Foundations of medical decision-making for older adults with cardiovascular disease

In order to help older adults with cardiovascular disease navigate complex decisions, clinicians must know tenets of medical ethics and have good communication skills. The elements of decision making capacity and informed consent are reviewed, using relevant clinical ex- amples to illustrate the basic concepts. The shared decision making model, by which clinician and patient work together to determine the plan of care, is described. Useful communication techniques to implement shared decision making are suggested.

Medical Diagnosis Based on Multi-Attribute Group Decision-Making Using Extension Fuzzy Sets,Aggregation Operators and Basic Uncertainty Information Granule

Accurate medical diagnosis,which involves identifying diseases based on patient symptoms,is often hindered by uncertainties in data interpretation and retrieval.Advanced fuzzy set theories have emerged as effective tools to address these challenges.In this paper,new mathematical approaches for handling uncertainty in medical diagnosis are introduced using q-rung orthopair fuzzy sets(q-ROFS)and interval-valued q-rung orthopair fuzzy sets(IVq-ROFS).Three aggregation operators are proposed in our methodologies:the q-ROF weighted averaging(q-ROFWA),the q-ROF weighted geometric(q-ROFWG),and the q-ROF weighted neutrality averaging(qROFWNA),which enhance decision-making under uncertainty.These operators are paired with ranking methods such as the similarity measure,score function,and inverse score function to improve the accuracy of disease identification.Additionally,the impact of varying q-rung values is explored through a sensitivity analysis,extending the analysis beyond the typical maximum value of 3.The Basic Uncertain Information(BUI)method is employed to simulate expert opinions,and aggregation operators are used to combine these opinions in a group decisionmaking context.Our results provide a comprehensive comparison of methodologies,highlighting their strengths and limitations in diagnosing diseases based on uncertain patient data.

A Synergistic Multi-Attribute Decision-Making Method for Educational Institutions Evaluation Using Similarity Measures of Possibility Pythagorean Fuzzy Hypersoft Sets

Due to the numerous variables to take into account as well as the inherent ambiguity and uncertainty,evaluating educational institutions can be difficult.The concept of a possibility Pythagorean fuzzy hypersoft set(pPyFHSS)is more flexible in this regard than other theoretical fuzzy set-like models,even though some attempts have been made in the literature to address such uncertainties.This study investigates the elementary notions of pPyFHSS including its set-theoretic operations union,intersection,complement,OR-and AND-operations.Some results related to these operations are also modified for pPyFHSS.Additionally,the similarity measures between pPyFHSSs are formulated with the assistance of numerical examples and results.Lastly,an intelligent decision-assisted mechanism is developed with the proposal of a robust algorithm based on similarity measures for solving multi-attribute decision-making(MADM)problems.A case study that helps the decision-makers assess the best educational institution is discussed to validate the suggested system.The algorithmic results are compared with the most pertinent model to evaluate the adaptability of pPyFHSS,as it generalizes the classical possibility fuzzy set-like theoretical models.Similarly,while considering significant evaluating factors,the flexibility of pPyFHSS is observed through structural comparison.

Congruent Feature Selection Method to Improve the Efficacy of Machine Learning-Based Classification in Medical Image Processing

Machine learning(ML)is increasingly applied for medical image processing with appropriate learning paradigms.These applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify specific flaws/diseases for diagnosis.The primary concern of ML applications is the precise selection of flexible image features for pattern detection and region classification.Most of the extracted image features are irrelevant and lead to an increase in computation time.Therefore,this article uses an analytical learning paradigm to design a Congruent Feature Selection Method to select the most relevant image features.This process trains the learning paradigm using similarity and correlation-based features over different textural intensities and pixel distributions.The similarity between the pixels over the various distribution patterns with high indexes is recommended for disease diagnosis.Later,the correlation based on intensity and distribution is analyzed to improve the feature selection congruency.Therefore,the more congruent pixels are sorted in the descending order of the selection,which identifies better regions than the distribution.Now,the learning paradigm is trained using intensity and region-based similarity to maximize the chances of selection.Therefore,the probability of feature selection,regardless of the textures and medical image patterns,is improved.This process enhances the performance of ML applications for different medical image processing.The proposed method improves the accuracy,precision,and training rate by 13.19%,10.69%,and 11.06%,respectively,compared to other models for the selected dataset.The mean error and selection time is also reduced by 12.56%and 13.56%,respectively,compared to the same models and dataset.

Getting physical with medical education:Exercise based virtual anatomy review classes for medical students

The benefits of regular physical activity are well known.Yet,few studies have examined the effectiveness of integrating physical activity(PA)into curricula within a post-secondary setting.To investigate the incorporation of PA into medical curriculum,we developed a series of optional exercise-based review sessions designed to reinforce musculoskeletal(MSK)anatomy course material.These synchronous sessions were co-taught by a group fitness instructor and an anatomy instructor.The fitness instructor would lead students through both strength and yoga style exercises,while the anatomy instructor asked questions about relevant anatomical structures related to course material previously covered.After the sessions,participants were asked to evaluate the classes on their self-reported exam preparedness in improving MSK anatomy knowledge,PA levels,and mental wellbeing.Thirty participants completed surveys;a majority agreed that the classes increased understanding of MSK concepts(90.0%)and activity levels(97.7%).Many(70.0%)felt that the classes helped reduce stress.The majority of respondents(90.0%)agreed that the classes contributed to increased feelings of social connectedness.Overall,medical students saw benefit in PA based interventions to supplement MSK course concepts.Along with increasing activity levels and promoting health behaviours,integrating PA into medical curriculum may improve comprehension of learning material,alleviate stress and foster social connectivity among medical students.

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

For the explosive development of emerging diagnostic and therapeutic technologies brought by the advancement of precision medicine strategy, shared decision-making could improve the quality of clinical decision-making and promote the transformation of clinical research evidence in TCM. Paying attention to patients＇ narrative needs and strengthening medical humanistic concerns could improve clinical outcome and patient satisfaction. We described the origins and development of evidence-based medicine, narrative medicine and shared decision-making, and analyzed the existing problems in TCM clinical decision-making. Further, we put forward the model of shared decision-making between clinicians and patients under the guidance of narrative evidence-based medicine concepts and methods.

Application of Real-World Evidence in Regulatory Decision-Making for Medical Devices

Objective To understand the real-world study(RWS)and its application in domestic and foreign pharmaceutical device regulatory decision-making,so as to promote its further application in the decision-making of medical devices supervision in China.Methods Literature analysis and comparative analysis were used to compare the real-world evidence(RWE)and randomized controlled trials to obtain the development status of medical devices in China and the United States.Results and Conclusion It is found that the application of RWS in the field of medical devices has many advantages,which can make up for the deficiencies of traditional clinical data.In addition,the RWE has a wide range of applications in the field of medical devices in China.In recent years,the RWE has received extensive attention in the medical field in the United States.The FDA has issued related guidelines,and its applications have gradually formed a complete system.Research on RWE will become the development trend of clinical evaluation of medical devices in the future.While attaching importance to the development of the RWE,China should learn from the specifications of relevant foreign organizations to improve its development in the field of health.

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.

Reconstruction of Knowledge and Medical Images in the Convergence of Chinese and Western Medicine:Taking “Sweet Meat” as an Example

The pancreas is neither part of the five Zang organs(五脏) nor the six Fu organs(六腑).Thus,it has received little attention in Chinese medical literature.In the late 19th century,medical missionaries in China started translating and introducing anatomical and physiological knowledge about the pancreas.As for the word pancreas,an early and influential translation was “sweet meat”(甜肉),proposed by Benjamin Hobson(合信).The translation “sweet meat” is not faithful to the original meaning of “pancreas”,but is a term coined by Hobson based on his personal habits,and the word “sweet” appeared by chance.However,in the decades since the term “sweet meat” became popular,Chinese medicine practitioners,such as Tang Zonghai(唐宗海),reinterpreted it by drawing new medical illustrations for “sweet meat” and giving new connotations to the word “sweet”.This discussion and interpretation of “sweet meat” in modern China,particularly among Chinese medicine professionals,is not only a dissemination and interpretation of the knowledge of “pancreas”,but also a construction of knowledge around the term “sweet meat”.

EDSUCh:A robust ensemble data summarization method for effective medical diagnosis

Identifying rare patterns for medical diagnosis is a challenging task due to heterogeneity and the volume of data.Data summarization can create a concise version of the original data that can be used for effective diagnosis.In this paper,we propose an ensemble summarization method that combines clustering and sampling to create a summary of the original data to ensure the inclusion of rare patterns.To the best of our knowledge,there has been no such technique available to augment the performance of anomaly detection techniques and simultaneously increase the efficiency of medical diagnosis.The performance of popular anomaly detection algorithms increases significantly in terms of accuracy and computational complexity when the summaries are used.Therefore,the medical diagnosis becomes more effective,and our experimental results reflect that the combination of the proposed summarization scheme and all underlying algorithms used in this paper outperforms the most popular anomaly detection techniques.

Process,Material,and Regulatory Considerations for 3D Printed Medical Devices and Tissue Constructs

Three-dimensional(3D)printing is a highly automated platform that facilitates material deposition in a layer-by-layer approach to fabricate pre-defined 3D complex structures on demand.It is a highly promising technique for the fabrication of personalized medical devices or even patient-specific tissue constructs.Each type of 3D printing technique has its unique advantages and limitations,and the selection of a suitable 3D printing technique is highly dependent on its intended application.In this review paper,we present and highlight some of the critical processes(printing parameters,build orientation,build location,and support structures),material(batch-to-batch consistency,recycling,protein adsorption,biocompatibility,and degradation properties),and regulatory considerations(sterility and mechanical properties)for 3D printing of personalized medical devices.The goal of this review paper is to provide the readers with a good understanding of the various key considerations(process,material,and regulatory)in 3D printing,which are critical for the fabrication of improved patient-specific 3D printed medical devices and tissue constructs.

Advancements in medical treatment for pancreatic neuroendocrine tumors:A beacon of hope

This editorial highlights the remarkable advancements in medical treatment strategies for pancreatic neuroendocrine tumors(pan-NETs),emphasizing tailored approaches for specific subtypes.Cytoreductive surgery and somatostatin analogs(SSAs)play pivotal roles in managing tumors,while palliative options such as molecular targeted therapy,peptide receptor radionuclide therapy,and chemotherapy are reserved for SSA-refractory patients.Gastrinomas,insul-inomas,glucagonomas,carcinoid tumors and VIPomas necessitate distinct thera-peutic strategies.Understanding the genetic basis of pan-NETs and exploring immunotherapies could lead to promising avenues for future research.This review underscores the evolving landscape of pan-NET treatment,offering renewed hope and improved outcomes for patients facing this complex disease.

Curved Spine:The Shape of Spine in Taoist Body Diagrams and Its Influence on Medical Body Diagrams

From the early Taoist diagrams of the human body to the end of the Qing dynasty and the beginning of the Republic of China,Taoists exaggerated and deformed the human spine in a shape-shifting manner.It is likely that medical practitioners were influenced by this style of representation,and there are also numerous diagrams of the human body with the curved spine in the lateral-view diagrams of viscera and Ming Tang Tu(明堂图Acupuncture and Moxibustion Chart),which constantly show the human torso in an elliptical“egg shape”.No later than the Ming dynasty,medical practitioners began to depict the actual physiological spinal curve of the human body.By the Qing dynasty,the depiction of the spinal curve in medical diagrams of the human figure showed a tendency to part ways with the Taoist freehand style of the previous generation.Although the representation of the curve of the spine was very crude,later medical images of the human body at least gradually straightened the spine and no longer depicted it in a shape-shifting manner.However,the curved spine in Taoist diagrams of the human body continued to exist,and the presentation of the curved spine never changed.This way of depicting its appearance,which is very different from reality,is shaped by Taoism's special way of perceiving and viewing the body,and may also contain another form of truth.

The"4+4"Medical Doctor(MD)Pilot Program at PUMC:Implementation,Performance,and Prospects

Peking Union Medical College(PUMC)launched the"4+4"Medical Doctor(MD)pilot program in 2018,admitting students with non-medical backgrounds from top universities,aligning with national medical talent training policies to foster diverse and eager learners in medicine.On the occasion of the graduation of the first class of the"4+4"MD pilot class at PUMC in 2023,we reviewed the teaching reform in the pilot program and carried out a systematic survey and interviews with students,faculties,and management staff of the pilot class.This article reports on the measures taken by the pilot class at PUMC in enrollment and curriculum setting,and demonstrates the achievements of the pilot class in terms of student academic background structure,knowledge acquisition and skill learning,scientific research ability,and course evaluation.The results indicated that the pilot class had met the national demand for the"Medicine+X"talent training model.More specifically,with a diverse academic backgrounds,the pilot class graduates had academic levels comparable to the eight-year medical education graduates,and their scientific research abilities were satisfactory.The pilot program at PUMC will optimize the curriculum setting,strengthen the construction of faculty,learning resources,and teaching facilities,and reform the academic evaluation methods,thus deepening the reform of medical education and improving the"4+4"MD program as a novel medical education model.

Traditional Chinese Medical Cases and Its Role in Pedagogy:A Narrative Analysis

Narrative medicine has gained significant attention in recent decades.The similarities between“parallel charts”and“medical cases”in traditional Chinese medicine(TCM)primarily lie in their authenticity.However,they differ in structure and narrative methods.Furthermore,medical case teaching is a prevalent pedagogical approach in TCM education that practitioners must master.This study explores the connection between TCM medical case teaching and narrative medicine,and concludes that the evolution of modern TCM case teaching aligns with the international standards of narrative medicine while integrating key TCM characteristics to enhance its value.This approach is essential for fostering humanistic sentiments,empathy,and reflective capabilities among future well-rounded TCM practitioners.

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.

Associative Tasks Computing Offloading Scheme in Internet of Medical Things with Deep Reinforcement Learning

The Internet of Medical Things(Io MT) is regarded as a critical technology for intelligent healthcare in the foreseeable 6G era. Nevertheless, due to the limited computing power capability of edge devices and task-related coupling relationships, Io MT faces unprecedented challenges. Considering the associative connections among tasks, this paper proposes a computing offloading policy for multiple-user devices(UDs) considering device-to-device(D2D) communication and a multi-access edge computing(MEC)technique under the scenario of Io MT. Specifically,to minimize the total delay and energy consumption concerning the requirement of Io MT, we first analyze and model the detailed local execution, MEC execution, D2D execution, and associated tasks offloading exchange model. Consequently, the associated tasks’ offloading scheme of multi-UDs is formulated as a mixed-integer nonconvex optimization problem. Considering the advantages of deep reinforcement learning(DRL) in processing tasks related to coupling relationships, a Double DQN based associative tasks computing offloading(DDATO) algorithm is then proposed to obtain the optimal solution, which can make the best offloading decision under the condition that tasks of UDs are associative. Furthermore, to reduce the complexity of the DDATO algorithm, the cacheaided procedure is intentionally introduced before the data training process. This avoids redundant offloading and computing procedures concerning tasks that previously have already been cached by other UDs. In addition, we use a dynamic ε-greedy strategy in the action selection section of the algorithm, thus preventing the algorithm from falling into a locally optimal solution. Simulation results demonstrate that compared with other existing methods for associative task models concerning different structures in the Io MT network, the proposed algorithm can lower the total cost more effectively and efficiently while also providing a tradeoff between delay and energy consumption tolerance.

Analysis and improving countermeasures of medical disputes from the perspective of legal changes

Under the background of medical disputes growing in number,scale and intensity,tracing back legal changes in medical field as a breakthrough point,this paper took a legal perspective to illustrate changes in medical dispute settlements from legislative orientation to legal system improvement.In view of the fact that early legislation in medical field was biased towards identification and punishment of doctors’responsibility,and later intensive legislation in balancing increasing"medical trouble"phenomenon with limited effects and difficulties to abide by the law,this paper proposed to improve doctor-patient dispute settlements system in China referencing from foreign law experience,to reduce investigation of doctors at the judicial level,and to establish a settlement mechanism on doctors’apology at the legislative level,so as to promote a healthy development of doctor-patient relationship.

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.