Technology and Health Care最新文献

{"title":"Forecasting of hospitalizations for COVID-19: A hybrid intelligence approach for Disease X research.","authors":"He Mu, Hongbing Zhu","doi":"10.1177/09287329241291772","DOIUrl":"10.1177/09287329241291772","url":null,"abstract":"<p><p>BackgroundThe COVID-19 pandemic underscores the necessity for proactive measures against emerging diseases, epitomized by WHO's <i>\"</i>Disease X.\" Among the myriad of indicators tracking COVID-19 progression, the count of hospitalized patients assumes a pivotal role. This metric facilitates timely responses from government agencies, enabling proactive allocation and management of medical resources.ObjectiveIn this study, we introduce a novel hybrid intelligent approach, the EMD&LSTM-ARIMA model.<b>Method:</b> This model integrates three techniques: Empirical Mode Decomposition (EMD) to decompose the data into intrinsic mode functions, Long Short-Term Memory (LSTM) neural network for capturing long-term dependencies and nonlinear relationships, and the Auto-Regressive Integrated Moving Average (ARIMA) model for handling linear trends and time series forecasting. We verify its high predictive power and utility through training and forecasting COVID-19 hospitalizations in the UK, Canada, Italy, and Japan.ResultsOur analysis reveals that all forecasted error rates remain below 10%, with Mean Absolute Percentage Error (MAPE) values obtained for these four countries as 2.30%, 3.33%, 1.63%, and 2.89%, respectively.ConclusionOur proposed EMD&LSTM-ARIMA model demonstrates robust forecasting performance, particularly for COVID-19 hospitalization data.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"768-780"},"PeriodicalIF":1.4,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143460232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Risk factors for recurrence in patients with uterine fibroids treated with high-intensity focused ultrasound.","authors":"Xiaoyan Bian, Xiaofeng Gu","doi":"10.1177/09287329241291362","DOIUrl":"10.1177/09287329241291362","url":null,"abstract":"<p><p>BackgroundUterine fibroids, benign smooth muscle tumors prevalent in the female genital tract, affecting up to 40% of women of childbearing age. High-intensity focused ultrasound (HIFU) has emerged as a promising non-invasive approach for treating uterine fibroids, but some patients may still experience recurrence of uterine fibroids after treatment.ObjectiveThis study aims to explore the risk factors associated with uterine fibroid recurrence following HIFU treatment, and to provide a basis for formulating response measures to prevent uterine fibroid recurrence after surgery in clinical practice.MethodsIn this regression observational study, 120 patients with uterine fibroids who underwent HIFU therapy at our institution from Jan 2018 to Dec 2021 were included as the study subjects. Collect clinical data of all included patients, and follow up for a total of 2 years every 6 menstrual periods with gynecological ultrasound or related examinations after surgery. Univariate and logistic regression analyses were performed to identify risk factors for recurrence in potential uterine fibroid patients receiving HIFU knife treatment.ResultsPatients were divided into a relapse group (n = 27) and a non-relapse group (n = 93) based on recurrence during the follow-up period. The outcome of univariate analysis indicated no statistically significant difference in age, BMI, age at menarche, history of preoperative pregnancy, history of postoperative pregnancy, family history of uterine fibroids, Bcl-2, FSH, LH, E2, PRL, P, and T between the two groups (<i>p </i>> 0.05). Notably, significant differences were observed in fibroid diameter, ER, and PR (<i>p </i>< 0.05). Logistic regression analysis revealed uterine fibroid diameter (OR = 28.032, 6.074 ∼ 129.372), PR (OR = 4.642, 2.382 ∼ 9.049), and ER (OR = 3.435, 1.820 ∼ 6.483) were independent risk factors for uterine fibroid recurrence after HIFU knife treatment. ROC curve analysis showed that the AUC of uterine fibroid recurrence predicted by fibroid diameter, ER, and PR after HIFU knife treatment were 0.791, 0.738, and 0.785, respectively.ConclusionThe diameter, ER, and PR of uterine fibroids are closely related to the recurrence of uterine fibroids after surgical treatment, and it is worth implementing key perioperative management in clinical practice to prevent and manage the recurrence of uterine fibroids.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"945-950"},"PeriodicalIF":1.4,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143460258","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research on mood monitoring and intervention for anxiety disorder patients based on deep learning wearable devices.","authors":"Xiao Gu, Xuedan Hu","doi":"10.1177/09287329241291376","DOIUrl":"https://doi.org/10.1177/09287329241291376","url":null,"abstract":"<p><p>BackgroundAnxiety disorders are common mental health issues that have a significant effect on people's quality of life. Conventional techniques for tracking emotional states frequently lack the accuracy and sensitivity needed for successful intervention.ObjectivesThis project aims to create a sophisticated monitoring system that uses deep learning methods to evaluate physiological data from wearables, emphasizing heart rate variability (HRV), to forecast patients' emotional states who suffer from anxiety disorders.MethodsWearable equipment monitors physiological characteristics, which we used to obtain patient HRV data. We processed the data using a Bidirectional Long-Short-Term Memory (Bi-LSTM) network to evaluate time-dependent variables and enhance the precision of emotional state predictions. The physiological signals were used to teach the model to recognize different emotional states, such as neutral, happy, and sad.ResultsOutperforming conventional machine learning models, the Bi-LSTM model showed a high accuracy rate of up to 97% in predicting emotional states. The findings suggest that ongoing HRV monitoring can accurately track shifts in emotional states and enable prompt responses.ConclusionThis work emphasizes the possibility of real-time emotional state monitoring in patients with anxiety disorders with wearable technology and deep learning. The results point to the potential benefits of this strategy for improving emotional regulation and improving anxiety sufferers' quality of life, opening new avenues for investigation and advancement in the field of mental health therapies.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":"33 2","pages":"1128-1139"},"PeriodicalIF":1.4,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143659040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Parkinson's disease prediction using improved crayfish optimization based hybrid deep learning.","authors":"A Malathi, R Ramalakshmi, Vaibhav Gandhi, A Bhuvanesh","doi":"10.1177/09287329241296352","DOIUrl":"https://doi.org/10.1177/09287329241296352","url":null,"abstract":"<p><p>BackgroundPredicting the course of Parkinson's disease is essential for prompt diagnosis and treatment, which may enhance patient outcomes.ObjectiveThis study presents a novel method for Parkinson's disease prediction using freely accessible resources. The suggested approach starts with band-pass filter data preprocessing and uses Empirical Mode Decomposition (EMD) for feature extraction. Then, for classification, these features are supplied into an Attention-based Efficient Bidirectional Network (ImCfO_Attn_EffBNet) based on Improved Crayfish Optimization. EfficientNet-B7, BiLSTM, and Attention modules are integrated by ImCfO_Attn_EffBNet to effectively gather temporal and geographic data.MethodsAdditionally, we use the Improved Crayfish Optimization (ImCfO) algorithm to maximize convergence rates, optimize the loss function, and find the global best solutions.ResultsImCfO enhances performance by adding a self-adaptation criterion to the traditional crayfish algorithm. The classifier's configurable parameters are adjusted using the ImCfO resultant solution, which raises the prediction accuracy overall.ConclusionBased on a number of assessments, the ImCfO_Attn_EffBNet analyzed the performance and found that the results were as follows: accuracy (95.068%), recall (92.948%), specificity (92.89%), F-Score (92.89%), precision (92.89%), and FPR (2.1%), in that order.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":"33 2","pages":"1021-1037"},"PeriodicalIF":1.4,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143659119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research progress and visual analysis of smart wearable devices for the elderly in the context of ageing 4.0.","authors":"Xianru Shang, Zijian Liu, Zhigang Hu, Tianxing Yang, Chen Gong","doi":"10.1177/09287329241291375","DOIUrl":"https://doi.org/10.1177/09287329241291375","url":null,"abstract":"<p><p>BackgroundThe world's population is ageing quickly, making managing older people's health more difficult. Although smart wearable devices (SWDs) have gained attention as possible instruments for enhancing health outcomes in this population, it is still uncertain how useful and long-lasting they will be.<b>Objectives:</b> The purpose of this study is to methodically evaluate the advancement of academic research and development concerning SWDs for the aged, with an emphasis on the effects these diseases have on quality of life, illness prevention, and health management.<b>Method:</b> 649 publications published in the Web of Science Core Collection between 2013 and 2023 underwent bibliometric analysis. VOSviewer and CiteSpace software were used to assess topic changes, publishing trends, and citation patterns. Co-citation analysis was used to pinpoint important literature and research focus clusters.ResultsThe results show a notable rise in publications about SWDs for the aged, emphasising essential topics, including fall detection, technology adoption, and healthcare applications. According to the report, multidisciplinary research integration is expanding, and publications like IEEE SENSORS JOURNAL and SENSORS-BASEL are making significant contributions.ConclusionThe research emphasises the crucial need to conduct long-term monitoring studies to verify the health advantages of SWDs for senior citizens. It highlights the necessity for user-centred design and urges future research to concentrate on improving wearable technology accuracy to maximise its usefulness in managing geriatric health.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":"33 2","pages":"895-914"},"PeriodicalIF":1.4,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143659278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessment of perceived realism in AI-generated synthetic spine fracture CT images.","authors":"Sindhura D N, Radhika M Pai, Shyamasunder N Bhat, Manohara Pai M M","doi":"10.1177/09287329241291368","DOIUrl":"https://doi.org/10.1177/09287329241291368","url":null,"abstract":"<p><p>BackgroundDeep learning-based decision support systems require synthetic images generated by adversarial networks, which require clinical evaluation to ensure their quality.ObjectiveThe study evaluates perceived realism of high-dimension synthetic spine fracture CT images generated Progressive Growing Generative Adversarial Networks (PGGANs).<b>Method:</b> The study used 2820 spine fracture CT images from 456 patients to train an PGGAN model. The model synthesized images up to 512 × 512 pixels, and the realism of the generated images was assessed using Visual Turing Tests and Fracture Identification Test. Three spine surgeons evaluated the images, and clinical evaluation results were statistically analysed.<b>Result:</b> Spine surgeons have an average prediction accuracy of nearly 50% during clinical evaluations, indicating difficulty in distinguishing between real and generated images. The accuracy varies for different dimensions, with synthetic images being more realistic, especially in 512 × 512-dimension images. During FIT, among 16 generated images of each fracture type, 13-15 images were correctly identified, indicating images are more realistic and clearly depict fracture lines in 512 × 512 dimensions.ConclusionThe study reveals that AI-based PGGAN can generate realistic synthetic spine fracture CT images up to 512 × 512 pixels, making them difficult to distinguish from real images, and improving the automatic spine fracture type detection system.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":"33 2","pages":"931-944"},"PeriodicalIF":1.4,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143659467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The study of transitional care on the psychological state of patients with advanced lung cancer chemotherapy.","authors":"A-Mao Tang, Miao Wang, Li Ning, Lijun Lin, Yi-Min Li","doi":"10.1177/09287329251314258","DOIUrl":"https://doi.org/10.1177/09287329251314258","url":null,"abstract":"<p><strong>Background: </strong>Lung cancer is one of the malignant tumors with the highest morbidity and mortality worldwide. Patients with an advanced stage need to face negative effects from chemotherapy, dread of dying, weakened role function and physical and mental suffering.</p><p><strong>Objectives: </strong>To examine the effect of transitional care on the psychological state of patients with advanced lung cancer chemotherapy.</p><p><strong>Methods: </strong>Seventy-two patients with advanced lung cancer who underwent chemotherapy in our hospital were arbitrarily split into the experimental group (30 cases) and the control group (31 cases). The control group received routine discharge care, whereas the experimental group received transitional care. The scores were compared before the first chemotherapy, the day after the end of the first-cycle chemotherapy, and the third week after the end of the 4-week chemotherapy according to SCL-90, PSS, PFE-R, SES, QLQ-C30, the rate of unplanned re-diagnosis and nursing satisfactory.</p><p><strong>Results: </strong>There was no significant difference in all aspects of scores between the two groups before and after chemotherapy (P > 0.05). Whereas there were significant differences in emotional function, fatigue, insomnia, depression and interpersonal sensitivity between the two groups after 4 cycles of chemotherapy (P < 0.05). The scores of PSS and PFE-R decreased significantly in the two groups, and the SES and QLQ-C30 in the experimental group were significantly higher than those in the control group (all P < 0.05).</p><p><strong>Findings: </strong>Applying transitional care intervention can lessen patients' negative emotions since being discharged with advanced lung cancer following chemotherapy and diminish the rate of unplanned re-diagnosis.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"9287329251314258"},"PeriodicalIF":1.4,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143505233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Relationship of influenza virus to inflammatory factors and immune function in elderly patients with COPD: A retrospective analysis.","authors":"Yanrong Chen, Yun Shi, Liyang Dou, Yan Liu, Jing Zhang","doi":"10.1177/09287329251317307","DOIUrl":"https://doi.org/10.1177/09287329251317307","url":null,"abstract":"<p><strong>Objective: </strong>The aim of this retrospective study was to investigate the relevance of influenza A virus (IAV) in acutely exacerbating airway inflammatory response and disrupting immune function in elderly COPD patients.</p><p><strong>Methods: </strong>The group conducted a pre-test: using multiplex combined real-time PCR detection kits Multiple real⁃time PCR was used to detect twenty-four pathogens, 385 patients clinically diagnosed with COPD were tested for viral nucleic acid in throat swabs. At the same time, peripheral blood leukapheresis was collected from both groups of patients, and their IL-6, IL-8, IL-1β, and TNF-α levels were detected, along with the levels of T-cell differentiation markers CD4 and CD8, to assess the influence of influenza virus on the immune function of elderly COPD patients and its relevance to the acute exacerbation of airway inflammatory response in elderly COPD patients.</p><p><strong>Results: </strong>Results showed that the expression of inflammatory cytokines IL-6, IL-8, IL-1β and TNF-α was significantly higher in the viral group compared with the non-infected group (P < 0.05, P < 0.01). The levels of T cell differentiation type markers CD4 and CD8 were significantly lower in the infected group compared with the uninfected group.</p><p><strong>Conclusion: </strong>Influenza virus further exacerbated airway inflammatory response and decreased the immune function of T cells by activating intrinsic immune molecules such as IL-6, IL-8, IL-1β and TNF-α.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"9287329251317307"},"PeriodicalIF":1.4,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143505260","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Technological integration in timing of endoscopy: Predictive modeling for acute upper gastrointestinal bleeding outcomes.","authors":"Yangde Miao, Ajun Gu, Guang Yu, Binbin Tang","doi":"10.1177/09287329251316050","DOIUrl":"https://doi.org/10.1177/09287329251316050","url":null,"abstract":"<p><strong>Background: </strong>Technological advancements have revolutionized the management of acute upper gastrointestinal bleeding (AUGIB). However, the impact of endoscopic timing on treatment outcomes remains a critical area of exploration.</p><p><strong>Objective: </strong>This study evaluated the role of endoscopic timing in improving treatment outcomes for AUGIB and introduces a predictive model incorporating clinical and technological insights.</p><p><strong>Methods: </strong>A retrospective analysis of AUGIB patients treated between December 2020 and December 2023 was conducted. Logistic regression identified significant predictors of outcomes, and receiver operating characteristic (ROC) analysis evaluated their predictive value. A predictive model was developed based on these findings.</p><p><strong>Results: </strong>Among 145 patients, 35 (24.1%) experienced rebleeding. Key predictors included endoscopic timing, active bleeding, shock on admission, and bleeding volume (p < 0.05). The predictive model demonstrated robust performance (C-index: 0.885, 95% CI: 0.810-0.956), emphasizing the clinical relevance of precise timing in endoscopic intervention.</p><p><strong>Conclusion: </strong>This study underscores the importance of integrating technology with clinical practice to optimize endoscopic timing and improve AUGIB outcomes. The predictive model offers a valuable tool for risk stratification and clinical decision-making in modern healthcare settings.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"9287329251316050"},"PeriodicalIF":1.4,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143505261","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Clinical decision system for chronic kidney disease staging using machine learning.","authors":"E Chandralekha, T R Saravanan, N Vijayaraj","doi":"10.1177/09287329251316447","DOIUrl":"https://doi.org/10.1177/09287329251316447","url":null,"abstract":"<p><strong>Background: </strong>Chronic Kidney Disease (CKD) is a prevalent health condition that requires personalized treatment planning at each of its five stages. Machine Learning (ML) and Generative AI have shown promise in predicting CKD progression based on patient data. However, existing prediction models have limitations on generalizability, interpretability, and resource requirements.</p><p><strong>Objective: </strong>This study aims to develop a clinical support system using ML models to classify CKD stages accurately. The research focuses on feature selection strategies and model performance evaluation to enhance prediction accuracy and guide personalized treatment planning for CKD patients.</p><p><strong>Methods: </strong>The study utilizes ML algorithms, including Gradient Boosting, XGBoost, CatBoost, and GAN AML, to categorize CKD stages. Various feature selection techniques such as Recursive Feature Elimination, chi-square test, and SHAP are employed to identify relevant features for improved prediction accuracy. The models are evaluated based on precision, recall, F1-score, accuracy, and AUC-ROC metrics.</p><p><strong>Conclusions: </strong>The findings demonstrate the effectiveness of CatBoost and GAN AML in accurately classifying CKD stages, highlighting the importance of expert knowledge in selecting feature selection strategies to enhance ML model performance. Future research directions include validating diverse datasets, integrating with clinical practice, and improving interpretability and explainability in CKD prediction models.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"9287329251316447"},"PeriodicalIF":1.4,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143505258","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}