Computer methods and programs in biomedicine update最新文献

{"title":"Machine learning-based diagnosis of breast cancer utilizing feature optimization technique","authors":"Khandaker Mohammad Mohi Uddin ,&nbsp;Nitish Biswas ,&nbsp;Sarreha Tasmin Rikta ,&nbsp;Samrat Kumar Dey","doi":"10.1016/j.cmpbup.2023.100098","DOIUrl":"10.1016/j.cmpbup.2023.100098","url":null,"abstract":"<div><p>Breast cancer disease is recognized as one of the leading causes of death in women worldwide after lung cancer. Breast cancer refers to a malignant neoplasm that develops from breast cells. Developed and less developed countries both are suffering from this extensive cancer. This cancer can be recuperated if it is detected at an early stage. Many researchers have proposed several machine learning techniques to predict breast cancer with the highest accuracy in the past years. In this research work, the Wisconsin Breast Cancer Dataset (WBCD) has been used as a training set from the UCI machine learning repository to compare the performance of the various machine learning techniques. Different kinds of machine learning classifiers such as support vector machine (SVM), Random Forest (RF), K-nearest neighbors(K-NN), Decision tree (DT), Naïve Bayes (NB), Logistic Regression (LR), AdaBoost (AB), Gradient Boosting (GB), Multi-layer perceptron (MLP), Nearest Cluster Classifier (NCC), and voting classifier (VC) have been used for comparing and analyzing breast cancer into benign and malignant tumors. Various matrices such as error rate, Accuracy, Precision, F1-score, and recall have been implemented to measure the model's performance. Each Algorithm's accuracy has been ascertained for finding the best suitable one. Based on the analysis, the result shows that the Voting classifier has the highest accuracy, which is 98.77%, with the lowest error rate. Finally, a web page is developed using a flask micro-framework integrating the best model using react.</p></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"3 ","pages":"Article 100098"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43734238","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":"Combining mathematical model for HRV mapping and machine learning to predict sudden cardiac death","authors":"Shahrzad Marjani ,&nbsp;Mohammad Karimi Moridani","doi":"10.1016/j.cmpbup.2023.100112","DOIUrl":"10.1016/j.cmpbup.2023.100112","url":null,"abstract":"<div><p>Sudden cardiac death, a prominent cause of mortality, often occurs within a narrow time window of less than an hour. This study introduces a novel methodology with the aim of early prediction of sudden cardiac death. The proposed approach involves the extraction of diverse features from the ECG signal, including the calculation of angles between two vectors, the computation of triangle areas formed by consecutive points, the determination of the shortest distance to a 450 line, and their combinations. Additionally, a thresholding technique is proposed to identify the risk period and predict the occurrence of sudden death. To assess the performance of the algorithm, data from the MIT-BH Holter database were utilized. The results obtained demonstrate that the angle feature achieves an average sensitivity of 93.75% with five false alarms, the area feature achieves an average sensitivity of 88.75% with nine false alarms, the shortest distance feature achieves an average sensitivity of 86.25% with 12 false alarms, and the combined feature achieves an average sensitivity of 96.25% with three false alarms. Remarkably, unlike existing methodologies in the literature, this method exhibits high accuracy in predicting the development of the risk of sudden cardiac death (SCD) even up to 30 min prior to onset. As a consequence, it plays a critical role in diagnosing patients' conditions and facilitating timely interventions. Moreover, the results confirm the feasibility of predicting cardiac arrest solely based on geometric features derived from variations in heart rate variability (HRV) dynamics.</p></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"4 ","pages":"Article 100112"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41463775","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":"Wearable devices for anxiety & depression: A scoping review","authors":"Arfan Ahmed ,&nbsp;Sarah Aziz ,&nbsp;Mahmood Alzubaidi ,&nbsp;Jens Schneider ,&nbsp;Sara Irshaidat ,&nbsp;Hashem Abu Serhan ,&nbsp;Alaa A Abd-alrazaq ,&nbsp;Barry Solaiman ,&nbsp;Mowafa Househ","doi":"10.1016/j.cmpbup.2023.100095","DOIUrl":"10.1016/j.cmpbup.2023.100095","url":null,"abstract":"<div><h3>Background</h3><p>The rates of mental health disorders such as anxiety and depression are at an all-time high especially since the onset of COVID-19, and the need for readily available digital health care solutions has never been greater. Wearable devices have increasingly incorporated sensors that were previously reserved for hospital settings. The availability of wearable device features that address anxiety and depression is still in its infancy, but consumers will soon have the potential to self-monitor moods and behaviors using everyday commercially-available devices.</p></div><div><h3>Objective</h3><p>This study aims to explore the features of wearable devices that can be used for monitoring anxiety and depression.</p></div><div><h3>Methods</h3><p>Six bibliographic databases, including MEDLINE, EMBASE, PsycINFO, IEEE Xplore, ACM Digital Library, and Google Scholar were used as search engines for this review. Two independent reviewers performed study selection and data extraction, while two other reviewers justified the cross-checking of extracted data. A narrative approach for synthesizing the data was utilized.</p></div><div><h3>Results</h3><p>From 2408 initial results, 58 studies were assessed and highlighted according to our inclusion criteria. Wrist-worn devices were identified in the bulk of our studies (<em>n</em> = 42 or 71%). For the identification of anxiety and depression, we reported 26 methods for assessing mood, with the State-Trait Anxiety Inventory being the joint most common along with the Diagnostic and Statistical Manual of Mental Disorders (<em>n</em> = 8 or 14%). Finally, <em>n</em> = 26 or 46% of studies highlighted the smartphone as a wearable device host device.</p></div><div><h3>Conclusion</h3><p>The emergence of affordable, consumer-grade biosensors offers the potential for new approaches to support mental health therapies for illnesses such as anxiety and depression. We believe that purposefully-designed wearable devices that combine the expertise of technologists and clinical experts can play a key role in self-care monitoring and diagnosis.</p></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"3 ","pages":"Article 100095"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9884643/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9636205","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":"Towards automatization of organoid analysis: A deep learning approach to localize and quantify organoid images","authors":"Asmaa Haja ,&nbsp;José M. Horcas-Nieto ,&nbsp;Barbara M. Bakker ,&nbsp;Lambert Schomaker","doi":"10.1016/j.cmpbup.2023.100101","DOIUrl":"10.1016/j.cmpbup.2023.100101","url":null,"abstract":"<div><p>The interest in the use of organoids in the biomedical field has increased exponentially in the past years. Organoids, or three-dimensional “mini-organs”, have the ability to proliferate and self-organize <em>in-vitro</em>, while displaying varying morphologies. When in culture, these structures can overlap with each other making the quantification and morphological characterization a challenging task. Quick and reliable analysis of organoid images could help in precisely modeling disease phenotypes as well as provide information on organ development. Therefore, automatization of the quantification and measurements is an important step towards an easier, faster, and less biased workflow.</p><p>In order to accomplish this, a free e-Science service (OrganelX) has been developed for localization and quantification of organoid size based on deep learning methods. The ability of the system was tested on murine liver organoids, and the data are made publicly available. The OrganelX e-Science free service is available at <span>https://organelx.hpc.rug.nl/organoid/</span><svg><path></path></svg>.</p></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"3 ","pages":"Article 100101"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42429858","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":"Improving SVM performance for type II diabetes prediction with an improved non-linear kernel: Insights from the PIMA dataset","authors":"Md.Shamim Reza ,&nbsp;Umme Hafsha ,&nbsp;Ruhul Amin ,&nbsp;Rubia Yasmin ,&nbsp;Sabba Ruhi","doi":"10.1016/j.cmpbup.2023.100118","DOIUrl":"10.1016/j.cmpbup.2023.100118","url":null,"abstract":"<div><p>Type 2 diabetes is a chronic metabolic disease that affects a significant portion of the worldwide people. Prediction of this disease using different machine learning (ML) based algorithms has gained substantial attention due to its potential for early detection and effective intervention. One of the most powerful ML algorithm support vector machines (SVM) has proven to be effective in a variety of classification tasks, including diabetes prediction. However, the kernel function chosen has a substantial effect on the performance of SVM classifiers. This paper proposes an improved non-linear kernel for the SVM model to enhance Type 2 diabetes classification. The new kernel uses radial basis function (RBF) and RBF city block kernels that enable SVM to learn complex decision boundaries and adapt to the intricacies of the PIMA dataset. The PIMA dataset contains various clinical and demographic features of individuals. To address missing values and outliers, we impute them using the median, ensuring the integrity of the dataset. We tackle the class imbalance issue by leveraging a robust synthetic-based over-sampling approach.</p><p>A comparative analysis is performed against several existing kernel functions to show that the proposed approach is superior in terms of various prediction evaluation matrices. Our recommended integrated kernel model also showed improved performance (ACC = 85.5, Recall = 87.0, Precision = 83.4, F1 score = 85.2, and AUC = 85.5) when compared to other approaches in the literature. The results of this study indicate that the proposed non-linear kernel in SVM outperforms existing kernel functions for predicting Type 2 diabetes using the PIMA dataset. Furthermore, a simulation study is carried out to robustify the proposed kernel in SVM and perform well. The improved accuracy and robustness of the model suggest its potential utility in clinical settings, aiding in the early identification and management of individuals at risk for developing diabetes.</p></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"4 ","pages":"Article 100118"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46853720","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":"A comparative approach to alleviating the prevalence of diabetes mellitus using machine learning","authors":"Md. Rifatul Islam ,&nbsp;Semonti Banik ,&nbsp;Kazi Naimur Rahman ,&nbsp;Mohammad Mizanur Rahman","doi":"10.1016/j.cmpbup.2023.100113","DOIUrl":"10.1016/j.cmpbup.2023.100113","url":null,"abstract":"<div><p>Diabetes mellitus, a metabolic disease with elevated blood sugar levels, is a significant global public health concern. Identification of diabetes at its very early stage can reduce the prevalence of cases. This work focuses on developing a machine learning-based system that will have a significant impact on diabetic patient identification. To develop such a system we utilized a dataset made up by acquiring direct questionnaires from Sylhet Diabetic Hospital patients. The dataset contains information about the signs and symptoms of patients who are new or likely to have diabetes. We applied 14 different machine-learning techniques where the Gradient Boosting Machine (GBM) outperformed other algorithms with the highest F1 and ROC scores of 99.37%, and 99.92% respectively. We also employed various ensemble-based approaches that show competitive performance to the individual model’s performance.</p></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"4 ","pages":"Article 100113"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48119339","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":"Development and validation of an electronic application (FoodEapp) to assess the dietary intake of adults in Karachi, Pakistan","authors":"Umber S Khan ,&nbsp;Maira Mubashir ,&nbsp;Tansheet Jawad ,&nbsp;Iqbal Azam ,&nbsp;Amna R Siddiqui ,&nbsp;Romaina Iqbal","doi":"10.1016/j.cmpbup.2023.100124","DOIUrl":"https://doi.org/10.1016/j.cmpbup.2023.100124","url":null,"abstract":"<div><h3>Background</h3><p>Under and over-nutrition-related health conditions are highly prevalent in Pakistan. Dietary data are required to understand the challenges of over and undernutrition in Pakistan.</p></div><div><h3>Objective</h3><p>The purpose of the study was to develop and validate a FoodEapp application (FoodEapp) for field staff with no formal education in nutrition (unskilled) to accurately collect 24-hour (24HR) dietary recall (DR) data to assess the dietary intake of adults in Karachi, Pakistan.</p></div><div><h3>Method</h3><p>We designed a novel FoodEapp application for unskilled data collectors to collect 24HR DR data. We validated the FoodEapp against the conventional 24HR DR method in rural and urban Karachi. We compared the mean intake of total energy (kcal), macronutrients, and micronutrients, reported through both methods using Pearson Correlation and Intraclass Correlation (ICC). We also used Bland Altman analysis to assess the agreement between the methods.</p></div><div><h3>Results</h3><p>We found a high correlation between the two methods for total energy (ρ = 0.88, <em>p</em>-value &lt; 0.001), protein (g) (ρ = 0.81, <em>p</em>-value &lt; 0.001), total lipids (g) (ρ = 0.74, <em>p</em>-value &lt; 0.001), and carbohydrates (g) (ρ = 0.68, <em>p</em>-value &lt; 0.001). Bland Altman's analysis showed good agreement in all the nutrients between the two methods.</p></div><div><h3>Conclusions</h3><p>FoodEapp has good validity and can be used to assess the dietary intake of the adult population in Karachi by non-nutritionists. This study may help overcome the limitation of dietary data collection and facilitate the researchers to conduct larger dietary surveys in Pakistan.</p></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"4 ","pages":"Article 100124"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49727004","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":"Semi-supervised active transfer learning for fetal ECG arrhythmia detection","authors":"Mohammad Reza Mohebbian ,&nbsp;Hamid Reza Marateb ,&nbsp;Khan A. Wahid","doi":"10.1016/j.cmpbup.2023.100096","DOIUrl":"https://doi.org/10.1016/j.cmpbup.2023.100096","url":null,"abstract":"","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"3 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49780851","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":"Exploring the impact of digital health literacy on COVID-19 behaviors in New York state college students during the COVID-19 pandemic","authors":"Molly Hadley ,&nbsp;Uday Patil ,&nbsp;Kimberly F. Colvin ,&nbsp;Tetine Sentell ,&nbsp;Philip M. Massey ,&nbsp;Mary Gallant ,&nbsp;Jennifer A. Manganello","doi":"10.1016/j.cmpbup.2023.100126","DOIUrl":"https://doi.org/10.1016/j.cmpbup.2023.100126","url":null,"abstract":"<div><p>Early in 2020, the COVID-19 pandemic became a global public health concern. College students became dependent on the online environment for learning, but also to receive COVID-19 information. Understanding digital health literacy and subsequent prevention behaviors in a digitally connected population during a public health crisis is crucial to prepare for future pandemics. This study focused on college students in the United States and explored whether digital health literacy predicted their main source of pandemic information, adherence to public health guidelines, and intentions to receive a COVID-19 vaccine. During the summer of 2020, 254 New York State college students completed the survey. Digital health literacy was found to predict using ‘<em>Government agencies websites’</em> as a main source of information and adherence to public health guidelines. It was not found to predict vaccine intentions. The findings confirm the importance of digital health literacy interventions in younger populations, especially with the rise of health misinformation available on the Internet.</p></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"4 ","pages":"Article 100126"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666990023000344/pdfft?md5=048d105c4d91a9f611fcbfdcf043a9f2&pid=1-s2.0-S2666990023000344-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138390524","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":"Improving assessment in kidney transplantation by multitask general path model","authors":"Qing Lan ,&nbsp;Xiaoyu Chen ,&nbsp;Murong Li ,&nbsp;John Robertson ,&nbsp;Yong Lei ,&nbsp;Ran Jin","doi":"10.1016/j.cmpbup.2023.100127","DOIUrl":"https://doi.org/10.1016/j.cmpbup.2023.100127","url":null,"abstract":"<div><h3>Background</h3><p>Kidney transplantation is a pivotal intervention for individuals suffering from end-stage renal diseases, offering them the potential for restored health and an enhanced quality of life. However, the successful outcome of these transplantation procedures relies significantly on the careful matching of donor kidneys with compatible recipients. Unfortunately, the current kidney-matching process overlooks viability changes during preservation. The objective of this study is to investigate the potential for forecasting heterogeneous kidney viability using historical datasets to enhance kidney-matching decision-making.</p></div><div><h3>Methods</h3><p>We present a multitask general path model designed for continuous forecasting of kidney viability during preservation. This model quantifies likely viability trajectories of donor kidneys based on pathologist-provided biopsy scores during preservation, explicitly addressing both inter-kidney similarities and individual differences. To validate our model, we conducted viability assessments on six recently procured porcine kidneys and needle biopsy insertion experiments on phantoms, utilizing a leave-one-kidney-out cross-validation approach.</p></div><div><h3>Results</h3><p>Our proposed model consistently exhibited the lowest forecasting error (averaged root mean squared error, RMSEbegin=0.61 at the beginning and RMSEend&lt;0.05 at the end of kidney preservation) when compared to widely-adopted benchmark models, including multitask learning (RMSEbegin=0.65, RMSEend=0.54), general path (RMSEbegin=0.58, RMSEend=0.49), and generalized linear models (RMSEbegin=0.59, RMSEend=0.56) in the kidney viability assessment study. Additionally, across all testing scenarios, the forecasting RMSE of our model rapidly diminished with minimal initial kidney samples during preservation. Similar patterns were observed from the needle biopsy insertion study.</p></div><div><h3>Conclusions</h3><p>In both validation studies, our model outperformed benchmark models and exhibited rapid learning with limited initial samples. This approach holds promise for enhancing kidney transplantation decision-making, including improving tissue extraction accuracy through needle biopsy data analysis. By implementing this model across various kidney assessment stages in transplantation, we aim to reduce kidney discards and benefit a larger number of patients.</p></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"4 ","pages":"Article 100127"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666990023000356/pdfft?md5=946267e50461f174efd8003330cdeb27&pid=1-s2.0-S2666990023000356-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138430873","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}