Computer methods and programs in biomedicine update最新文献

Background

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.

Objective

This study aims to explore the features of wearable devices that can be used for monitoring anxiety and depression.

Methods

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.

Results

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 (n = 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 (n = 8 or 14%). Finally, n = 26 or 46% of studies highlighted the smartphone as a wearable device host device.

Conclusion

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.

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 in-vitro, 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.

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 https://organelx.hpc.rug.nl/organoid/.

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.

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.

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.

Background

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.

Objective

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.

Method

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.

Results

We found a high correlation between the two methods for total energy (ρ = 0.88, p-value < 0.001), protein (g) (ρ = 0.81, p-value < 0.001), total lipids (g) (ρ = 0.74, p-value < 0.001), and carbohydrates (g) (ρ = 0.68, p-value < 0.001). Bland Altman's analysis showed good agreement in all the nutrients between the two methods.

Conclusions

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.

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 ‘Government agencies websites’ 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.

Background

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.

Methods

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.

Results

Our proposed model consistently exhibited the lowest forecasting error (averaged root mean squared error, RMSEbegin=0.61 at the beginning and RMSEend<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.

Conclusions

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.

Machine Learning (ML) algorithms can be used to analyze metabolomic expression data to explore the association between metabolite expression and disease etiology. In this study, we used and compared the performance of ML algorithms to analyze polar aqueous and blood-based lipid-based metabolites to identify meaningful patterns correlated with the development of gallstone disease (GSD) while examining the sex disparity. We also developed ML approaches that used clinical risk factors for predicting GSD, including age, obesity, body mass index, hemoglobin A1c, dyslipidemia index cholesterol to high-density lipoprotein ratio (CHOL/HDL). A more powerful data fusion model that combines both metabolomic and clinical features achieved accuracy of 83% for accurate prediction of the presence of GSD.

The Coronavirus, known as COVID-19, which appeared in 2019 in China, has significantly affected the global health and become a huge burden on health institutions all over the world. These effects are continuing today. One strategy for limiting the virus's transmission is to have an early diagnosis of suspected cases and take appropriate measures before the disease spreads further. This work aims to diagnose and show the probability of getting infected by the disease according to textual clinical data. In this work, we used five machine learning techniques (GWO_MLP, GWO_CMLP, MGWO_MLP, FDO_MLP, FDO_CMLP) all of which aim to classify Covid-19 patients into two categories (Positive and Negative). Experiments showed promising results for all used models. The applied methods showed very similar performance, typically in terms of accuracy. However, in each tested dataset, FDO_MLP and FDO_CMLP produced the best results with 100% accuracy. The other models' results varied from one experiment to the other. It is concluded that the models on which the FDO algorithm was used as a learning algorithm had the possibility of obtaining higher accuracy. However, it is found that FDO has the longest runtime compared to the other algorithms. The link to the Covid 19 models is found here: https://github.com/Tarik4Rashid4/covid19models