Healthcare Technology Letters最新文献

{"title":"Feasibility study of a clinical decision support system for polymedicated patients in primary care","authors":"Juan Manuel Pinar Manzanet,&nbsp;Giuseppe Fico,&nbsp;Beatriz Merino-Barbancho,&nbsp;Liss Hernández,&nbsp;Cecilia Vera-Muñoz,&nbsp;Germán Seara,&nbsp;Macarena Torrego,&nbsp;Henar Gonzalez,&nbsp;Jonas Wastesson,&nbsp;Johan Fastbom,&nbsp;Julio Mayol,&nbsp;Kristina Johnell,&nbsp;Tomás Gómez-Gascón,&nbsp;María Teresa Arredondo","doi":"10.1049/htl2.12046","DOIUrl":"10.1049/htl2.12046","url":null,"abstract":"<p>Age-related changes in pharmacokinetics and pharmacodynamics, multimorbidity, frailty, and cognitive impairment represent challenges for drug treatments. Moreover, older adults are commonly exposed to polypharmacy, leading to increased risk of drug interactions and related adverse events, and higher costs for the healthcare systems. Thus, the complex task of prescribing medications to older polymedicated patients encourages the use of Clinical Decision Support Systems (CDSS). This paper evaluates the CDSS miniQ for identifying potentially inappropriate prescribing in poly-medicated older adults and assesses the usability and acceptability of the system in health care professionals, patients, and caregivers. The results of the study demonstrate that the miniQ system was useful for Primary Care physicians in significantly improving prescription, thereby reducing potentially inappropriate medication prescriptions for elderly patients. Additionally, the system was found to be beneficial for patients and their caregivers in understanding their medications, as well as usable and acceptable among healthcare professionals, patients, and caregivers, highlighting the potential to improve the prescription process and reduce errors, and enhancing the quality of care for elderly patients with polypharmacy, reducing adverse drug events, and improving medication management.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"10 3","pages":"62-72"},"PeriodicalIF":2.1,"publicationDate":"2023-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/65/e0/HTL2-10-62.PMC10230557.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9940020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automatic cardiac arrhythmias classification using CNN and attention-based RNN network","authors":"Jie Sun","doi":"10.1049/htl2.12045","DOIUrl":"10.1049/htl2.12045","url":null,"abstract":"<p>Cardiac disease has become a severe threat to public health according to the government report. In China, there are 0.29 billion cardiac patients and early diagnosis will greatly reduce mortality and improve life quality. Electrocardiogram (ECG) signal is a priority tool in the diagnosis of heart diseases because it is non-invasive and easily available with a simple diagnostic tool of low cost. The paper proposes an automatic classification model by combing convolutional neural network (CNN) and recurrent neural network (RNN) to distinguish different types of cardiac arrhythmias. Morphology features of the raw ECG signals are extracted by CNN blocks and fed into a bidirectional gated recurrent unit (GRU) network. Attention mechanism is used to highlight specific features of the input sequence and contribute to the performance improvement of classification. The model is evaluated with two datasets considering the class imbalance problem constructed with records from MIT-BIH arrhythmia database and China Physiological Signal Challenge 2018 database. Experimental results show that this model achieves good performance with an average F1 score of 0.9110 on public dataset and 0.9082 on subject-specific dataset, which may have potential practical applications.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"10 3","pages":"53-61"},"PeriodicalIF":2.1,"publicationDate":"2023-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/9b/ac/HTL2-10-53.PMC10230559.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9923670","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detection and classification of atrial and ventricular cardiovascular diseases to improve the cardiac health literacy for resource constrained regions","authors":"Neha Arora,&nbsp;Biswajit Mishra","doi":"10.1049/htl2.12043","DOIUrl":"10.1049/htl2.12043","url":null,"abstract":"<p>ECG is a non-invasive way of determining cardiac health by measuring the electrical activity of the heart. A novel detection technique for feature points P, QRS and T is investigated to diagnose various atrial and ventricular cardiovascular anomalies with ECG signals for ambulatory monitoring. Before the system is worthy of field trials, it is validated with several databases and recorded their response. The QRS complex detection is based on the Pan Tompkins algorithm and difference operation method that provides positive predictivity, sensitivity and false detection rate of 99.29%, 99.49% and 1.29%, respectively. Proposed novel T wave detection provides sensitivity of 97.78%. Also, proposed P wave detection provides positive predictivity, sensitivity and false detection rate of 99.43%, 99.4% and 1.15% for the control study (normal subjects) and 82.68%, 94.3% and 25.4% for the case (patients with cardiac anomalies) study, respectively. Disease detection such as arrhythmia is based on standard R-R intervals while myocardial infarction is based on the ST-T deviations where the positive predictivity, sensitivity and accuracy are observed to be 94.6%, 84.2% and 85%, respectively. It should be noted that, since the frontal leads are only used, the anterior myocardial infarction cases are detected with the injury pattern in lead <i>avl</i> and ST depression in reciprocal leads. Detection of atrial fibrillation is done for both short and long duration signals using statistical methods using interquartile range and standard deviations, giving very high accuracy, 100% in most cases. The system hardware for obtaining the 2 lead ECG signal is designed using commercially available off the shelf components. Small field validation of the designed system is performed at a Public Health Centre in Gujarat, India with 42 patients (both cases and controls). 78.5% accuracy was achieved during the field validation. It is thus concluded that the proposed method is ideal for improvisation in cardiac health monitoring outreach in resource constrained regions.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"10 3","pages":"35-52"},"PeriodicalIF":2.1,"publicationDate":"2023-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/htl2.12043","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9923668","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On the prediction of premature births in Hispanic labour patients using uterine contractions, heart beat signals and prediction machines","authors":"Ejay Nsugbe,&nbsp;Jose Javier Reyes-Lagos,&nbsp;Dawn Adams,&nbsp;Oluwarotimi Williams Samuel","doi":"10.1049/htl2.12044","DOIUrl":"10.1049/htl2.12044","url":null,"abstract":"<p>Preterm birth is a global epidemic affecting millions of mothers across different ethnicities. The cause of the condition remains unknown but has recognised health-based implications, in addition to financial and economic ones. Machine Learning methods have enabled researchers to combine datasets using uterine contraction signals with various forms of prediction machines to improve awareness of the likelihood of premature births. This work investigates the feasibility of enhancing these prediction methods using physiological signals including uterine contractions, and foetal and maternal heart rate signals, for a population of south American women in active labour. As part of this work, the use of the Linear Series Decomposition Learner (LSDL) was seen to lead to an improvement in the prediction accuracies of all models, which included supervised and unsupervised learning models. The results from the supervised learning models showed high prediction metrics upon the physiological signals being pre-processed by the LSDL for all variations of the physiological signals. The unsupervised learning models showed good metrics for the partitioning of Preterm/Term labour patients from their uterine contraction signals but produced a comparatively lower set of results for the various kinds of heart rate signals investigated.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"10 1-2","pages":"11-22"},"PeriodicalIF":2.1,"publicationDate":"2023-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/htl2.12044","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9378174","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Acceptance factors of telemedicine technology during Covid-19 pandemic among health professionals: A qualitative study","authors":"Rouidi Mohammed,&nbsp;Elouadi Abd Elmajid,&nbsp;Hamdoune Amine,&nbsp;Choujtani Khadija","doi":"10.1049/htl2.12042","DOIUrl":"10.1049/htl2.12042","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>Health professionals are the main users of telemedicine systems, and their acceptance will contribute to the successful implementation of this technology. The objective of this study is to provide a better understanding of the issues surrounding the acceptance of telemedicine technology by Moroccan health professionals in the public sector, in the preparation for a possible generalization of this technology in Morocco.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Method</h3>\u0000 \u0000 <p>Following a literature review, the authors mobilized a modified version of the unified model of technology acceptance and use, to explain and understand the determinants of health professionals’ intention to accept telemedicine technology. The authors’ methodology is based on a qualitative analysis and is primarily based on data obtained through semi-structured interviews with health professionals, who the authors believe are the primary actors in the acceptance of this technology within Moroccan hospitals.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The authors’ results suggest that performance expectancy, effort expectancy, compatibility, facilitating conditions, perceived incentives, and social influence have a significant positive impact on health professionals’ behavioural intention to accept telemedicine technology.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Practical implications</h3>\u0000 \u0000 <p>From a practical point of view, the results of this study can help the government, organizations responsible for the implementation of telemedicine, and policymakers to understand the key factors that may affect the behaviour of future users of this technology, and to develop very specific strategies and policies for a successful generalization.</p>\u0000 </section>\u0000 </div>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"10 1-2","pages":"23-33"},"PeriodicalIF":2.1,"publicationDate":"2023-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/e6/b7/HTL2-10-23.PMC10107386.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9378173","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An integrated approach for mental health assessment using emotion analysis and scales","authors":"N. Shanthi, Albert Alexander Stonier, A. Sherine, T. Devaraju, S. Abinash, R. Ajay, V. Arul Prasath, Vivekananda Ganji","doi":"10.1049/htl2.12040","DOIUrl":"https://doi.org/10.1049/htl2.12040","url":null,"abstract":"","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"1 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41686657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diabetes prediction using machine learning and explainable AI techniques","authors":"Isfafuzzaman Tasin,&nbsp;Tansin Ullah Nabil,&nbsp;Sanjida Islam,&nbsp;Riasat Khan","doi":"10.1049/htl2.12039","DOIUrl":"10.1049/htl2.12039","url":null,"abstract":"<p>Globally, diabetes affects 537 million people, making it the deadliest and the most common non-communicable disease. Many factors can cause a person to get affected by diabetes, like excessive body weight, abnormal cholesterol level, family history, physical inactivity, bad food habit etc. Increased urination is one of the most common symptoms of this disease. People with diabetes for a long time can get several complications like heart disorder, kidney disease, nerve damage, diabetic retinopathy etc. But its risk can be reduced if it is predicted early. In this paper, an automatic diabetes prediction system has been developed using a private dataset of female patients in Bangladesh and various machine learning techniques. The authors used the Pima Indian diabetes dataset and collected additional samples from 203 individuals from a local textile factory in Bangladesh. Feature selection algorithm mutual information has been applied in this work. A semi-supervised model with extreme gradient boosting has been utilized to predict the insulin features of the private dataset. SMOTE and ADASYN approaches have been employed to manage the class imbalance problem. The authors used machine learning classification methods, that is, decision tree, SVM, Random Forest, Logistic Regression, KNN, and various ensemble techniques, to determine which algorithm produces the best prediction results. After training on and testing all the classification models, the proposed system provided the best result in the XGBoost classifier with the ADASYN approach with 81% accuracy, 0.81 F1 coefficient and AUC of 0.84. Furthermore, the domain adaptation method has been implemented to demonstrate the versatility of the proposed system. The explainable AI approach with LIME and SHAP frameworks is implemented to understand how the model predicts the final results. Finally, a website framework and an Android smartphone application have been developed to input various features and predict diabetes instantaneously. The private dataset of female Bangladeshi patients and programming codes are available at the following link: https://github.com/tansin-nabil/Diabetes-Prediction-Using-Machine-Learning.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"10 1-2","pages":"1-10"},"PeriodicalIF":2.1,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/htl2.12039","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9384370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Validity and feasibility of remote measurement systems for functional movement and posture assessments in people with axial spondylarthritis","authors":"Erin Hannink,&nbsp;Maedeh Mansoubi,&nbsp;Neil Cronin,&nbsp;Benjamin Wilkins,&nbsp;Ali A. Najafi,&nbsp;Benjamin Waller,&nbsp;Helen Dawes","doi":"10.1049/htl2.12038","DOIUrl":"10.1049/htl2.12038","url":null,"abstract":"<p>Introduction: This study aimed to estimate the criterion validity of functional movement and posture measurement using remote technology systems in people with and without Axial spondylarthritis (axSpA).</p><p>Methods: Validity and agreement of the remote-technology measurement of functional movement and posture were tested cross-sectionally and compared to a standard clinical measurement by a physiotherapist. The feasibility of remote implementation was tested in a home environment. There were two cohorts of participants: people with axSpA and people without longstanding back pain. In addition, a cost-consequence analysis was performed.</p><p>Results: Sixty-two participants (31 with axSPA, 53% female, age = 45(SD14), BMI = 26.6(SD4.6) completed the study. In the axSpA group, cervical rotation, lumbar flexion, lumbar side flexion, shoulder flexion, hip abduction, tragus-to-wall and thoracic kyphosis showed a significant moderate to strong correlation; in the non-back pain group, the same measures showed significant correlation ranging from weak to strong.</p><p>Conclusions: Although not valid for clinical use in its current form, the remote technologies demonstrated moderate to strong correlation and agreement in most functional and postural tests measured in people with AxSA. Testing the CV-aided system in a home environment suggests it is a safe and feasible method. Yet, validity testing in this environment still needs to be performed.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"9 6","pages":"110-118"},"PeriodicalIF":2.1,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9731560/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10361200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Alzheimer's disease classification using cluster-based labelling for graph neural network on heterogeneous data","authors":"Niamh McCombe,&nbsp;Jake Bamrah,&nbsp;Jose M. Sanchez-Bornot,&nbsp;David P. Finn,&nbsp;Paula L. McClean,&nbsp;KongFatt Wong-Lin,&nbsp;Alzheimer's Disease Neuroimaging Initiative (ADNI)","doi":"10.1049/htl2.12037","DOIUrl":"10.1049/htl2.12037","url":null,"abstract":"<p>Biomarkers for Alzheimer's disease (AD) diagnosis do not always correlate reliably with cognitive symptoms, making clinical diagnosis inconsistent. In this study, the performance of a graphical neural network (GNN) classifier based on data-driven diagnostic classes from unsupervised clustering on heterogeneous data is compared to the performance of a classifier using clinician diagnosis as an outcome. Unsupervised clustering on tau-positron emission tomography (PET) and cognitive and functional assessment data was performed. Five clusters embedded in a non-linear uniform manifold approximation and project (UMAP) space were identified. The individual clusters revealed specific feature characteristics with respect to clinical diagnosis of AD, gender, family history, age, and underlying neurological risk factors (NRFs). In particular, one cluster comprised mainly diagnosed AD cases. All cases within this cluster were re-labelled AD cases. The re-labelled cases are characterized by high cerebrospinal fluid amyloid beta (CSF Aβ) levels at a younger age, even though Aβ data was not used for clustering. A GNN model was trained using the re-labelled data with a multiclass area-under-the-curve (AUC) of 95.2%, higher than the AUC of a GNN trained on clinician diagnosis (91.7%; <i>p</i> = 0.02). Overall, our work suggests that more objective cluster-based diagnostic labels combined with GNN classification may have value in clinical risk stratification and diagnosis of AD.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"9 6","pages":"102-109"},"PeriodicalIF":2.1,"publicationDate":"2022-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9731537/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10729996","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A systematic comparison of the accuracy of monocular RGB tracking and LiDAR for neuronavigation","authors":"Talha Khan,&nbsp;Jacob T. Biehl,&nbsp;Edward G. Andrews,&nbsp;Dmitriy Babichenko","doi":"10.1049/htl2.12036","DOIUrl":"10.1049/htl2.12036","url":null,"abstract":"<p>With the advent of augmented reality (AR), the use of AR-guided systems in the field of medicine has gained traction. However, the wide-scale adaptation of these systems requires highly accurate and reliable tracking. In this work, the tracking accuracy of two technology platforms, LiDAR and Vuforia, are developed and rigorously tested for a catheter placement neurological procedure. Several experiments (900) are performed for each technology across various combinations of catheter lengths and insertion trajectories. This analysis shows that the LiDAR platform outperformed Vuforia; which is the state-of-the-art in monocular RGB tracking solutions. LiDAR had 75% less radial distance error and 26% less angle deviation error. Results provide key insights into the value and utility of LiDAR-based tracking in AR guidance systems.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"9 6","pages":"91-101"},"PeriodicalIF":2.1,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/d6/3d/HTL2-9-91.PMC9731545.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10361198","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}