This paper aims at the shortcomings of traditional citation similarity calculation methods in mining semantic information of documents, and proposes a research on citation similarity algorithm based on semantic link network (SLN). Traditional citation similarity algorithms often only focus on citation relationships while ignoring semantic information, which leads to certain limitations in the accuracy of the algorithms. Firstly, this paper proposes a citation semantic link network model, and then the paper proposes a citation similarity algorithm based on citation semantic link network model. By introducing semantic information, this algorithm can more accurately measure the similarity between documents, effectively improving the accuracy and efficiency of the algorithm. This provides a new approach for document analysis and is of great significance for improving the quality of document retrieval.

BACKGROUND:Abdominal aortic aneurysm (AAA) is a significant global health concern, yet comprehensive population-based studies remain limited. This study aimed to evaluate the hospitalization rates, surgical trends, mortality, and reintervention rates for ruptured (r-AAA) and nonruptured (nr-AAA) AAA using data from a national health insurance database. METHODS:A population-based retrospective cohort study was conducted utilizing data from the Taiwanese National Health Insurance Research Database from 2007 to 2018. The study included individuals aged 20 years and older with a newly diagnosed AAA. RESULTS:Among 70 457 patients diagnosed with aortic aneurysm or dissection, 22 538 (32%) adult patients (≥20 years) were identified with AAA. The annual incidence of AAA ranged from 7.7 to 10.3 per 100 000 population, with r-AAA decreasing from 1.3 to 0.8 per 100 000 and nr-AAA from 9.0 to 6.8 per 100 000. Most patients with AAA were older adults (85%), with 15 392 (68%) hospitalized and 4885 (32%) undergoing surgery within 14 days of diagnosis. The use of endovascular aneurysm repair (EVAR) significantly increased from 28% to 96% over the study period. Long-term survival was higher in patients who underwent open surgical repair (OSR) compared to those who received EVAR or conservative management, irrespective of whether they had r-AAA or nr-AAA. CONCLUSION:AAA predominantly affects older individuals, and the annual incidence shows a declining trend. Since the introduction of EVAR, its use has steadily increased while OSR rates have decreased. Although both EVAR and OSR are associated with reduced mortality in patients with r-AAA, OSR is linked to superior long-term survival outcomes.

Three-dimensional printing (3DP) is an evolving technology with a wide range of medical applications. It complements the traditional methods of visualizing the cardiovascular anatomy and assists in clinical decision making, especially in the planning and simulation of percutaneous surgical procedures. The doctor–patient relationship has changed substantially, and patients have become increasingly aware of their rights and proactively make decisions regarding their treatment. We present our experience in using 3DP for aortic repair, preoperative surgical decision making for congenital heart disease, and simulation-based training for junior vascular surgeons. 3DP can revolutionize individualized treatment, especially for congenital heart disease, which involves unique anatomy that is difficult to examine using traditional computed tomography. As cardiovascular medicine and surgery require increasingly complex interventions, 3DP is becoming an essential technology for surgical instructors and trainees, who can learn to become responsible and humane medical doctors. 3DP will play an increasingly crucial role in the future training of surgeons.

BACKGROUND: The increasing prevalence of end-stage renal disease (ESRD) imposes a substantial economic burden on public health-care systems. Hemodialysis (HD) is a pivotal treatment modality for patients with ESRD. However, prolonged use of HD vessels may result in stenosis, thrombosis, and occlusion due to repeated daily punctures. Thus, early detection and prevention of the dysfunction of dialysis routes are crucial. OBJECTIVE: In this study, we designed a wearable device for the early and accurate detection of arteriovenous access (AVA) stenosis in HD patients. METHODS: A personalized three-dimensional (3D) printed wearable device was designed by combining the phonoangiography (PAG) and photoplethysmography (PPG) techniques. The capability of this device to monitor AVA dysfunction before and after percutaneous transluminal angioplasty (PTA) was evaluated. RESULTS: After PTA, the amplitudes of both PAG and PPG signals increased in patients with arteriovenous fistulas and those with arteriovenous grafts; this might be due to increased blood flow. CONCLUSION: Our designed multi-sensor wearable medical device using PAG, PPG, and 3D printing appears suitable for early and accurate detection of AVA stenosis in HD patients.

Three-dimensional printing is a developing technology with several medical uses. Not only can it be used to complement the visualizing anatomy of the organs and circulatory system and aid in clinical decision-making, but it is also a helpful tool for planning and modeling surgical procedures. A senior person with acute kind The patient with an aortic dissection declined open surgery due to his advanced age, many comorbidities, and considerable surgical risk. We provide our expertise in rebuilding the diseased state of the aorta with 3Dp, explain the spatial link to establish the potential of the curative impact of thoracic endovascular stent graft revascularization (TEVAR), and effectively treat this patient. Due to the inability of conventional computed tomography's two-dimensional imaging to completely depict the unique anatomical structure of the study subject, it is necessary for the surgeon to utilize his or her imagination to form the picture in the brain. The use of 3Dp may transform a picture into a comprehensive three-dimensional structure and apply it; this may radically alter the individualized treatment strategy as a whole. Here, we used computed tomography scans and 3D Slicer software to recreate the ascending aorta's 3D image structure. Its primary objective is to elucidate the anatomical anatomy and spatial geometry of the ascending aorta burst hole. To patch the burst hole, we designed and utilized three short stent grafts instead of one large stent graft. 3Dp may provide unique preoperative planning and solutions for some presently limited procedures. With the advent of more complicated, minimally invasive, or endovascular surgery, 3Dp may become an essential platform for surgical instructors and trainees to engage and undertake surgical training.

By using on-demand, multi-sensor wearable 3D printed medical devices, not only can we early detect the arteriovenous access dysfunction, moreover, we could offer a better solution for holistic hemodialysis patient care. This project is to design a wearable medical device which can measure and monitor the hemodynamically significant stenosis of the dialysis access using sensor of phonoangiography (PAG) for exploring vascular pitch pattern primarily and sensor of Photoplethysmography (PPG) for estimating the flow volume secondarily as a double checking of the AV access condition. A novel function of autoregressive (AR) model was added onto the PAG-based sensors to detect AVA stenosis and simultaneously audit the status of its life cycle by tracking and obtaining changes in frequency spectra domain. It helps hemodialysis patients to be aware earlier of the dysfunction of AVA and reminds them to make a return visit. The purpose of the complement deployment of vital sign sensors into the 3D printed devices is to improve the prognosis and optimize overall health by providing analysis of physiological signals, including water content index, pulse oximetry, and blood pressure at the same time. With these sensors, the concept of holistic hemodialysis patient care (HHPC) might be proved.

Three-dimensional printing is a developing technology with several medical uses. Not only can it be used to complement the visualizing anatomy of the organs and circulatory system and aid in clinical decision-making, but it is also a helpful tool for planning and modeling surgical procedures. A senior person with acute kind The patient with an aortic dissection declined open surgery due to his advanced age, many comorbidities, and considerable surgical risk. We provide our expertise in rebuilding the diseased state of the aorta with 3Dp, explain the spatial link to establish the potential of the curative impact of thoracic endovascular stent graft revascularization (TEVAR), and effectively treat this patient. Due to the inability of conventional computed tomography's two-dimensional imaging to completely depict the unique anatomical structure of the study subject, it is necessary for the surgeon to utilize his or her imagination to form the picture in the brain. The use of 3Dp may transform a picture into a comprehensive three-dimensional structure and apply it; this may radically alter the individualized treatment strategy as a whole. Here, we used computed tomography scans and 3D Slicer software to recreate the ascending aorta's 3D image structure. Its primary objective is to elucidate the anatomical anatomy and spatial geometry of the ascending aorta burst hole. To patch the burst hole, we designed and utilized three short stent grafts instead of one large stent graft. 3Dp may provide unique preoperative planning and solutions for some presently limited procedures. With the advent of more complicated, minimally invasive, or endovascular surgery, 3Dp may become an essential platform for surgical instructors and trainees to engage and undertake surgical training.

Cardiomegaly is an asymptomatic disease. Symptoms, such as palpitations, chest tightness, and shortness of breath, may be the early indications of cardiac hypertrophy, which can be divided into cardiac hypertrophy and ventricular enlargement. Their causes and treatment strategies are different. The early detection of cardiomegaly can help to make decisions for administering drugs and surgical treatments. In addition, with regard to problems in manual inspection, such as time consuming and the need for human interpretations and experiences, an assistive tool is required to automatically develop and identify normal heart or enlarged hearts. Therefore, this study proposes the combination of 2D (two dimensional) and 1D (one dimensional) convolutional neural network based classifier for rapid cardiomegaly screening in clinical applications based on chest X-ray (CXR) examinations in frontal posteroanterior view. The 2D and 1D convolutional processes and multilayer connected classification network are used to enhance the original CXR image and to remove unwanted noises to increase accuracy in feature extraction and pattern recognition tasks. The training dataset and testing dataset are collected from the National Institutes of Health CXR image database, which is used to train the classifier and validate the performance of the classifier in a K-fold cross-validation manner. Experimental results indicate the potential performance for rapid cardiomegaly screening with regard to recall (%), precision (%), accuracy (%), and F1 score.

Patients suffering from end-stage renal disease usually receive dialysis therapy. The arteriovenous (AV) shunt is a vital vascular access for achieving sufficient blood flow in the vein during hemodialysis and can be either an arteriovenous fistula (AVF) or an arteriovenous graft (AVG). However, the lumens of dialysis accesses are frequently narrowed by thrombosis, resulting in stenosis at the venous anastomosis site or progression of inflow stenosis at the arterial anastomosis site. A narrowed vascular wall will produce abnormal physical stress and cause turbulent flow and high blood pressure. Hence, murmur sounds will occur around the stenotic site. The auscultation method is a noninvasive technique to detect these sounds as phonoangiograph (PAG) signals. The empirical mode decomposition (EMD) method is used to decompose intrinsic fast and slow oscillation components from PAG signals. In this study, the slow oscillation component containing key spectral energy distributions (< 100, 100–300 Hz, and > 300 Hz) will be used to identify the normal and abnormal conditions for further stenosis level assessment. After extracting key frequency-based features using EMD, 1D convolutional and pooling processes, feature patterns can be extracted from spectral patterns, which can distinguish distinct feature patterns and reduce the dimensions of feature patterns and the number of feature datasets for training the classifier. Next, a convolutional neural network-based classifier is used to assess the stenosis levels at a near venous anastomosis site. Its model can solve nonlinear mapping applications and nonlinear separable classifications, including the normal condition, AVG stenosis, and AVF stenosis. The experimental tests with cross-validation will indicate that the proposed method provides a promising result in clinical trials, including mean recall (%), mean precision (%), mean accuracy (%), and a mean F1 score of 95.35 %, 89.49 %, 86.82 %, and 0.9232, respectively, by offering an automatic procedure for AV shunt stenosis assessment without the need for manual feature extraction and classification.

Valvular heart diseases or chronic valvular dysfunctions are widely treated by percutaneous pulmonary valve implantation (PPVI) in cases requiring prosthetic pulmonary valve implantation and replacement. Commercial pulmonary valve stents (CPVS), sucas Epic (TM) valved stents or mechanical heart valves, are available to improve narrowed pulmonary valves or address problems of blood flow regurgitation in the right ventricle to pulmonary artery conduit. However, these commercial valve stents have limited availability of different sizes and diameters. Currently, the customized handmade trileaflet-valved conduit (HTVC) is a novel surgical strategy being used in young adults and children. The HTVC can be designed witdifferent optimal parameters and can be reconstructed as expanded polytetrafluoroethylene (ePTFE)-valved conduits for PPVI clinical applications. To verify the availability and durability of the HTVC, we validated its hemodynamic and functional performances using a mock circulation system in an in vitro study. At different heart rates and blood flows, we used the forward stroke flow (systolic period) and regurgitation flow (diastolic period) to calculate the pulmonary regurgitation fraction (RF) and ejection efficiency (EE) to evaluate the HTVC performances. We also observed its dynamic behaviours using an endoscopic camera in a pulsatile experimental setting. In addition, we used a Duffing-Holmes-based chaotic synchronization system to track the trajectories of pulmonary artery pressure waveforms of the HTVC and CPVS, whiccan synchronously obtain the dynamic self-synchronization errors for quantifying the HTVC's performance. Througin vitro laboratory experiments, the comprehensive dynamic errors were found to be positively correlated witthe RFs and EEs. The experimental results indicate that the results obtained by the HTVC are promising, including a decrease in the RF and an increase in the EE, as compared witCPVSs. Therefore, the dynamic errors can be used to obtain rapid quantitative indications of the performance quality of HTVCs under different hemodynamic conditions for RV-PA reconstruction.

Extracorporeal membrane oxygenation (ECMO) is employed to treat critical patients for one to a few days of life support in intensive care units. Venovenous (VV) and venoarterial (VA) ECMO configurations are the most commonly used rescue strategies for temporary cardiac and respiratory function support. However, both ECMO modes sometimes cannot meet a patient's demands because of (a) less oxygenated blood in either the upper body or lower body, or (b) a deterioration in the patient's hemodynamic status. Veno-Venoarterial (VVA) ECMO is an upgraded system that provides sufficiently oxygenated blood to the systemic and pulmonary circulation systems. Drainage cannulas and gas flow exchanges are determined to provide the maximum drainage blood flow required by the patient through a servo-regulator that adjusts the motor speed. A generalized regression neural network (GRNN) based estimator is created to automatically estimate the desired pump speed and then provide sufficient drainage flow for temporary life support. To achieve stability flow in an ECMO circuit, a bisection approach algorithm (BAA) is employed to improve the performance of transient responses in step controls and steady state controls. Experimental studies are used to validate the proposed model and it is compared with conventional controllers to indicate good performance in clinical VVA ECMO applications.

Biogenic microvesicles (MVs) play a pivotal role in intercellular signal communication, thus initiating critical biological responses such as the proliferation of cancer cells, gene and protein transport, and chemo-drug resistance. In addition, they have been recognized as having great potential in drug delivery applications. However, the productivity of biologically produced MVs is not sufficient for clinical applications. In this study, synthetic poly(lactic-co-glycolic acid) (PLGA) MVs were prepared via a double emulsion method. The PLGA MVs had a biogenic MV-mimic vesicular structure with a hydrophilic core/surface and hydrophobic interior of the shell, showing great potential for drug delivery. We successfully embedded hydrophobic iron carbonyl (IC), a carbon monoxide (CO) donor, in the PLGA shell region, enabling the delivery of IC in an aqueous solution. Because of the intrinsic properties of PLGA, it was susceptible to temperature, and the MVs could easily collapse in a warm environment, leading to the decomposition of IC into CO. The in vitro result indicated that the cell viability of A549 lung carcinoma cells significantly decreased to 14% after treatment with IC-loaded PLGA MVs for 24 h, suggesting that these synthetic PLGA MVs constitute an excellent drug delivery platform.

Analyzing digitalized hand-drawn patterns, such as Archimedes' spirals, words, and sentences, is one strategy for evaluating functional tremors and upper-limb movement disorders for neurodegenerative diseases. A pattern, such as a spiral or a line, in polar coordinates is a straight line or a curve that can be easily compared to a hand-drawn pattern with the same coordinates. Hence, in this study, using polar expression features, the deviation (cm), the accumulation angle (rad), and the drawing velocity (cm/s) were extracted to scale the variability in different tremor levels associated with Parkinson's disease (PD) or essential tremor (ET). Then, a nonlinear support vector machine (SVM)-based classifier was used to separate the normal condition from PD or ET. The classifier was trained by the wolf pack search (WPS) optimization method. Tremor level progress was evaluated by integrating an assistive method in a smart mobile device (iPad), along with the nonlinear SVM-based classifier, into a decision-making system for individualized functions. In contrast to the multilayer machine learning method, with the 8-fold cross-validation, the proposed classifier exhibits superior performance in identifying normal controls and PD or ET, with the mean true positive, mean true negative, and mean hit rates being 93.72%, 86.79%, and 90.84%, respectively. The experimental results indicated that the proposed decision-making method is effective for detecting the progression of Parkinson-related diseases. This examination technique is simple, comfortable, and repeatable for helping neurologists with preliminary diagnoses, drug treatments, and at-home monitoring.

Digitalized hand-drawn pattern is a noninvasive and reproducible assistive manner to obtain hand actions and motions for evaluating functional tremors and upper-limb movement disorders. In this study, spirals and straight lines in polar coordinates are used to extract polar expression features such as the key parameters deviation (cm) and accumulation angle (rad). These parameters are quantitative manner to scale the variations of functional tremors in normal control subjects and patients with Parkinson’s disease (PD) and essential tremor (ET). However, difficulty arises in using nonlinear polar expression features in the two-dimensional feature space to separate normal control subjects from those with PD and ET. To solve the nonlinear separable classification problem, hash transformation is used to map polar expression features to a high-dimensional space using hash weighing function and modulo operation. Then, a machine learning method, such as the generalized regression neural network (GRNN), is implemented to train a decision-making classifier using the particle swarm optimization (PSO) algorithm for possible class assessment. With the enrolled data from 50 subjects, the fivefold cross validation, mean true positive, mean true negative, and mean hit rates of 98.93%, 98.96%, and 98.93%, respectively, are obtained to quantify the performance of the proposed decision-making classifier to identify normal controls and subjects with PD or ET. The experimental results indicate that the proposed screening model can improve the accuracy rate compared with the conventional machine learning classifier.

The ever-increasing incidence of end-stage renal disease (ESRD) has already become a major burden to health budgets and a threat to public health nationwide in Taiwan. According to the United States Renal Data System Annual Data Report in 2015, the prevalence and incidence of ESRD in Taiwan are the highest in the world. Moreover, for the population of 82 thousand ESRD patients receiving hemodialysis treatments, the total cost is up to NT$34.2 billion annually out of the National Health Insurance (NHI) program budget. This project is to design a wearable medical device which can measure and monitor the fluid dynamics of the dialysis access using sensor of phonoangiography (PAG) for exploring vascular pitch pattern and sensor of Photoplethysmography (PPG) for estimating the flow volume as a double checking of the AV access condition. We use arteriovenous access (AVA) stenosis detector based on phonoangiography technique and autoregressive model to detect access stenosis and simultaneously estimate the status of AVA life cycle by tracking and obtaining changes in frequency spectra domain. It helps hemodialysis patients to be aware earlier of the dysfunction of AVA and reminds them to make a return visit. The purpose of the complement deployment of vital sign sensors is to improve the prognosis and optimize overall health by providing analysis of physiological signals, including water content index, pulse oximetry, and blood pressure at the same time. With these sensors, the concept of holistic hemodialysis patient care (HHPC) might be proved.

Peripheral arterial disease (PAD) is highly prevalent in haemodialysis (HD) patients with type 2 diabetes. Atherosclerosis may occur in both lower and upper peripheral arteries, causing progressive dialysis access stenosis in HD patients. To assess the risk of PAD, non-invasive bilateral photoplethysmography (PPG) can be used to obtain continuous variations in blood flow volume in in vivo examinations. The authors propose an astable multivibrator to model the peripheral circulation system and to produce PPG oscillation with time constants, duty ratio (rising time), and amplitude ratio of systolic and diastolic pressures. Then, the bilateral differences in the time constant and duty ratio are used to separate the normal condition from PAD. The machine learning decision-making process utilises a screening method to automatically detect subjects with and without the risk of PAD. The radial-based function is employed to parameterise the similarity and dissimilarity levels using probability values. Colour relation analysis incorporates the probability values into the perceptual colour relationships for PAD screening. The experimental results indicate that in comparison with bilateral timing parameters, degree of stenosis, and resistive index, the proposed screening method is efficient in preventing complications of PAD and is easily implemented in an embedded system.

Functional tremors are clear symptoms of neurodegenerative diseases; as such, they indicate the progression of Parkinson's disease (PD). Digitized handwritten pattern analysis of Archimedes' spirals, words, and sentences can help evaluate movement discords in the upper limbs. It offers a simple, comfortable, and repeatable method of examination for clinical applications and at-home monitoring usages. Upper limb tremors can be found in PD, essential tremor (ET), and cerebellar disorders. This paper proposes a quantitative method to scale the variations of functional tremors. The deviation (in cm) and the accumulation angle (in rad) of the feature pattern in polar expression were extracted to scale the variability at different tremor levels. Then, the proposed intelligent classifier, which is used as a perceptual color representation-based classifier (PCRC) and comprises a radial Bayesian network and a color relation analysis method, was employed to screen PD or ET with perceptual color representation. An assistant tool can integrate a smart mobile device (iPad/smartphone) and PCRC into the decision support system for individualized functions to evaluate the progression of the tremor level. The proposed decision support system was validated using data collected from 50 subjects. With fivefold cross-validation, average true positive, average true negative, and hit rates of 92.02%, 88.17% and 90.44%, respectively, were obtained to quantify the performance of the proposed classifier for identifying normal controls and PD or ET.

Long-term repeating traumatic puncture is required for dialysis therapy, which results in frequent thrombosis and graduate vascular access stenosis, such as inflow or outflow stenosis and coexistence of both. An arteriovenous graft has a higher patency rate than an arteriovenous fistula. This study intends to use the dual-channel auscultation-based non-invasive method to screen inflow and outflow stenoses. Frequency analysis is used to decompose phonoangiography (PAG) signals to frequency features using the different data length of acoustic data. Burg autoregressive method is employed to extract the key frequency parameters from sufficient spectral data, including characteristic frequencies and distinct peaks of power spectral densities (PSDs). In big data processing, PSDs and the degree of stenosis (DOS) have been validated to show a positive correlation with sufficient big spectral data. An intelligent machine learning model, bidirectional hetero-associative memory network (BHAMN), is carried out to identify the level of DOS at the inflow site, the mid-site, or the outflow site of a vascular access. The experimental results will indicate that the proposed intelligent machine learning model has higher hit rates.

The use of nasogastric (NG) tubes in acute, critical, and long-term care may lead to mechanical, infectious, and metabolic complications. NG intubation is a risk factor for aspiration and complications of organ injury. Mechanical complications include deliberate self-extubation and accidental extubation, both of which comprise unplanned extubation and occur in >35% of cases in rehabilitation rooms. Therefore, we intend to propose a digital warning tool to detect NG tube dislodgment over several days or weeks for a continuous insertion of the NG tube. On the basis of fog computing, integrating dexter-to-sinister light-controlled sensors and fuzzy Petri net (FPN) was performed to achieve the proposed assistant tool. The proposed intelligent algorithm can also be easily implemented using a high-level programming language (Language C/C++) in an embedded system. The experimental results demonstrated the feasibility of the algorithm under normal conditions and partial and NG two-tube dislodgments.