data processing,pattern recognition,and autonomous learning capabilities,can transcend the limitations of traditional decision-making models,facilitating a significant shift from causal inference to probabilistic creation in decision-making paradigms.By systematically constructing an intelligent financial decision-making model that includes data governance,core engine,and decision output layers,the study clarifies the functional roles and collaborative mechanisms of each layer.Additionally,it addresses key challenges in technology application,institutional adaptation,and organizational transformation by proposing systematic strategies for technical risk management,institutional innovation,and organizational capability enhancement,aiming to provide robust theoretical support and practical guidance for the intelligent transformation of corporate financial decision-making.
文摘BACKGROUND Cancer care faces challenges due to tumor heterogeneity and rapidly evolving therapies,necessitating artificial intelligence(AI)-driven clinical decision support.While general-purpose models like ChatGPT offer adaptability,domain-specific systems(e.g.,DeepSeek)may better align with clinical guidelines.However,their comparative efficacy in oncology remains underexplored.This study hypothesizes that domain-specific AI will outperform general-purpose models in technical accuracy,while the latter will excel in patient-centered communication.AIMS To compare ChatGPT and DeepSeek in oncology decision support for diagnosis,treatment,and patient communication.METHODS A retrospective analysis was conducted using 1200 anonymized oncology cases(2018–2023)from The Cancer Genome Atlas and institutional databases,covering six cancer types.Each case included histopathology,imaging,genomic profiles,and treatment histories.Both models generated diagnostic interpretations,staging assessments,and therapy recommendations.Performance was evaluated against NCCN/ESMO guidelines and expert oncologist panels using F1-scores,Cohen'sκ,Likert-scale ratings,and readability metrics.Statistical significance was assessed via analysis of variance and post-hoc Tukey tests.RESULTS DeepSeek demonstrated superior performance in diagnostic accuracy(F1-score:89.2%vs ChatGPT's 76.5%,P<0.001)and treatment alignment with guidelines(κ=0.82 vs 0.67,P=0.003).ChatGPT exhibited strengths in patient communi-cation,generating layman-friendly explanations(readability score:8.2/10 vs DeepSeek's 6.5/10,P=0.012).Both models showed limitations in rare cancer subtypes(e.g.,cholangiocarcinoma),with accuracy dropping below 60%.Clinicians rated DeepSeek's outputs as more actionable(4.3/5 vs 3.7/5,P=0.021)but highlighted ChatGPT's utility in palliative care discussions.CONCLUSION Domain-specific AI(DeepSeek)excels in technical precision,while general-purpose models(ChatGPT)enhance patient engagement.A hybrid system integrating both approaches may optimize oncology workflows,contingent on expanded training for rare cancers and real-time guideline updates.
文摘Gastrointestinal and hepatic disorders exhibit significant heterogeneity,charac-terized by complex and diverse clinical phenotypes.Most lesions present without typical symptoms in their early stages,which poses substantial challenges for early clinical identification and intervention.As an interdisciplinary field at the forefront of technology,artificial intelligence(AI)integrates theoretical inno-vation,algorithm development,and engineering applications,triggering para-digm shifts within the medical field.Current research trends indicate that AI technology is progressively permeating the entire diagnostic and therapeutic process for gastrointestinal and hepatic disorders,facilitating intelligent transformations in precise lesion detection,optimization of treatment decisions,and prognosis evaluation through the integration of different modal data,construction of intelligent algorithms,and establishment of clinical verification systems.This article systematically reviews the latest advancements in AI technology concerning the diagnosis and treatment of gastrointestinal diseases(such as inflammatory bowel disease and digestive system tumors)and hepatic diseases(including hepato-cirrhosis and liver cancer),emphasizing its application value and transformative potential in critical areas such as imaging omics analysis,endoscopic intelligent identification,and personalized treatment prediction.
基金co-supported by the National Natural Science Foundation of China(No.91852115)。
文摘The high maneuverability of modern fighters in close air combat imposes significant cognitive demands on pilots,making rapid,accurate decision-making challenging.While reinforcement learning(RL)has shown promise in this domain,the existing methods often lack strategic depth and generalization in complex,high-dimensional environments.To address these limitations,this paper proposes an optimized self-play method enhanced by advancements in fighter modeling,neural network design,and algorithmic frameworks.This study employs a six-degree-of-freedom(6-DOF)F-16 fighter model based on open-source aerodynamic data,featuring airborne equipment and a realistic visual simulation platform,unlike traditional 3-DOF models.To capture temporal dynamics,Long Short-Term Memory(LSTM)layers are integrated into the neural network,complemented by delayed input stacking.The RL environment incorporates expert strategies,curiositydriven rewards,and curriculum learning to improve adaptability and strategic decision-making.Experimental results demonstrate that the proposed approach achieves a winning rate exceeding90%against classical single-agent methods.Additionally,through enhanced 3D visual platforms,we conducted human-agent confrontation experiments,where the agent attained an average winning rate of over 75%.The agent's maneuver trajectories closely align with human pilot strategies,showcasing its potential in decision-making and pilot training applications.This study highlights the effectiveness of integrating advanced modeling and self-play techniques in developing robust air combat decision-making systems.
基金supported by the Major Projects for Science and Technology Innovation 2030(2018AAA0100805).
文摘To address the confrontation decision-making issues in multi-round air combat,a dynamic game decision method is proposed based on decision tree for the confrontation of unmanned aerial vehicle(UAV)air combat.Based on game the-ory and the confrontation characteristics of air combat,a dynamic game process is constructed including the strategy sets,the situation information,and the maneuver decisions for both sides of air combat.By analyzing the UAV’s flight dyna-mics and the both sides’information,a payment matrix is estab-lished through the situation advantage function,performance advantage function,and profit function.Furthermore,the dynamic game decision problem is solved based on the linear induction method to obtain the Nash equilibrium solution,where the decision tree method is introduced to obtain the optimal maneuver decision,thereby improving the situation advantage in the next round of confrontation.According to the analysis,the simulation results for the confrontation scenarios of multi-round air combat are presented to verify the effectiveness and advan-tages of the proposed method.
文摘Background: Clinical decision support tools provide suggestions to support healthcare providers and clinicians, as they attend to patients. Clinicians use these tools to rapidly consult the evidence at the point of care, a practice which has been found to reduce the time patients spend in hospitals, promote the quality of care and improve healthcare outcomes. Such tools include Medscape, VisualDx, Clinical Key, DynaMed, BMJ Best Practice and UpToDate. However, use of such tools has not yet been fully embraced in low-resource settings such as Uganda. Objective: This paper intends to collate data on the use and uptake of one such tool, UpToDate, which was provided at no cost to five medical schools in Uganda. Methods: Free access to UpToDate was granted through the IP addresses of five medical schools in Uganda in collaboration with Better Evidence at The Global Health Delivery Project at Harvard and Brigham and Women’s Hospital and Wolters Kluwer Health. Following the donation, medical librarians in the respective institutions conducted training sessions and created awareness of the tool. Usage data was aggregated, based on logins and content views, presented and analyzed using Excel tables and graphs. Results: The data shows similar trends in increased usage over the period of August 2022 to August 2023 across the five medical schools. The most common topics viewed, mode of access (using either the computer or the mobile app), total usage by institution, ratio of uses to eligible users by institution and ratio of uses to students by institution are shared. Conclusion: The study revealed that the tool was used by various user categories across the institutions with similar steady improved usage over the year. These results can inform the librarians as they encourage their respective institutions to continue using the tool to support uptake of point-of-care tools in clinical practice.
文摘System upgrades in unmanned systems have made Unmanned Aerial Vehicle(UAV)-based patrolling and monitoring a preferred solution for ocean surveillance.However,dynamic environments and large-scale deployments pose significant challenges for efficient decision-making,necessitating a modular multiagent control system.Deep Reinforcement Learning(DRL)and Decision Tree(DT)have been utilized for these complex decision-making tasks,but each has its limitations:DRL is highly adaptive but lacks interpretability,while DT is inherently interpretable but has limited adaptability.To overcome these challenges,we propose the Adaptive Interpretable Decision Tree(AIDT),an evolutionary-based algorithm that is both adaptable to diverse environmental settings and highly interpretable in its decision-making processes.We first construct a Markov decision process(MDP)-based simulation environment using the Cooperative Submarine Search task as a representative scenario for training and testing the proposed method.Specifically,we use the heat map as a state variable to address the issue of multi-agent input state proliferation.Next,we introduce the curiosity-guiding intrinsic reward to encourage comprehensive exploration and enhance algorithm performance.Additionally,we incorporate decision tree size as an influence factor in the adaptation process to balance task completion with computational efficiency.To further improve the generalization capability of the decision tree,we apply a normalization method to ensure consistent processing of input states.Finally,we validate the proposed algorithm in different environmental settings,and the results demonstrate both its adaptability and interpretability.
文摘Artificial intelligence(AI)is rapidly transforming radiology and computed tomography(CT)imaging by enabling automated image analysis,improved diagnostic accuracy,and clinical decision-support.We performed a systematic review of peerreviewed studies published between January 1,2010 and March 31,2025 to quantify reported gains in diagnostic performance and workflow efficiency,to evaluate clinical decision-support benefits and risks,and to identify integration priorities.We searched PubMed,IEEE Xplore,Scopus,ScienceDirect,and Google Scholar and screened 128 records;26 studies met the inclusion criteria.Extracted data included study design,AI architecture,sample size,and quantitative performance metrics;study quality was assessed using Newcastle-Ottawa Scales(NOS),Cochrane RoB 2,or AMSTAR 2 as appropriate.Across included studies,AI applications in CT showed consistent improvements in sensitivity,specificity,and time-to-diagnosis in specific tasks(notably lung-nodule detection and intracranial hemorrhage triage),with reported detection-rate increases up to~20%and reduced turnaround times in several real-world implementations.Barriers include dataset bias,limited external validation,interpretability(“black-box”)concerns,workflow integration challenges,and evolving regulatory issues.Economic analyses suggest potentially favorable return on investment(ROI)in high-volume settings but are sensitive to licensing and infrastructure costs.To realize AI's benefits in CT imaging,rigorous multi-center validation,transparent reporting,humancentered workflow design,and post-deployment surveillance are essential.
文摘Despite the widespread use of Decision trees (DT) across various applications, their performance tends to suffer when dealing with imbalanced datasets, where the distribution of certain classes significantly outweighs others. Cost-sensitive learning is a strategy to solve this problem, and several cost-sensitive DT algorithms have been proposed to date. However, existing algorithms, which are heuristic, tried to greedily select either a better splitting point or feature node, leading to local optima for tree nodes and ignoring the cost of the whole tree. In addition, determination of the costs is difficult and often requires domain expertise. This study proposes a DT for imbalanced data, called Swarm-based Cost-sensitive DT (SCDT), using the cost-sensitive learning strategy and an enhanced swarm-based algorithm. The DT is encoded using a hybrid individual representation. A hybrid artificial bee colony approach is designed to optimize rules, considering specified costs in an F-Measure-based fitness function. Experimental results using datasets compared with state-of-the-art DT algorithms show that the SCDT method achieved the highest performance on most datasets. Moreover, SCDT also excels in other critical performance metrics, such as recall, precision, F1-score, and AUC, with notable results with average values of 83%, 87.3%, 85%, and 80.7%, respectively.
文摘With the growth of the construction industry,risk management in construction projects has garnered significant attention from the academic community.Effective risk management during the decision-making stage can greatly enhance project management efficiency.This paper integrates the AHP-entropy value method and constructs a risk management model based on the DPSIR framework for construction projects.The model is applied to evaluate and analyze the risk level of the decision-making stage in a navigation and electricity hub project in Chongqing Municipality.The results demonstrate the scientific validity and effectiveness of the proposed model.
文摘In this investigation,the Gradient Boosting(GB),Linear Regression(LR),Decision Tree(DT),and Voting algo-rithms were applied to predict the distribution pattern of Au geochemical data.Trace and indicator elements,including Mo,Cu,Pb,Zn,Ag,Ni,Co,Mn,Fe,and As,were used with these machine learning algorithms(MLAs)to predict Au concentration values in the Doostbigloo porphyry Cu-Au-Mo mineralization area.The performance of the models was evaluated using the Mean Absolute Percentage Error(MAPE)and Root Mean Square Error(RMSE)metrics.The proposed ensemble Voting algorithm outperformed the other models,yielding more ac-curate predictions according to both metrics.The predicted data from the GB,LR,DT,and Voting MLAs were modeled using the Concentration-Area fractal method,and Au geochemical anomalies were mapped.To compare and validate the results,factors such as the location of the mineral deposits,their surface extent,and mineralization trend were considered.The results indicate that integrating hybrid MLAs with fractal modeling signifi-cantly improves geochemical prospectivity mapping.Among the four models,three(DT,GB,Voting)accurately identified both mineral deposits.The LR model,however,only identified Deposit I(central),and its mineralization trend diverged from the field data.The GB and Voting models produced similar results,with their final maps derived from fractal modeling showing the same anomalous areas.The anomaly boundaries identified by these two models are consistent with the two known reserves in the region.The results and plots related to prediction indicators and error rates for these two models also show high similarity,with lower error rates than the other models.Notably,the Voting model demonstrated superior performance in accurately delineating mineral deposit locations and identifying realistic mineralization trends while minimizing false anomalies.
基金fully supported by the University of Vaasa and VTT Technical Research Centre of Finland.
文摘Secure and automated sharing of medical information among different medical entities/stakeholders like patients,hospitals,doctors,law enforcement agencies,health insurance companies etc.,in a standard format has always been a challenging problem.Current methods for ensuring compliance with medical privacy laws require specialists who are deeply familiar with these laws'complex requirements to verify the lawful exchange of medical information.This article introduces a Smart Medical Data Exchange Engine(SDEE)designed to automate the extracting of logical rules from medical privacy legislation using advanced techniques.These rules facilitate the secure extraction of information,safeguarding patient privacy and confidentiality.In addition,SMDEE can generate standardised clinical documents according to Health Level 7(HL7)standards and also standardise the nomenclature of requested medical data,enabling accurate decision-making when accessing patient data.All access requests to patient information are processed through SMDEE to ensure authorised access.The proposed system's efficacy is evaluated using the Health Insurance Portability and Accountability Act(HIPAA),a fundamental privacy law in the United States.However,SMDEE's flexibility allows its application worldwide,accommodating various medical privacy laws.Beyond facilitating global information exchange,SMDEE aims to enhance international patients'timely and appropriate treatment.
文摘BACKGROUND Eyelid reconstruction is an intricate process,addressing both aesthetic and functional aspects post-trauma or oncological surgery.Aesthetic concerns and oncological radicality guide personalized approaches.The complex anatomy,involving anterior and posterior lamellae,requires tailored reconstruction for optimal functionality.AIM To formulate an eyelid reconstruction algorithm through an extensive literature review and to validate it by juxtaposing surgical outcomes from Cattinara Hos-in dry eye and tears,which may lead to long-term consequences such as chronic conjunctivitis,discomfort,or photo-phobia.To prevent this issue,scars should be oriented vertically or perpendicularly to the free eyelid margin when the size of the tumor allows.In employing a malar flap to repair a lower eyelid defect,the malar incision must ascend diagonally;this facilitates enhanced flap advancement and mitigates ectropion by restricting vertical traction.Conse-quently,it is imperative to maintain that the generated tension remains consistently horizontal and never vertical[9].Lagophthalmos is a disorder characterized by the inability to completely close the eyelids,leading to corneal exposure and an increased risk of keratitis or ulceration;it may arise following upper eyelid surgery.To avert this issue,it is essential to preserve a minimum of 1 cm of skin between the superior edge of the excision and the inferior boundary of the eyebrow.Epiphora may occur in cancers involving the lacrimal puncta,requiring their removal.As previously stated,when employing a glabellar flap to rectify medial canthal abnormalities,it is essential to prevent a trapdoor effect or thickening of the flap relative to the eyelid skin to which it is affixed.Constraints about our proposed algorithm enco-mpass limited sample sizes and possible publication biases in existing studies.Subsequent investigations ought to examine long-term results to further refine the algorithm.Future research should evaluate the algorithm across varied populations and examine the impact of novel graft materials on enhancing reconstructive outcomes.CONCLUSION Eyelid reconstruction remains one of the most intriguing challenges for a plastic surgeon today.The most fascinating aspect of this discipline is the need to restore the functionality of such an essential structure while maintaining its aesthetics.In our opinion,creating decision-making algorithms can facilitate reaching this goal by allowing for the individualization of the reconstructive path while minimizing the incidence of complications.The fact that we have decreased the incidence of severe complications is a sign that the work is moving in the right direction.The fact that there has been no need for reintervention,neither for reconstructive issues nor for inadequate oncological radicality,overall signifies greater patient satisfaction as they do not have to undergo the stress of new surgeries.Even the minor complic-ations recorded are in line with those reported in the literature,and,even more importantly for patients,they are of limited duration.In our experience,after a year of application,we can say that the objective has been achieved,but much more can still be done.Behind every work,a scientific basis must be continually renewed and refreshed to maintain high-quality standards.Therefore,searching for possible alternative solutions to be included in one’s surgical armamentarium is fundamental to providing the patient with a fully personalized option.
