Information Network Research Articles

Optimal Status Updates for Minimizing Age of Correlated Information in IoT Networks With Energy Harvesting Sensors

Optimal Status Updates for Minimizing Age of Correlated Information in IoT Networks With Energy Harvesting Sensors

Safinamide as adjunctive therapy to levodopa monotherapy for patients with Parkinson's disease with wearing-off: The Japanese observational J-SILVER study

BackgroundSafinamide is an effective adjunctive therapy for wearing-off in Parkinson's disease (PD); however, evidence is lacking in older patients and those in the early stages of wearing-off. This study evaluated the efficacy and safety of safinamide as adjunctive therapy in patients with PD treated with levodopa monotherapy in clinical practice. MethodsThis multicentre, open-label observational study was conducted at five sites in Japan. Patients diagnosed with PD and wearing-off initiated safinamide as adjunctive therapy with levodopa monotherapy. Efficacy endpoints were mean changes in Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) Part I, III, and IV scores; daily ON-time without dyskinesia using 24-h patient symptom diaries; and 39-item Parkinson's Disease Questionnaire (PDQ-39) scores at 18 weeks of treatment. ResultsIn total, 24 patients initiated safinamide (66.7% were aged ≥75 years); the mean duration of wearing-off was 1.2 years. MDS-UPDRS Part III total score, Part IV total score, and PDQ-39 summary index decreased significantly from baseline (mean change −7.0 [p = 0.012], −2.4 [p = 0.007] and − 5.3 [p = 0.012], respectively). There was a non-statistically significant increase of 1.55 h in mean daily ON-time without dyskinesia. Numerical Rating Scale total score for pain (p = 0.015), and scores for OFF-period pain (p = 0.012) and nocturnal pain (p = 0.021) subdomains were significantly improved in the subgroup with pain. Most reported adverse events were classified as mild. ConclusionSafinamide improved motor and non-motor symptoms and quality of life-related measures in older patients with PD in the early stages of wearing-off without new safety concerns. Study registrationUniversity Hospital Medical Information Network in Japan; study ID: UMIN000044341.

Predictive Model for EV Charging Load Incorporating Multimodal Travel Behavior and Microscopic Traffic Simulation

A predictive model for the spatiotemporal distribution of electric vehicle (EV) charging load is proposed in this paper, considering multimodal travel behavior and microscopic traffic simulation. Firstly, the characteristic variables of travel time are fitted using advanced techniques such as Gaussian mixture distribution. Simultaneously, the user’s multimodal travel behavior is delineated by introducing travel purpose transfer probabilities, thus establishing a comprehensive travel spatiotemporal model. Secondly, the improved Floyd algorithm is employed to select the optimal path, taking into account various factors including signal light status, vehicle speed, and the position of starting and ending sections. Moreover, the approach of multi-lane lane change following and the utilization of cellular automata theory are introduced. To establish a microscopic traffic simulation model, a real-time energy consumption model is integrated with the aforementioned techniques. Thirdly, the minimum regret value is leveraged in conjunction with various other factors, including driving purpose, charging station electricity price, parking cost, and more, to simulate the decision-making process of users regarding charging stations. Subsequently, an EV charging load predictive framework is proposed based on the approach driven by electricity prices and real-time interaction of coupled network information. Finally, this paper conducts large-scale simulations to analyze the spatiotemporal distribution characteristics of EV charging load using a regional transportation network in East China and a typical power distribution network as case studies, thereby validating the feasibility of the proposed method.

Multi-Source Information Fusion Graph Convolution Network for traffic flow prediction

As a fundamental technology in the field of intelligent transportation systems, traffic flow prediction has a wide range of applications. The utilization of Graph Convolutional Network (GCN) models is notable for capturing the complex spatial–temporal dependencies in traffic data, leading to a significant improvement in prediction accuracy. However, most existing graph construction methods overlook joint impact of auxiliary information such as weather and traffic speed on the road topology. Moreover, the research on interactions within time series at coarse temporal resolutions remains insufficiently explored, giving rise to unsatisfactory long-term prediction performance. In this study, we present a novel framework, namely Multi-Source Information Fusion Graph Convolution Network (MIFGCN), for spatial–temporal traffic flow prediction. Our key innovation lies in creating a dynamic graph that integrates weather, traffic speed, and global spatial information, effectively simulating significant traffic fluctuations caused by subtle ancillary information in the road network. Simultaneously, it captures the evolving hidden adjacency relationships between nodes over time. Furthermore, by combining with an attention-based temporal interaction module, MIFGCN learns multiscale temporal correlations at course temporal resolutions, enhancing the ability for long-term prediction. Experiments conducted on four real-world traffic datasets demonstrate that MIFGCN outperforms various state-of-the-art baselines, especially achieving a 10.50% average improvement on the PeMS08 dataset.

Skip connection information enhancement network for retinal vessel segmentation.

Many major diseases of the retina often show symptoms of lesions in the fundus of the eye. The extraction of blood vessels from retinal fundus images is essential to assist doctors. Some of the existing methods do not fully extract the detailed features of retinal images or lose some information, making it difficult to accurately segment capillaries located at the edges of the images. In this paper, we propose a multi-scale retinal vessel segmentation network (SCIE_Net) based on skip connection information enhancement. Firstly, the network processes retinal images at multiple scales to achieve network capture of features at different scales. Secondly, the feature aggregation module is proposed to aggregate the rich information of the shallow network. Further, the skip connection information enhancement module is proposed to take into account the detailed features of the shallow layer and the advanced features of the deeper network to avoid the problem of incomplete information interaction between the layers of the network. Finally, SCIE_Net achieves better vessel segmentation performance and results on the publicly available retinal image standard datasets DRIVE, CHASE_DB1, and STARE.

Türkiye’de Kamu Politikalarında Yerel Karar Verme Sürecinin Merkezi İdareye Bağlı Taşra Teşkilatlarında Uygulama Örneği: Kefe Projesi

The supervision authority of the Ministry of National Education, which is the central administration in education policies in Turkey, is exercised by various methods in many areas. Auxiliary resources such as Education Information Network (Eba), General Directorate of Secondary Education Material (Ogmmateryal), Student/Teacher Support System (Ods) prepared by the Ministry of National Education for the use of students and teachers in lessons have been made available as an education policy on the basis of equal opportunity in education. However, the supervision and control of these resources by the Ministry of National Education at the policy evaluation stage is carried out only in Support and Training Courses, and there is no supervision or control in their use in other education and training activities. Within the scope of the Kulu District National Education Directorate Equal Opportunity in Education (KEFE) Project, ensuring that the auxiliary resources of the Ministry of National Education are used by teachers and students in lessons is supervised within a system integrity, taking into account local needs in addition to the central administration's supervision system. This research examines an example of how the KEFE project can be implemented by taking initiative in provincial organizations within the local decision-making process, which is mostly seen in local governments, by extending the partial control system of the central administration to all education and training activities, outside the central administration in the supervision of education policies. Keywords: Public Policies, Education policies, Local decision-making process, Ministry of National Education.

The Cost of Urban Renewal: Annual Construction Waste Estimation via Multi-Scale Target Information Extraction and Attention-Enhanced Networks in Changping District, Beijing

Construction waste is an inevitable byproduct of urban renewal, causing severe pressure on the environment, health, and ecology. Accurately estimating the production of construction waste is crucial for assessing the consumption of urban renewal. However, traditional manual estimation methods rely heavily on statistical data and historical experience, which lack flexibility in practical applications and are time-consuming and labor-intensive. In addition, their accuracy and timeliness need to be improved urgently. Fortunately, with the advantages of high-resolution remote sensing images (HRSIs) such as strong timeliness, large amounts of information, and macroscopic observations, they are suitable for the large-scale dynamic change detection of construction waste. However, the existing deep learning models have a relatively poor ability to extract and fuse features for small and multi-scale targets, and it is difficult to deal with irregularly shaped and fragmented detection areas. Therefore, this study proposes a Multi-scale Target Attention-Enhanced Network (MT-AENet), which is used to dynamically track and detect changes in buildings and construction waste disposal sites through HRSIs and accurately estimate the annual production of urban construction waste. The MT-AENet introduces a novel encoder–decoder architecture. In the encoder, ResNet-101 is utilized to extract high-level semantic features. A depthwise separable-atrous spatial pyramid pooling (DS-ASPP) module with different dilation rates is constructed to address insufficient receptive fields, resolving the issue of discontinuous holes when extracting large targets. A dual-attention mechanism module (DAMM) is employed to better preserve positional and channel details. In the decoder, multi-scale feature fusion (MS-FF) is utilized to capture contextual information, integrating shallow and intermediate features of the backbone network, thereby enhancing extraction capabilities in complex scenes. The MT-AENet is used to extract buildings and construction waste at different periods in the study area, and the actual production and landfill volume of construction waste are calculated based on area changes, indirectly measuring the rate of urban construction waste resource conversion. The experimental results in Changping District, Beijing demonstrate that the MT-AENet outperforms existing baseline networks in extracting buildings and construction waste. The results of this study are validated according to government statistical standards, providing a promising direction for efficiently analyzing the consumption of urban renewal.

Local Cultures of Advice Tie Formation

Abstract Informal social networks and especially advice networks are a key subject of organizational sociological research. The formation of these networks has so far been explained on the basis of universal tie-formation mechanisms. However, recent conceptual contributions suggest that tie formation practices follow cultural rules that may vary across social contexts. In our paper, we substantiate this argument empirically. Based on rich qualitative data, we compare advice tie formation (a) between two entrepreneurial communities and (b) between three research and development departments of large enterprises. We show that both impact and configuration of tie formation mechanisms systematically differ between comparable contexts. Building on our findings, we finally formulate propositions describing which advice network structures can be expected in specific cultures.

#2856 Sleep disturbances in non-dialysis dependent chronic kidney disease—The CKD-REIN cohort study

Abstract Background and Aims Sleep disturbances are common in patients on dialysis, but little is known about their prevalence and determinants in earlier chronic kidney disease (CKD) stages. We assessed the prevalence of insomnia, excessive daytime sleepiness (EDS), and sleep apnea, and studied their relation with kidney function, before and after accounting for various other potential determinants in patients with moderate or severe CKD. Method This study included 2657 nephrology outpatients with non-dialysis dependent CKD (mean age, 67 ± 12 years; 66% men; mean eGFR, 33 ± 12 mL/min/1.73 m2) from the French CKD-REIN (CKD-Renal Epidemiology and Information Network) cohort study who completed the Basic Nordic Sleep Questionnaire at baseline. Insomnia was defined as having badly or rather badly been sleeping, or having had difficulties falling asleep or awakened too early without being able to fall asleep again at least 3 times per week, over the past 3 months. And EDS, as feeling excessively sleepy during daytime at least 3 times per week over the past 3 months. Sleep apnea with or without continuous positive airway pressure use were identified from medical records. For each sleep disturbance, we assessed the crude prevalence by sex and by estimated glomerular filtration rate (eGFR) categories. We estimated adjusted odds ratios (OR) with 95% confidence interval (95% CI) associated with eGFR decrease (per 10 mL/min/1.73 m2) and urinary albumin-to-creatinine ratio (ACR) categories (A1-A3), as well as with age, sex, education (years), smoking, alcohol consumption, physical activity (estimated by the Global Physical Activity Questionnaire), performance score in Instrumental Activities of Daily Living [IADL], obesity, diabetes, cardiovascular disease, depression, patient-reported cramps, itching and stress or worry about CKD, and relevant drug use. Results Overall prevalence of insomnia, EDS, and sleep apnea were 23%, 15%, and 14%, respectively, with differences between women and men for insomnia, 30% vs 19%, EDS, 17% vs 13%, and sleep apnea, 10% vs 17%, respectively (all P-values <.001). Crude prevalence of EDS significantly increased with decreasing eGFR category (without interaction with sex), but not those for insomnia and sleep apnea (Figure). ACR categories were not associated with either sleep disturbances. In multivariable analyses, factors significantly associated with higher prevalence of insomnia included female sex, smoking, low physical activity, depression, and thyroid hormone use. There were also strong dose-response relations between insomnia prevalence and to what extend patients reported being bothered by cramps or itching, or stressed/worried about CKD. The crude relation of eGFR with EDS tended to weaken (ORs [95% CI], 1.10 [0.99-1.22] per 10 mL/min decrease, p-value 0.09) in multivariable analysis, which showed higher ORs associated with past smoking, depression, lower IADL score, insomnia, and betablocker use, as well as dose-response relations with itching, and stress/worry about CKD. Factors significantly associated with increased prevalence of sleep apnea included male sex, alcohol consumption, lower IADL score, obesity, depression, and cardiovascular disease. Conclusion This analysis of baseline data from the CKD-REIN cohort shows that, in patients with non-dialysis dependent CKD, EDS alone appeared to be related to kidney function level. The study, however, suggests the potential role of modifiable CKD-related symptoms, cramps and itching, and of being stressed or worried about CKD, in addition to that well-documented for behavioral and clinical risk factors, and drugs in the risk of insomnia and EDS.

#2587 Survival and access to renal transplantation of patients with type 1 diabetes: a study from the REIN registry

Abstract Background and Aims One of the most severe complication of type 1 diabetes (D1) is renal injury which can lead to end stage renal disease. Even though improvement have been made to slow the progression of the renal disease, some of D1 patients still reach dialysis. Those patients seem to have more comorbidities and poor prognosis compare to patients without diabetes (D0) starting dialysis or patients with type 2 (D2) diabetes. Method The French Renal Epidemiology and Information Network (REIN) registry collects information on all patients starting dialysis in France. We used this cohort to explore survival and access to renal transplantation of D1 on dialysis patients compare to D0 and D2 patients, using Cox proportional hazard models and graphical representation of the variables to explore the pathway from type 1 diabetes to the outcomes of interest. We performed sensitivity analysis on the imputed base obtain by MICE (in order to reduce the influence of missing data) to compare HR. Results From January 1, 2010 to December 31,2016, 1596 D1 patients were compared to 39540 D0 patients and 28170 D2 patients. Excess in all-cause mortality rate were 1.26 [1.14-1.40] (p < 0.001) for D1 versus D0 and rises to 1.45 [1.32-1.59] (p < 0.001) when considering diabetes-related comorbidities on the pathway from diabetes to death. D1 had a poorer access to kidney transplantation (0.74 [0.66-0.82] p < 0.001) adjusting for non-diabetes-related comorbidities. Using the survival model to compare D1 and D2 on the imputed bases, we obtained an HR: 1.11; 95% CI: 1.01-1.21 p = 0.027, in favor of a worse survival of D1 patients in dialysis compare to D2 patients Conclusion These results confirm that D1 patients starting dialysis are severely ill and particular care need to be given to detect and so reduce diabetes-related comorbidities and increase access to kidney transplantation.

Expatriate voice: The effects of nationality and social status

AbstractThe expression of voice is valuable for organisations and individuals but most of what we know about voice is drawn from western contexts. This paper focuses on why, how and on what issues expatriates speak up. Examining voice in the multinational context of the United Arab Emirates allows us to develop a more in‐depth understanding of national, organisational, and individual factors that influence voice. Interviews with 71 expatriates and 29 host country nationals show that expatriates' social status influences their attitude and behaviour towards voice and that informal structures and social networks in organisations are important in understanding expatriates' voice behaviours. The overlapping categories of expatriates from non‐western countries and expatriates in low‐status jobs take a wider view of their situation and are as a result less likely to use voice in the workplace.

Community-led approaches to the academic development of pracademics in universities: evidence from Ghana

ABSTRACT This paper examines how pracademics who teach at different universities in Ghana contribute to shaping their academic development. In this study, a pracademic refers to an industry practitioner who is employed by a university as a part-time academic to teach academic courses and support the knowledge and skills development of learners. Using a dialogical narrative approach, data were gathered from 14 pracademics who teach academic courses at different universities in Ghana. The findings revealed that the academic development of pracademics is linked to their unique professional identity, the social networks that support their personal and professional growth, and the complex professional community in which they operate. The narratives of the participants revealed that the network of pracademics serves to promote their common interests, which include needs (social, job-related, and resources), expectations, access to opportunities, and their ability to promote academic disciplines. Additionally, pracademics are likely to be highly motivated to join informal networks within a broad context – pracademia, when they know that issues concerning their job security and professional development in the areas of teaching, research, and engaged scholarship will be prioritised by the management of universities.

Verification of the accuracy of dynamic navigation for conventional and mouthpiece methods: in vivo study.

Dynamic navigation for implant placement is becoming popular under the concept of top-down treatment. The purpose of this study is to verify the accuracy of a dynamic navigation system for implant placement. Implant placement was performed on 38 patients using 50 implant fixtures. Patients in group C were treated using a conventional method, in which thermoplastic clips were fixed to the teeth, and patients in group M were treated using thermoplastic clips fixed to a mouthpiece attached to the teeth. The groups were compared to verify whether an accuracy difference existed. A treatment planning support program for dental implants was used to superimpose the postoperative computed tomography data on the preoperative implant design data to measure the entry point, apex point, and angular deviation. The accuracy of group C was 1.36 ± 0.51mm for entry point, 1.30 ± 0.59mm for apex point, and 3.20 ± 0.74° for angular deviation. The accuracy of group M was 1.06 ± 0.31mm for the entry point, 1.02 ± 0.30mm for the apex point, and 2.91 ± 0.97° for angular deviation. Significant differences were observed in the entry and apex points between the two groups. The results indicate that group M exhibited better accuracy than group C, indicating that the stability of the thermoplastic clip is important for ensuring the accuracy of the dynamic navigation system. No previous studies have verified the accuracy of this system using the mouthpiece method, and additional data is required to confirm its accuracy for dental implant placement. The mouthpiece method improves the accuracy of implant placement and provides a safer implant treatment than the conventional method. University hospital Medical Information Network Clinical Trials Registry (UMIN-CTR), Registration Number: UMIN000051949, URL: https://center6.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view_his.cgi on August 21, 2023.

Associations between tobacco smoking status and patch test results-A cross-sectional pilot study from the Information Network of Departments of Dermatology (IVDK).

Earlier studies suggested a potential association between tobacco smoking and nickel sensitization, but little is known about other contact allergens. To investigate the association of smoking status and contact sensitizations as well as subtypes of dermatitis, and to analyse the sensitization profiles of tobacco smokers. Within the Information Network of Departments of Dermatology (IVDK), we performed a cross-sectional multicentre pilot study comprising 1091 patch-tested patients from 9 departments, comparing 541 patients with a history of cigarette smoking (281 current and 260 former smokers) with 550 never-smokers. We could not confirm the previously reported association between nickel sensitization and tobacco smoking. Moreover, sensitizations to other allergens, including colophony, fragrance mix I, Myroxylon pereirae and formaldehyde, were not increased in cigarette smokers compared with never smokers. Hand dermatitis (50.6% vs. 33.6%) and occupational cause (36.2% vs. 22.5%) were significantly more frequent among cigarette smokers compared with never-smokers as shown by non-overlapping 95% confidence intervals. Although our study does not allow a firm conclusion on whether smoking status contributes to certain contact sensitizations, it confirms an association of smoking with hand dermatitis and occupational cause.

Vulnerability Analysis of UAV Swarm Network with Emergency Tasks

With the rapid development of Unmanned Aerial Vehicle (UAV) technology, UAV swarms are used for emergency tasks in various scenarios such as area detection, fire rescue, logistics, and transportation. However, for complex scenarios, UAV swarms are prone to environmental interference that damages their equipment or disrupts their communication links, affecting the normal execution of tasks. In this paper, an information–communication interdependent network model is designed for the vulnerability analysis of UAV swarm networks with emergency tasks. Firstly, from the perspective of network functions of a UAV swarm, we introduce the theory of interdependent networks to abstract the relationship between the UAV swarm’s communication network and its information network, where the communication network represents its communication topology and the information network is related to the function of each UAV individual in the UAV swarm. Then, the vulnerability of the UAV swarm is analyzed according to the relationship between network construction costs and network connectivity under environmental interference. Finally, the effectiveness of the vulnerability analysis method is verified through simulation.

Fast computation of General SimRank on heterogeneous information network

Similarity computation is a fundamental aspect of information network analysis, underpinning many research tasks including information retrieval, clustering, and recommendation systems. General SimRank (GSR), an extension of the well-known SimRank algorithm, effectively computes link-based global similarities incorporating semantic logic within heterogeneous information networks (HINs). However, GSR inherits the recursive nature of SimRank, making it computationally expensive to achieve convergence through iterative processes. While numerous rapid computation methods exist for SimRank, their direct application to GSR is impeded by differences in their underlying equations. To accelerate GSR computation, we introduce a novel approach based on linear systems. Specifically, we transform the pairwise surfer model of GSR on HINs into a new random walk model on a node-pair graph, establishing an equivalent linear system for GSR. We then develop a fast algorithm utilizing the local push technique to compute all-pair GSR scores with guaranteed accuracy. Additionally, we adapt the local push method for dynamic HINs and introduce a corresponding incremental algorithm. Experimental results on various real datasets demonstrate that our algorithms significantly outperform the traditional power method in both static and dynamic HIN contexts.

Modeling the dissemination of privacy information in online social networks

AbstractDriven by the rapid development of information technology, online social networks (OSNs) have experienced a fast development in recent years, allowing increasingly more people to share and spread information over OSNs. The rapid rise of OSN platforms such as Facebook and Twitter is sufficient evidence of such development. As one type of information, privacy information can also be created and disseminated over an OSN, posing a severe threat to individual privacy. This article attempts to construct a model for disseminating privacy information in OSNs and to analyze the model by simulating the dissemination process of privacy information in OSNs. First, we establish network models that exhibit the main characteristics of OSNs. Second, by considering the factors related to social relationships, especially intimacy between users and the attention of users to the privacy subject, we derive the parameters for privacy information dissemination models in OSNs. Third, based on the theory of information dissemination dynamics, we construct a model for information dissemination that conforms to the properties of privacy information. We also present some experimental results based on the constructed model and analyze the characteristics of privacy information dissemination. Fourth, we study and verify the various properties of the model through a set of experiments. The proposed model provides the opportunity to better understand the dynamics of privacy information dissemination in OSNs and the effect of user behavior on dissemination.

Analytical Properties and Numerical Preservation of an Age-Group Susceptible-Infected-Recovered Model: Application to the Diffusion of Information

Abstract This paper analyzes an age-group susceptible-infected-recovered (SIR) model. Theoretical results concerning the conservation of the total population, the positivity of the analytical solution, and the final size of the epidemic are derived. Since the model is a nonlinear system of ordinary differential equations (ODEs), a numerical approximation is considered, based on Standard and non-Standard Finite Difference methods, and on a Modified Patankar-Runge–Kutta (MPRK) method. The numerical preservation of the qualitative properties of the analytical solution is studied. The obtained results are applied to the diffusion of information in social networks, and the effectiveness of the different numerical approaches is shown through several numerical tests on real data.

Genetic assessment of diverse rice germplasm for yield-related and stress tolerance traits

AbstractThe development of stress-resistant crops without compromising their yield and nutritional quality is one of the major challenges in plant biology. Plant breeders have made considerable effort in developing cultivars with high yield and tolerance to biotic and abiotic stresses. This research aimed to evaluate pre-breeding resources for favorable genes conferring good yield and resistance to biotic and abiotic stresses for rice varietal improvement. Two hundred forty diverse rice cultivars from top countries with which the United States imports and exports rice were selected from the Germplasm Resource Information Network (GRIN) collection, and these were characterized using molecular markers for various desirable traits. Among the cultivars, 16 were used as checks. The genes of various traits evaluated were grain number (Gn1a), panicle weight (WFP), cold stress tolerance (qSCT-11 and qCTS-12), anaerobic germination (AG1 and AG2), rice blast disease resistance (PiB, Pi-ta, Ptr, and Piz), wide compatibility (S5-n), and restoring ability (Rf4). Out of these 240 accessions, 192 have the Gn1a gene, 168 possess the qSCT-11 cold tolerance gene, 139 carry the Rf4 gene, 152 have the qCTS12 cold tolerance gene, 87 have blast resistance genes, and 44 possess the WC gene. None of the 240 accessions possess the WFP (Wealthy Farmer’s Panicle) gene. The selections with favorable genes can be used by breeders as potential donor parents for varietal improvement programs to produce new and promising cultivars with desired specific traits that may contribute towards alleviating global issues related to food security.

The biomedical knowledge graph of symptom phenotype in coronary artery plaque: machine learning-based analysis of real-world clinical data.

A knowledge graph can effectively showcase the essential characteristics of data and is increasingly emerging as a significant means of integrating information in the field of artificial intelligence. Coronary artery plaque represents a significant etiology of cardiovascular events, posing a diagnostic challenge for clinicians who are confronted with a multitude of nonspecific symptoms. To visualize the hierarchical relationship network graph of the molecular mechanisms underlying plaque properties and symptom phenotypes, patient symptomatology was extracted from electronic health record data from real-world clinical settings. Phenotypic networks were constructed utilizing clinical data and protein‒protein interaction networks. Machine learning techniques, including convolutional neural networks, Dijkstra's algorithm, and gene ontology semantic similarity, were employed to quantify clinical and biological features within the network. The resulting features were then utilized to train a K-nearest neighbor model, yielding 23 symptoms, 41 association rules, and 61 hub genes across the three types of plaques studied, achieving an area under the curve of 92.5%. Weighted correlation network analysis and pathway enrichment were subsequently utilized to identify lipid status-related genes and inflammation-associated pathways that could help explain the differences in plaque properties. To confirm the validity of the network graph model, we conducted coexpression analysis of the hub genes to evaluate their potential diagnostic value. Additionally, we investigated immune cell infiltration, examined the correlations between hub genes and immune cells, and validated the reliability of the identified biological pathways. By integrating clinical data and molecular network information, this biomedical knowledge graph model effectively elucidated the potential molecular mechanisms that collude symptoms, diseases, and molecules.