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Journal of Healthcare Engineering
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  This is an Open Access Journal Open Access journal
ISSN (Print) 2040-2295 - ISSN (Online) 2040-2309
Published by Hindawi Homepage  [339 journals]
  • Predictive Modeling of Short-Term Poor Prognosis of Successful Reperfusion
           after Endovascular Treatment in Patients with Anterior Circulation Acute
           Ischemic Stroke

    • Abstract: This study aimed to propose and internally validate a prediction model of short-term poor prognosis in patients with acute ischemic stroke (AIS). In the retrospective study, 356 eligible AIS patients receiving endovascular treatment (EVT) were included and divided into the good prognosis group and the poor prognosis group. Data from 70% of patients were collected as training set and the 30% as validation set. Univariate analysis and multivariate logistic regression were used for identifying independent predictors. The performance of the model was evaluated by receiver operating characteristic (ROC) curve and the paired Chi-square test was used for internal validation. A model for the prediction of short-term poor prognosis in atherosclerotic AIS patients who successfully underwent endovascular reperfusion was developed: log (Pr/1 − Pr) = 3.500 + Blood glucose  0.174 + Infarct volume  0.128 + the National Institutes of Health Stroke Scale score × Onset-to-reperfusion time (NIHSS-ORT)  0.014 + Intraoperative hypotension (Yes)  1.037 + Mean arterial pressure (MAP) decrease from baseline (>40%) 2.061 (Pr represented the probability of short-term poor prognosis). The area under the curve (AUC) was 0.806 (0.748 − 0.864) in the training set and 0.850 (0.779 − 0.920) in the testing set, which suggested the good performance of the model. We proposed and validated a combined prediction model to predict the short-term poor prognosis of AIS patients after EVT, which could provide reference for clinicians to identify AIS patients with a higher risk of poor outcomes and thus improving the prognosis of EVT.
      PubDate: Fri, 12 Aug 2022 12:20:00 +000
  • Current Status and Factors Associated with Clean Operating Rooms: A Survey
           of Hospitals in China

    • Abstract: Background. Indoor air quality is controlled in the clean operating room (OR) to reduce the risk of surgical-site infections (SSIs). The aim of this study is to assess the usage and management of clean ORs in China and to identify factors associated with the risk of SSIs. Methods. An online survey was distributed to hospitals in China from August 5 to September 5, 2018 via the WeChat account of the Shanghai International Forum for Infection Control and Prevention. The questionnaire consisted of two parts: basic information (hospital type, level, and number of beds) and usage and management (number of ORs, usage time, maintenance mode, test frequency, compliance with current standards, and comfort of healthcare workers). The significance of factors associated with the cleanliness and maintenance of clean ORs was assessed by univariate and multivariate logistic regression analyses. Results. Among 1,308 responding hospitals, 25.7% failed to comply with current standards. “Maintenance mode” had a significant effect on compliance with current standards for clean ORs () and “professional” maintenance was superior to “outsource or no” maintenance (odds ratio = 0.511, 95% confidence interval = 0.367–0.711). There was a significant difference in the comfort of healthcare workers in clean ORs that complied with current standards vs. those that did not (39.92% [388/972] vs. 64.28% [216/336], respectively, ). Humidity was the chief complaint among healthcare workers. Conclusion. Maintenance of clean ORs was significantly associated with the compliance of current standards. Noncompliance with current standards was associated with greater risks of SSIs. Maintenance of ORs for prevention of SSIs should consider the costs and benefits.
      PubDate: Fri, 12 Aug 2022 11:20:00 +000
  • Pneumonia Detection in Chest X-Ray Images Using Enhanced Restricted
           Boltzmann Machine

    • Abstract: The process of pneumonia detection has been the focus of researchers as it has proved itself to be one of the most dangerous and life-threatening disorders. In recent years, many machine learning and deep learning algorithms have been applied in an attempt to automate this process but none of them has been successful significantly to achieve the highest possible accuracy. In a similar attempt, we propose an enhanced approach of a deep learning model called restricted Boltzmann machine (RBM) which is named enhanced RBM (ERBM). One of the major drawbacks associated with the standard format of RBM is its random weight initialization which leads to improper feature learning of the model during the training phase, resulting in poor performance of the machine. This problem has been tried to eliminate in this work by finding the differences between the means of a specific feature vector and the means of all features given as inputs to the machine. By performing this process, the reconstruction of the actual features is increased which ultimately reduces the error generated during the training phase of the model. The developed model has been applied to three different datasets of pneumonia diseases and the results have been compared with other state of the art techniques using different performance evaluation parameters. The proposed model gave highest accuracy of 98.56% followed by standard RBM, SVM, KNN, and decision tree which gave accuracies of 97.53%, 92.62%, 91.64%, and 88.77%, respectively, for dataset named dataset 2. Similarly, for the dataset 1, the highest accuracy of 96.66 has been observed for the eRBM followed by srRBM, KNN, decision tree, and SVM which gave accuracies of 90.22%, 89.34%, 87.65%, and 86.55%, respectively. In the same way, the accuracies observed for the dataset 3 by eRBM, standard RBM, KNN, decision tree, and SVM are 92.45%, 90.98%, 87.54%, 85.49%, and 84.54%, respectively. Similar observations can also be seen for other performance parameters showing the efficiency of the proposed model. As revealed in the results obtained, a significant improvement has been observed in the working of the RBM by introducing a new method of weight initialization during the training phase. The results show that the improved model outperforms other models in terms of different performance evaluation parameters, namely, accuracy, sensitivity, specificity, F1-score, and ROC curve.
      PubDate: Fri, 12 Aug 2022 07:05:00 +000
  • Sulforaphane Upregulates Cultured Mouse Astrocytic Aquaporin-4 Expression
           through p38 MAPK Pathway

    • Abstract: Protein misfolding and/or aggregation are common pathological features associated with a number of neurodegenerative diseases, including Alzheimer’s disease (AD) and Parkinson disease (PD). Abnormal protein aggregation may be caused by misfolding of the protein and/or dysfunction of the protein clearance system. Recent studies have demonstrated that the specific water channel protein, aquaporin-4 (AQP4), plays a role in the pathogenesis of neurodegenerative diseases involving protein clearance system. In this study, we aimed to investigate the role of sulforaphane (SFN) in the upregulation of AQP4 expression, along with its underlying mechanism using cultured mouse astrocytes as a model system. At low concentrations, SFN was found to increase cell proliferation and result in the activation of astrocytes. However, high SFN concentrations were found to suppress cell proliferation of astrocytes. In addition, our study found that a 1 μM concentration of SFN resulted in the upregulation of AQP4 expression and p38 MAPK phosphorylation in cultured mouse astrocytes. Moreover, we demonstrated that the upregulation of AQP4 expression was significantly attenuated when cells were pretreated with SB203580, a p38 MAPK inhibitor. In conclusion, our findings from this study revealed that SFN exerts hormesis effect on cultured mouse astrocytes and can upregulate astrocytic AQP4 expression by targeting the p38 MAPK pathway.
      PubDate: Wed, 10 Aug 2022 12:50:00 +000
  • The Relationship between Burnout and Intention to Leave Work among
           Midwives: The Long-Lasting Impacts of COVID-19

    • Abstract: Objective. It is important to evaluate the long-term effects of the COVID-19 pandemic on the intention of midwives to leave their jobs. The study examined the relationship between burnout and the intent to leave work among midwives who worked at Ayatollah Mousavi Hospital of Zanjan, one year after the COVID-19 outbreak. Method. In a descriptive-analytical study, the intention of 88 midwives to leave their jobs was evaluated, one year after the outbreak of COVID-19 disease in 2021. The midwives were selected using convenience sampling methods. Data were collected using the Maslach burnout questionnaire and the Anticipated Turnover Scale (ATS). Data were analyzed with descriptive statistics, Chi-square test, Pearson correlation coefficient, and multiple linear regression model with the stepwise method at a 95% confidence level. Results. The mean intention to leave the job was 29.71 ± 6.75. Most of the midwives reported a moderate level of intention to leave the job (47.7%). There was a significant positive correlation between the intention to leave the job and all three components of burnout. The stepwise regression analyses indicated that emotional exhaustion (β = 0.344) and working rotational shifts (β = 0.276) were significant predictors of intent to leave the job. Conclusions. It can be concluded that the intention to leave the job of midwives was moderate. Given the relationship between emotional exhaustion and the intent to leave the job, interventions to increase the mental strength and resilience of midwives during the COVID-19 pandemic seem necessary.
      PubDate: Tue, 09 Aug 2022 10:50:00 +000
  • Application of the Full-Width-at-Half-Maximum Image Segmentation Method to
           Analyse Retinal Vascular Changes in Patients with Diabetic Retinopathy

    • Abstract: This study used spectral domain optical coherence tomography (SD-OCT) and full-width-at-half-maximum image segmentation to investigate the morphological changes of retinal blood vessels in patients with diabetic retinopathy (DR). Seventy-five patients with type 2 diabetes mellitus (T2DM) without DR and 65 patients with DR were studied. Vascular images of superior temporal region B of the retina were obtained by SD-OCT. The edges of retinal vessels were identified by the full-width-at-half-maximum image segmentation method. The lumen diameter, wall thickness (WT), wall cross-sectional area (WCSA), and wall-to-lumen ratio (WLR) were investigated. We found that, compared with the no diabetic retinopathy (NDR) group, patients in the DR group had an increased retinal arteriolar lumen diameter (RALD), retinal arteriolar outer diameter (RAOD), and WT (128.80 μm versus 104.88 μm; 147.01 μm versus 135.60 μm; 18.29 μm versus 15.26 μm, , respectively). The retinal venular lumen diameter (RVLD), retinal venular outer diameter (RVOD), and venular WT in the DR group were also increased (146.17 μm versus 133.66 μm; 180.20 μm versus 156.43 μm; 17.01 μm versus 11.38 μm, , respectively). The morphological changes in retinal vessels were significantly correlated with DR stage. In conclusion, in diabetic patients with DR, both retinal arteries and veins are widened and exhibit increased vascular thickness.
      PubDate: Mon, 08 Aug 2022 22:50:03 +000
  • Effect of Ambient Lights on the Accuracy of a 3-Dimensional Optical
           Scanner for Face Scans: An In Vitro Study

    • Abstract: Most 3D scanners use optical technology that is impacted by lighting conditions, especially in triangulation with structured-light or laser techniques. However, the effect of ambient lights on the accuracy of the face scans remains unclear. The purpose of this study is to investigate the effect of ambient lights on the accuracy of the face scans obtained from the face scanner (EinScan Pro 2X Plus, Shining 3D Tech. Co., LTD., Hangzhou, China). A head model was designed in Rhinoceros 5 software (Rhino, Robert McNeel and Associates for Windows, Washington DC, USA) and printed with 200 micron resolution of polylactic acid and was dented with 2.0 mm of carbide bur to aid in superimposition in software. The head model was measured by a coordinate-measuring machine (CMM) to generate a reference stereolithography (STL) file as a control. The face model was scanned four times under nine light conditions: cool white (CW), warm white (WW), daylight (DL), natural light (NL), and illuminant (9w, 18w, 22w). Scan data were exported into an STL file. The scan STL files obtained were compared with the reference STL file by 3D inspection software (Geomagic Control X version 17, Geomagic, Morrisville, NC, USA). The deviations and root mean square errors (RMSEs) between the reference model (trueness) and within the group (precision) were selected for the statistical analysis. The statistical analysis was done using SPSS 20.0 (IBM Company, Chicago, USA). The trueness and precision were evaluated with the one-way ANOVA with multiple comparisons using the Tukey method. For trueness, the scanner showed the lowest RMSE under the NL group (77.18 ± 3.22) and the highest RMSE under the 18w-DL group (95.33 ± 6.89). There was a statistically significant difference between the NL group and the 18w-DL group (p 
      PubDate: Mon, 08 Aug 2022 22:50:02 +000
  • Using Healthcare Resources Wisely: A Predictive Support System Regarding
           the Severity of Patient Falls

    • Abstract: Background. An injurious fall is one of the main indicators of care quality in healthcare facilities. Despite several fall screen tools being widely used to evaluate a patient’s fall risk, they are frequently unable to reveal the severity level of patient falls. The purpose of this study is to build a practical system useful to predict the severity level of in-hospital falls. This practice is done in order to better allocate limited healthcare resources and to improve overall patient safety. Methods. Four hundred and forty-six patients who experienced fall events at a large Taiwanese hospital were referenced. Eight predictors were used to ascertain the severity of patient falls solely based on the above study population. Multinomial logistic regression, Naïve Bayes, random forest, support vector machine, eXtreme gradient boosting, deep learning, and ensemble learning were adopted to establish predictive models. Accuracy, F1 score, precision, and recall were utilized to assess the models’ performance. Results. Compared to other learners, random forest exhibited satisfying predictive performance in terms of all metrics (accuracy: 0.844, F1 score: 0.850, precision: 0.839, and recall: 0.875 for the test dataset), and it was adopted as the base learner for a severity-level predictive system which is web-based. Furthermore, age, ability of independent activity, patient sources, use of assistive devices, and fall history within the past 12 months were deemed the top five important risk factors for evaluating fall severity. Conclusions. The application of machine learning techniques for predicting the severity level of patient falls may result in some benefits to monitor fall severity and to better allocate limited healthcare resources.
      PubDate: Mon, 01 Aug 2022 11:05:00 +000
  • Day Surgery Scheduling and Optimization in Large Public Hospitals in
           China: A Three-Station Job Shop Scheduling Problem

    • Abstract: Day surgery scheduling allocates hospital resources to day surgical cases and decides on the time to perform the surgeries in the day surgery center (DSC). Based on the day surgery service process of large public hospitals in China, we found that the service efficiency of the process depends on the utilization of hospital resources efficiently and could be optimized through day surgery scheduling. We described it as a flexible flow shop owing to the three-station nature of surgery. Allocating all types of hospital resources to the three stations and determining the length of time for each stage during surgery are crucial to improving the efficiency of DSC. This paper integrates a three-station job shop scheduling problem (JSSP) into the day surgery scheduling and optimization problem. The JSSP was formulated as a mixed-integer linear programming model, and the elicitation of the model for scheduling surgeries with different priorities in the DSC is discussed. The model illustrated a case study of the DSC within West China Hospital (WCH). Numerical experiments based on the genetic algorithm design were conducted. Compared to the other optimization strategies, we proposed that the three-station job shop scheduling strategy (TSJS) could not only improve the efficiency and reduce the waiting time of the patients of the DSC in large public hospitals in China but also allow for timely scheduling adjustments during the advent of emergency surgeries.
      PubDate: Sat, 30 Jul 2022 10:35:00 +000
  • Technology Acceptance in Socially Assistive Robots: Scoping Review of
           Models, Measurement, and Influencing Factors

    • Abstract: Objectives. We summarized technology acceptance and the influencing factors of elderly people toward socially assistive robots (SARs). Methods. A scoping review whereby a literature search was conducted in Embase, Cochrane, Scopus, PubMed, and Web of Science databases (2006–2021) to retrieve studies. No restrictions on study methodology were imposed. Results. Out of the 1187 retrieved papers, 35 studies were finally included in the study. The articles covered various aspects, including general attitudes towards using SARs, technology acceptance theory models, and factors associated with technology acceptance. Twelve studies reported a positive attitude towards SARs. Three explicit theoretical frameworks were reported. Studies involving the elderly reported three themes that influence attitudes towards SARs: individual characteristics, concerns/problems regarding robots, and social factors. Conclusions. This review elucidates on the suitability of theory-based framework as applied to acceptance of SARs. We found that research on technology acceptance with regard to SARs is still in the developmental stages, and further studies of assessment tools for SARs are required. It is also essential to consider the factors that influence the acceptance of SARs by older people to ensure that they meet the end goal requirements of the user.
      PubDate: Fri, 22 Jul 2022 09:50:01 +000
  • Corrigendum to “A Dynamic Model for Imputing Missing Medical Data: A
           Multiobjective Particle Swarm Optimization Algorithm”

    • PubDate: Thu, 21 Jul 2022 06:50:00 +000
  • TiM-Net: Transformer in M-Net for Retinal Vessel Segmentation

    • Abstract: retinal image is a crucial window for the clinical observation of cardiovascular, cerebrovascular, or other correlated diseases. Retinal vessel segmentation is of great benefit to the clinical diagnosis. Recently, the convolutional neural network (CNN) has become a dominant method in the retinal vessel segmentation field, especially the U-shaped CNN models. However, the conventional encoder in CNN is vulnerable to noisy interference, and the long-rang relationship in fundus images has not been fully utilized. In this paper, we propose a novel model called Transformer in M-Net (TiM-Net) based on M-Net, diverse attention mechanisms, and weighted side output layers to efficaciously perform retinal vessel segmentation. First, to alleviate the effects of noise, a dual-attention mechanism based on channel and spatial is designed. Then the self-attention mechanism in Transformer is introduced into skip connection to re-encode features and model the long-range relationship explicitly. Finally, a weighted SideOut layer is proposed for better utilization of the features from each side layer. Extensive experiments are conducted on three public data sets to show the effectiveness and robustness of our TiM-Net compared with the state-of-the-art baselines. Both quantitative and qualitative results prove its clinical practicality. Moreover, variants of TiM-Net also achieve competitive performance, demonstrating its scalability and generalization ability. The code of our model is available at https://github.com/ZX-ECJTU/TiM-Net.
      PubDate: Mon, 11 Jul 2022 07:50:01 +000
  • A Context-Aware MRIPPER Algorithm for Heart Disease Prediction

    • Abstract: These days, mobile computing devices are ubiquitous and are widely used in almost every facet of daily life. In addition, computing and the modern technologies are not really coexisting anymore. With a wide range of conditions and areas of concern, the medical domain was also concerned. New types of technologies, such as context-aware systems and applications, are constantly being infused into the medicine field. An IoT-enabled healthcare system based on context awareness is developed in this work. In order to collect and store the patient data, smart medical devices are employed. Context-aware data from the database includes the patient’s medical records and personal information. The MRIPPER (Modified Repeated Incremental Pruning to Produce Error) technique is used to analyze and classify the data. A rule-based machine learning method is used in this algorithm. The rules for analyzing datasets in order to make predictions about heart disease are framed using this algorithm. MATLAB is used to simulate the proposed model’s performance analysis. Other models like random forest, J48, CART, JRip, and OneR algorithms are also compared to validate the proposed model’s performance. The proposed model obtains 98.89 percent accuracy, 96.76 percent precision, 99.05 percent sensitivity, 94.35 percent specificity, and 97.60 percent f-score. Predictions for subjects in the normal and abnormal classes were both accurate with 97.38 for normal and 97.93 for abnormal subjects.
      PubDate: Mon, 11 Jul 2022 07:50:00 +000
  • Sleep Staging Using Noncontact-Measured Vital Signs

    • Abstract: As a physiological phenomenon, sleep takes up approximately 30% of human life and significantly affects people’s quality of life. To assess the quality of night sleep, polysomnography (PSG) has been recognized as the gold standard for sleep staging. The drawbacks of such a clinical device, however, are obvious, since PSG limits the patient’s mobility during the night, which is inconvenient for in-home monitoring. In this paper, a noncontact vital signs monitoring system using the piezoelectric sensors is deployed. Using the so-designed noncontact sensing system, heartbeat interval (HI), respiratory interval (RI), and body movements (BM) are separated and recorded, from which a new dimension of vital signs, referred to as the coordination of heartbeat interval and respiratory interval (CHR), is obtained. By extracting both the independent features of HI, RI, and BM and the coordinated features of CHR in different timescales, Wake-REM-NREM sleep staging is performed, and a postprocessing of staging fusion algorithm is proposed to refine the accuracy of classification. A total of 17 all-night recordings of noncontact measurement simultaneous with PSG from 10 healthy subjects were examined, and the leave-one-out cross-validation was adopted to assess the performance of Wake-REM-NREM sleep staging. Taking the gold standard of PSG as reference, numerical results show that the proposed sleep staging achieves an averaged accuracy and Cohen’s Kappa index of 82.42% and 0.63, respectively, and performs robust to subjects suffering from sleep-disordered breathing.
      PubDate: Fri, 08 Jul 2022 12:20:00 +000
  • How to Maintain a Sustainable Doctor-Patient Relationship in Healthcare in
           China: A Structural Equation Modeling Approach

    • Abstract: It is important to identify means of improving and maintaining a sustainable doctor-patient relationship to address current healthcare issues. Although many studies have made outstanding contributions to the healthcare doctor-patient relationship literature, little work has been done to explore the influencing elements of the doctor-patient relationship in relation to expectation confirmation theory. To fill this gap, this study produced a theoretical framework model of the influencing factors of the doctor-patient relationship according to the expectation confirmation theory. Data from 335 Chinese patients were analyzed using a structural equation modeling method, and the results showed that patient satisfaction and patient trust are the most important factors in building a good relationship between doctor and patient. Furthermore, three components of postdiagnosis patient’s perception, namely, perceived service quality, perceived communication quality, and perceived service attitude, are examined. These have a significant impact on patient confirmation. These three components ultimately affect the doctor-patient relationship. This study will be helpful for doctors to understand patients’ service demands and their future diagnosis behavior. The proposals of this study may lead to optimization of the process of diagnosis and improvements in the quality of clinic services.
      PubDate: Tue, 05 Jul 2022 11:50:00 +000
  • Comprehensive Analysis of Prognostic Value and Immune Infiltration of
           IGFBP Family Members in Glioblastoma

    • Abstract: Glioblastoma (GBM) is the most common primary malignant brain tumor in adults. The insulin-like growth factor-binding protein (IGFBP) family is involved in tumorigenesis and the development of multiple cancers. However, little is known about the prognostic value and regulatory mechanisms of IGFBPs in GBM. Oncomine, Gene Expression Profiling Interactive Analysis, PrognoScan, cBioPortal, LinkedOmics, TIMER, and TISIDB were used to analyze the differential expression, prognostic value, genetic alteration, biological function, and immune cell infiltration of IGFBPs in GBM. We observed that IGFBP1, IGFBP2, IGFBP3, IGFBP4, and IGFBP5 mRNA expression was significantly upregulated in patients with GBM, whereas IGFBP6 was downregulated; this difference in mRNA expression was statistically insignificant. Subsequent investigations showed that IGFBP4 and IGFBP6 mRNA levels were significantly associated with overall survival in patients with GBM. Functional Gene Ontology Annotation and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis revealed that genes coexpressed with IGFBP4 and IGFBP6 were mainly enriched in immune-related pathways. These results were validated using the TIMER and TSMIDB databases. This study demonstrated that the IGFBP family has prognostic value in patients with GBM. IGFBP4 and IGFBP6 are two members of the IGFBP family that had the highest prognostic value; thus, they have the potential to serve as survival predictors and immunotherapeutic targets in GBM.
      PubDate: Mon, 04 Jul 2022 07:05:00 +000
  • A Study on Early Death Prognosis Model in Adult Patients with Secondary
           Hemophagocytic Lymphohistiocytosis

    • Abstract: Background. The mortality risks for secondary hemophagocytic lymphohistiocytosis in the induction stage and investigated prognostic factors need to be further discussed. Objective. The aim of this study is to establish a clinical model for predicting early death in adult patients with secondary hemophagocytic lymphohistiocytosis. Design, Participants, and Main Measures. The baseline characteristics, laboratory examination results, and 8-week survival rate of 139 adult sHLH patients diagnosed from January 2018 to December 2018 were analyzed retrospectively, and a prognostic model was constructed with low-risk (score 0–2), medium-risk (score 3), and high-risk (score ≥ 4) as parameters. Key Results. Univariate analysis confirmed that early death was not related to the type of HLH but significantly related to the patient’s response to first-line treatment. The peripheral blood cell count was significantly decreased, C-reactive protein was higher, glutamyl transpeptidase and total bilirubin were higher, albumin was significantly lower, urea nitrogen was higher, hypocalcemia and hyponatremia, deep organ hemorrhage and D-dimer increased, cardiac function damage and HLH central involvement, sCD25 increased, and EB virus infection were predictive factors of early death. In the multivariate model, patients’ response to first-line treatment was a good predictor of overall survival, and hypocalcemia and deep organ bleeding were associated with poor survival. The risk factors were scored and graded according to the risk ratio. The 8-week overall survival rates of the low-risk group (82 cases), medium-risk group (36 cases), and high-risk group (21 cases) were 85.4%, 52.8%, and 23.8%, respectively ( 
      PubDate: Fri, 01 Jul 2022 05:20:00 +000
  • Establishment and Validation for Predicting the Lymph Node Metastasis in
           Early Gastric Adenocarcinoma

    • Abstract: Lymph node metastasis (LNM) is considered to be one of the important factors in determining the optimal treatment for early gastric cancer (EGC). This study aimed to develop and validate a nomogram to predict LNM in patients with EGC. A total of 842 cases from the Surveillance, Epidemiology, and End Results (SEER) database were divided into training and testing sets with a ratio of 6 : 4 for model development. Clinical data (494 patients) from the hospital were used for external validation. Univariate and multivariate logistic regression analyses were used to identify the predictors using the training set. Logistic regression, LASSO regression, ridge regression, and elastic-net regression methods were used to construct the model. The performance of the model was quantified by calculating the area under the receiver operating characteristic curve (AUC) with 95% confidence intervals (CIs). Results showed that T stage, tumor size, and tumor grade were independent predictors of LNM in EGC patients. The AUC of the logistic regression model was 0.766 (95% CI, 0.709–0.823), which was slightly higher than that of the other models. However, the AUC of the logistic regression model in external validation was 0.625 (95% CI, 0.537–0.678). A nomogram was drawn to predict LNM in EGC patients based on the logistic regression model. Further validation based on gender, age, and grade indicated that the logistic regression predictive model had good adaptability to the population with grade III tumors, with an AUC of 0.803 (95% CI, 0.606–0.999). Our nomogram showed a good predictive ability and may provide a tool for clinicians to predict LNM in EGC patients.
      PubDate: Wed, 29 Jun 2022 05:50:00 +000
  • Using the Intelligent System to Improve the Delivered Adequacy of Dialysis
           by Preventing Intradialytic Complications

    • Abstract: Acute kidney failure patients while detoxificated by hemodialysis (HD) mostly or continuously faced regular problems such as low blood pressure (hypotension), muscle cramps, nausea, or vomiting. Higher intradialytic symptom leads to low-quality HD treatment. Although more known therapeutic interventions are used to relieve the HD side effects, this study was designed to investigate how intelligent systems can make highly beneficial alterations in dialysis facilities and equipment to ease intradialytic complications and help the staff deliver high-quality treatment. A search was performed among relevant research articles based on nonpharmacological intervention methods considered to prevent adverse effects of renal replacement therapy until 2020 in the PubMed databases using the terms “intradialytic complications,” “intradialytic complication interventions,” “nonpharmacological interventions,” “intradialytic exercises,” and “adequacy calculation methods.” Studies included the prevalence of intradialytic complications, different strategies with the aim of preventing complications, the outcome of intradialytic exercises on dialysis symptoms, and dialysis dose calculation methods. The results showed the incidence of hypotension varying between 5% and 30%, fatigue, muscular cramps, and vomiting as the most common complications during dialysis, which greatly affect the outcome of HD sessions. To prevent hypotension, ultrafiltration profiling, sodium modeling, low dialysate temperature, and changing the position to Trendelenburg are some strategies. Urea reduction ratio (URR), formal urea kinetic modeling (FUKM), formal single-pool urea kinetics, and online clearance monitoring (OCM) are methods for calculating the delivered dose of dialysis in which OCM is a low-cost and accessible way to monitor regularly the quality of dialysis delivered. Integration of the chair and HD machine which is in direct contact with the patient provides an intelligent system that improves the management of the dialysis session to enhance the quality of healthcare service.
      PubDate: Thu, 23 Jun 2022 09:35:01 +000
  • An Efficient Rotation Forest-Based Ensemble Approach for Predicting
           Severity of Parkinson’s Disease

    • Abstract: Parkinson’s disease (PD) is a neurodegenerative nervous system disorder that mainly affects body movement, and it is one of the most common diseases, particularly in elderly individuals. This paper proposes a new machine learning approach to predict Parkinson’s disease severity using UCI’s Parkinson’s telemonitoring voice dataset. The proposed method analyses the patient's voice data and classifies them into “severe” and “nonsevere” classes. At first, a subset of features was selected, then a novel approach with a combination of Rotation Forest and Random Forest was applied on selected features to determine each patient’s disease severity. Analysis of the experimental results shows that the proposed approach can detect the severity of PD patients in the early stages. Moreover, the proposed model is compared with several algorithms, and the results indicate that the model is highly successful in classifying records and outperformed the other methods concerning classification accuracy and F1-measure rate.
      PubDate: Thu, 23 Jun 2022 09:35:01 +000
  • Machine Learning Models to Predict In-Hospital Mortality among Inpatients
           with COVID-19: Underestimation and Overestimation Bias Analysis in
           Subgroup Populations

    • Abstract: Prediction of the death among COVID-19 patients can help healthcare providers manage the patients better. We aimed to develop machine learning models to predict in-hospital death among these patients. We developed different models using different feature sets and datasets developed using the data balancing method. We used demographic and clinical data from a multicenter COVID-19 registry. We extracted 10,657 records for confirmed patients with PCR or CT scans, who were hospitalized at least for 24 hours at the end of March 2021. The death rate was 16.06%. Generally, models with 60 and 40 features performed better. Among the 240 models, the C5 models with 60 and 40 features performed well. The C5 model with 60 features outperformed the rest based on all evaluation metrics; however, in external validation, C5 with 32 features performed better. This model had high accuracy (91.18%), F-score (0.916), Area under the Curve (0.96), sensitivity (94.2%), and specificity (88%). The model suggested in this study uses simple and available data and can be applied to predict death among COVID-19 patients. Furthermore, we concluded that machine learning models may perform differently in different subpopulations in terms of gender and age groups.
      PubDate: Thu, 23 Jun 2022 09:35:00 +000
  • Prediction of Dental Implants Using Machine Learning Algorithms

    • Abstract: It has been claimed that artificial intelligence (AI) has transformative potential for the healthcare sector by enabling increased productivity and creative methods of delivering healthcare services. Recently, there has been a major shift to artificial intelligence by businesses, government, and private sectors in general and the health sector in particular. Many studies have proven that artificial intelligence is contributing greatly to the health sector by discovering diseases and determining the best treatments for patients. Dentistry requires new innovative methods that serve both the patient and the service provider in obtaining the best and appropriate medical services. Artificial intelligence has the ability to develop the field of dentistry through early diagnosis and prediction of dental implant cases. This research develops a set of four machine learning algorithms to predict when a patient might need dental implants. These models are the Bayesian network, random forest, AdaBoost algorithm, and improved AdaBoost algorithm. This work shows that the developed algorithms can predict when a patient needs dental implants. Also, we believe that this proposal will advise managers and decision-makers in targeting patients with particular diagnoses. Analysis of the obtained results indicates good performance of the developed machine learning. As a result of this research, we note that the proposed improved AdaBoost algorithm increases the level of prediction accuracy and gives significantly higher performance than the other studied methods with the accuracy for the improved AdaBoost algorithm reaching 91.7%.
      PubDate: Mon, 20 Jun 2022 13:20:00 +000
  • Effects of Laparoscopic versus Open Surgery for Advanced Gastric Cancer
           after Neoadjuvant Chemotherapy: A Meta-Analysis

    • Abstract: Objective. To evaluate the efficacy of laparoscopy and laparotomy after neoadjuvant chemotherapy in the treatment of advanced gastric cancer by meta-analysis. Methods. Cochrane Library, Embase, and PubMed were searched by computer until December 1, 2021. Literature was screened according to inclusion and exclusion criteria, and relevant data were extracted for meta-analysis using RevMan 5.3. Results. A total of 1027 patients from 11 literature studies were included in this study, including 413 patients in the laparoscopic group and 614 patients in the open group. Meta-analysis showed that the laparoscopic group had less intraoperative bleeding (SMD = −1.11; 95% CI: −1.75–0.47; ), early postoperative exhaust (SMD = −0.45; 95% CI: −0.70–0.20; ), and shorter postoperative hospital stay (SMD = 0.97; 95% CI: 1.69∼0.26; ), but had longer the operation time (SMD = 0.65; 95% CI: 0.52∼0.79; ). There was no significant difference in the number of lymph nodes dissected during operation (SMD = −0.45; 95% CI: −0.42–0.19; ), the incidence of surgical complications 30 days after operation (OR = 0.78; 95% CI: 0.53∼1.13; ), time of first defecation (MD = 0.00; 95% CI: −0.10∼0.10; ), and time of first postoperative feeding (MD = −0.05; 95% CI: −0.22∼0.12; ) between the two groups. For long-term prognosis, there was no significant difference in the 3-year overall survival rate after operation between the two groups (RR = 0.84; 95% CI: 0.63–1.12; ).Conclusion. Compared with the open stomach cancer surgery, laparoscopic gastric cancer surgery has less intraoperative blood loss, shorter hospitalization time, and advantages such as early rehabilitation, postoperative complications rate, and long-term survival, which confirmed the validity and security of the laparoscopic surgery.
      PubDate: Sat, 18 Jun 2022 06:05:01 +000
  • Design and Performance Analysis of a Dynamic Magnetic Resonance
           Imaging-Compatible Device for Triangular Fibrocartilage Complex Injury

    • Abstract: Pain and injury of the triangular fibrocartilage complex (TFCC) due to overuse or trauma are commonly diagnosed through static MRI scanning, while TFCC is always involved in radial and ulnar deviation of the wrist. To the best of our knowledge, a dynamic MRI diagnostic method and auxiliary tool have not been applied or fully developed in the literature. As such, this study presents the design and evaluation of a dynamic magnetic resonance imaging (MRI) auxiliary tool for TFCC injury diagnosis. First, 3D scanning and Python are used to measure and fit the radial and ulnar deviation trajectories of healthy participants and patients. 3D printing is then used to manufacture the auxiliary tool for dynamic MRI, and dynamic MRI diagnosis is then conducted to explore the clinical effect. The radial and ulnar deviation trajectory is presented as an asymmetric curve without an obvious circular centre, and the results indicate that the designed auxiliary device meets the requirements of the ulnar and radial movements of the human wrist. According to the MRI contrast test results, the image quality score of patients wearing the auxiliary device is higher than for those without. Such devices could assist clinicians in the diagnosis of TFCC damage, and our method could not only serve as the reference standard for clinical noninvasive diagnosis but also help in understanding the disease and improving the accuracy of TFCC diagnosis.
      PubDate: Thu, 16 Jun 2022 10:20:00 +000
  • Comparison of Conventional Modeling Techniques with the Neural Network
           Autoregressive Model (NNAR): Application to COVID-19 Data

    • Abstract: The coronavirus disease 2019 (COVID-19) pandemic continues to destroy human life around the world. Almost every country throughout the globe suffered from this pandemic, forcing various governments to apply different restrictions to reduce its impact. In this study, we compare different time-series models with the neural network autoregressive model (NNAR). The study used COVID-19 data in Pakistan from February 26, 2020, to February 18, 2022, as a training and testing data set for modeling. Different models were applied and estimated on the training data set, and these models were assessed on the testing data set. Based on the mean absolute scaled error (MAE) and root mean square error (RMSE) for the training and testing data sets, the NNAR model outperformed the autoregressive integrated moving average (ARIMA) model and other competing models indicating that the NNAR model is the most appropriate for forecasting. Forecasts from the NNAR model showed that the cumulative confirmed COVID-19 cases will be 1,597,180 and cumulative confirmed COVID-19 deaths will be 32,628 on April 18, 2022. We encourage the Pakistan Government to boost its immunization policy.
      PubDate: Tue, 14 Jun 2022 11:05:00 +000
  • VEMERS 2.0: Upgrading of an Emergency Use Ventilator from a Single
           Mandatory Volume Control Mode of Ventilation (VEMERS 1.0) to 8 Modes of

    • Abstract: The upgrading of an emergency use ventilator from a single mandatory volume control mode of ventilation (VEMERS 1.0) to 8 modes of ventilation (VEMERS 2.0) is described. The original VEMERS 1.0 was developed in the midst of the COVID-19 crisis in Chile (April to August 2020) following special but nonetheless strict guidelines specified by local medical associations and national health and scientific ministries. The upgrade to 8 modes of ventilation in VEMERS 2.0 was made possible with minor but transcendental changes to the original architecture. The main contribution of this research is that starting from a functional block diagram of an ICU mechanical ventilator and carrying a systematic analysis, the main function blocks are implemented in such a way that combinations of standard off-the-shelf pneumatic and electronic components can be used. This approach has both economical and technical advantages. No special parts need to be fabricated at all, and because of a wider variety of options, the use of extensively field-proven off-the-shelf commercial components assures better availability and lower costs when compared to that of conventional ICU mechanical ventilators, without sacrificing reliability. Given the promising results obtained with VEMERS 2.0 in the subsequent national certification process, the production of 40 VEMERS 2.0 units was sponsored by the Ministry of Science and the Ministry of Economy. Twenty units have been distributed among hospitals along the country. The purpose of VEMERS 2.0, as a low-cost but very reliable option, is to increase the number of mechanical ventilators available (3,000 for a population of 18,000,000) in the country to eventually reach a ratio similar to that of more developed countries. VEMERS is an open-source project for others to use the knowledge gained.
      PubDate: Mon, 06 Jun 2022 08:50:01 +000
  • Characteristics of Plantar Pressure Distribution in Diabetes with or
           without Diabetic Peripheral Neuropathy and Peripheral Arterial Disease

    • Abstract: Background. Excessive plantar pressure leads to increased risk of diabetic foot ulcers. Diabetic peripheral neuropathy (DPN) and peripheral arterial disease (PAD) have been considered to be associated with alterations in gait and plantar pressure in diabetic patients. However, few studies have differentiated the effects with each of them. Objective. To investigate the plantar pressure distribution in diabetic patients, with DPN and PAD as independent or combined factors. Methods. 112 subjects were recruited: 24 diabetic patients with both DPN and PAD (DPN-PAD group), 12 diabetic patients with DPN without PAD (DPN group), 10 diabetic patients with PAD without DPN (PAD group), 23 diabetic patients without DPN or PAD, and 43 nondiabetic healthy controls (HC group). The in-shoe plantar pressure during natural walking was measured. Differences in peak pressure, contact area, proportion of high pressure area (%HP), and anterior/posterior position of centre of pressure (COP) were analysed. Results. Compared with HC group, in DPN-PAD group and DPN group, the peak pressures in all three forefoot regions increased significantly; in PAD group, the peak pressure in lateral forefoot increased significantly. The contact area of midfoot in the DPN-PAD group decreased significantly. PAD group had larger HP% of lateral forefoot, DPN group had larger HP% of inner forefoot, and DPN-PAD group had larger HP% of total plantar area. There was a significant tendency of the anterior displacement of COP in the DPN-PAD group and DPN group. No significant differences were observed between the D group and HC group. Conclusion. DPN or PAD could affect the plantar pressure distribution in diabetic patients independently or synergistically, resulting in increased forefoot pressure and the area at risk of ulcers. DPN has a more pronounced effect on peak pressure than PAD. The synergistic effect of them could significantly reduce the plantar contact area of midfoot.
      PubDate: Mon, 06 Jun 2022 08:50:00 +000
  • A Flexible Bayesian Parametric Proportional Hazard Model: Simulation and
           Applications to Right-Censored Healthcare Data

    • Abstract: Survival analysis is a collection of statistical techniques which examine the time it takes for an event to occur, and it is one of the most important fields in biomedical sciences and other variety of scientific disciplines. Furthermore, the computational rapid advancements in recent decades have advocated the application of Bayesian techniques in this field, giving a powerful and flexible alternative to the classical inference. The aim of this study is to consider the Bayesian inference for the generalized log-logistic proportional hazard model with applications to right-censored healthcare data sets. We assume an independent gamma prior for the baseline hazard parameters and a normal prior is placed on the regression coefficients. We then obtain the exact form of the joint posterior distribution of the regression coefficients and distributional parameters. The Bayesian estimates of the parameters of the proposed model are obtained using the Markov chain Monte Carlo (McMC) simulation technique. All computations are performed in Bayesian analysis using Gibbs sampling (BUGS) syntax that can be run with Just Another Gibbs Sampling (JAGS) from the R software. A detailed simulation study was used to assess the performance of the proposed parametric proportional hazard model. Two real-survival data problems in the healthcare are analyzed for illustration of the proposed model and for model comparison. Furthermore, the convergence diagnostic tests are presented and analyzed. Finally, our research found that the proposed parametric proportional hazard model performs well and could be beneficial in analyzing various types of survival data.
      PubDate: Thu, 02 Jun 2022 05:05:00 +000
  • An ROI Extraction Method of Finger Vein Images Based on Large Receptive
           Field Gradient Operator for Accurate Localization of Joint Cavity

    • Abstract: Region of interest (ROI) extraction is a key step in finger vein recognition preprocessing. The current method takes the finger region in the vein image as the ROI, but this method cannot obtain better recognition accuracy because it only removes the background noise of the image and ignores factors such as the position and shape of the finger. To solve this problem, we limited the ROI to a fixed region between two finger joint cavities, proposed a new large receptive field gradient operator, and designed and implemented a new method for ROI extraction. It uses a large receptive field to search the target, which is similar to human vision, thus solving the problem of difficult ROI localization for images with large gradient areas. Moreover, for external factors such as noise and uneven illumination in the vein image, the interference factors can be eliminated by averaging them to a larger range with a larger size operator to improve the accuracy of the subsequent matching recognition. To verify the effectiveness of the proposed method, we conducted sufficient matching experiments on three public finger vein datasets. On various datasets, our method significantly reduced the identified EER value, with the lowest EER value reaching 0.96%. The experimental results show that the proposed ROI extraction method can effectively eliminate the influence of finger position, finger shape, and other factors on the subsequent recognition performance, accurately locate the finger joint cavity, and effectively improve the recognition performance.
      PubDate: Mon, 30 May 2022 08:35:01 +000
  • Effects of Knee Flexion Angles on the Joint Force and Muscle Force during
           Bridging Exercise: A Musculoskeletal Model Simulation

    • Abstract: Bridging exercise is commonly used to increase the strength of the hip extensor and trunk muscles in physical therapy practice. However, the effect of lower limb positioning on the joint and muscle forces during the bridging exercise has not been analyzed. The purpose of this study was to use a musculoskeletal model simulation to examine joint and muscle forces during bridging at three different knee joint angle positions. Fifteen healthy young males (average age: 23.5 ± 2.2 years) participated in this study. Muscle and joint forces of the lumbar spine and hip joint during the bridging exercise were estimated at knee flexion angles of 60°, 90°, and 120° utilizing motion capture data. The lumbar joint force and erector spinae muscle force decreased significantly as the angle of the knee joint increased. The resultant joint forces were 200.0 ± 23.2% of body weight (%BW), 174.6 ± 18.6% BW, and 150.5 ± 15.8% BW at 60°, 90°, and 120° knee flexion angles, respectively. On the other hand, the hip joint force, muscle force of the gluteus maxims, and adductor magnus tended to increase as the angle of the knee joint increased. The resultant joint forces were 274.4 ± 63.7% BW, 303.9 ± 85.8% BW, and 341.1 ± 85.7% BW at a knee flexion angle of 60°, 90°, and 120°, respectively. The muscle force of the biceps femoris decreased significantly with increased knee flexion during the bridging exercise. In conclusion, the knee flexion position during bridging exercise has different effects on the joint and muscle forces around the hip joint and lumbar spine. These findings would help clinicians prescribe an effective bridging exercise that includes optimal lower limb positioning for patients who require training of back and hip extensor muscles.
      PubDate: Sun, 29 May 2022 12:05:00 +000
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