To achieve a more general and unbiased evaluation of input variable importance in a predictive environment, this paper proposes XAIRE. This methodology leverages multiple predictive models. We present an ensemble method that aggregates outputs from various prediction models for determining a relative importance ranking. In order to reveal any statistically significant differences in the relative importance of the predictor variables, the methodology utilizes statistical testing. In a case study application, XAIRE was used to examine patient arrivals at a hospital emergency department, producing a dataset with one of the most extensive sets of diverse predictor variables found in any published work. From the extracted knowledge, the relative significance of the case study's predictors is apparent.
High-resolution ultrasound is an advancing technique for recognizing carpal tunnel syndrome, a disorder due to the compression of the median nerve at the wrist. A systematic review and meta-analysis was undertaken to examine and collate data on the efficacy of deep learning algorithms in automated sonographic evaluations of the median nerve at the carpal tunnel.
Deep neural networks' application in assessing the median nerve for carpal tunnel syndrome was explored in studies culled from PubMed, Medline, Embase, and Web of Science, encompassing the period from earliest records to May 2022. Using the Quality Assessment Tool for Diagnostic Accuracy Studies, the quality of the included studies underwent evaluation. The outcome was assessed through the lens of precision, recall, accuracy, F-score, and the Dice coefficient.
From the collection of articles, 373 participants were found in seven included studies. U-Net, phase-based probabilistic active contour, MaskTrack, ConvLSTM, DeepNerve, DeepSL, ResNet, Feature Pyramid Network, DeepLab, Mask R-CNN, region proposal network, and ROI Align are part of the broader category of deep learning algorithms. The aggregated precision and recall values were 0.917 (95% confidence interval 0.873-0.961) and 0.940 (95% confidence interval 0.892-0.988), respectively. Concerning pooled accuracy, the result was 0924, with a 95% confidence interval of 0840 to 1008. The Dice coefficient was 0898 (95% CI 0872-0923), and the summarized F-score was 0904, within a 95% confidence interval from 0871 to 0937.
The deep learning algorithm permits accurate and precise automated localization and segmentation of the median nerve at the carpal tunnel in ultrasound images. Subsequent research is projected to authenticate the efficacy of deep learning methods in recognizing and segmenting the median nerve throughout its entirety across data sets collected using diverse ultrasound manufacturing equipment.
Deep learning algorithms successfully automate the localization and segmentation of the median nerve at the carpal tunnel level within ultrasound images, with acceptable levels of accuracy and precision. Further studies are anticipated to validate the performance of deep learning algorithms in identifying and segmenting the median nerve along its full length, encompassing datasets from a variety of ultrasound manufacturers.
The paradigm of evidence-based medicine demands that medical decisions be made by relying on the most up-to-date and substantiated knowledge accessible through published studies. Systematic reviews and/or meta-reviews frequently encapsulate existing evidence, which is rarely presented in a structured fashion. The expense of manual compilation and aggregation is substantial, and a systematic review demands a considerable investment of effort. The need to collect and synthesize evidence isn't limited to clinical trials; it's equally pertinent to pre-clinical studies using animal subjects. To ensure the successful translation of promising pre-clinical therapies into clinical trials, the act of evidence extraction is crucial for improving and streamlining the clinical trial design process. To address the task of aggregating evidence from published pre-clinical research, this paper proposes a novel system for automatically extracting and storing structured knowledge in a domain knowledge graph. Leveraging a domain ontology, the approach facilitates model-complete text comprehension, resulting in a detailed relational data structure mirroring the principal concepts, procedures, and key findings of the studies. Regarding spinal cord injury, a pre-clinical study's single outcome is detailed by up to 103 outcome parameters. The problem of extracting all the variables together proves to be intractable, thus we propose a hierarchical architecture that iteratively constructs semantic sub-structures according to a predefined data model, moving from the bottom to the top. Conditional random fields underpin a statistical inference method integral to our approach. This method is utilized to determine the most likely instance of the domain model, given the input text from a scientific publication. This approach enables a semi-interconnected way to model dependencies among the diverse variables used in the study. Evaluating our system's capacity for in-depth study analysis, crucial for generating novel knowledge, forms the core of this comprehensive report. We wrap up the article with a brief exploration of real-world applications of the populated knowledge graph and examine how our research can contribute to the advancement of evidence-based medicine.
During the SARS-CoV-2 pandemic, the need for software systems that facilitated patient categorization, specifically concerning potential disease severity or even the risk of death, was dramatically emphasized. This article evaluates a collection of Machine Learning algorithms, taking plasma proteomics and clinical data as input, to forecast the severity of conditions. This report details AI-based advancements in COVID-19 patient management, showcasing the scope of applicable technical progress. This review documents the creation and deployment of an ensemble machine learning algorithm to analyze COVID-19 patient clinical and biological data (plasma proteomics, in particular) with the goal of evaluating AI's potential for early patient triage. The proposed pipeline is evaluated on three publicly accessible datasets, with separate training and testing sets. Ten distinct ML tasks are outlined, and various algorithms are meticulously evaluated using hyperparameter tuning to pinpoint the models exhibiting the highest performance. Overfitting, a substantial concern when the size of the training and validation datasets is constrained, is addressed through the application of a multitude of evaluation metrics in these kinds of approaches. Evaluation results showed recall scores spanning a range from 0.06 to 0.74, and F1-scores demonstrating a similar variation from 0.62 to 0.75. Multi-Layer Perceptron (MLP) and Support Vector Machines (SVM) algorithms are observed to yield the best performance. In addition, the input data, encompassing proteomics and clinical data, were ranked based on their corresponding Shapley additive explanations (SHAP) values, and their predictive power and immuno-biological importance were evaluated. Our machine learning models, employing an interpretable approach, revealed that critical COVID-19 cases were largely determined by patient age and plasma proteins linked to B-cell dysfunction, excessive activation of inflammatory pathways like Toll-like receptors, and diminished activation of developmental and immune pathways such as SCF/c-Kit signaling. The computational methodology detailed in this document is independently verified using a separate dataset, demonstrating the advantages of MLPs and supporting the predictive biological pathways previously described. The presented machine learning pipeline's effectiveness is hampered by the limitations of the datasets, specifically the low sample size (below 1000 observations) coupled with the extensive input features, which create a high-dimensional, low-sample (HDLS) dataset susceptible to overfitting. see more By combining biological data (plasma proteomics) with clinical-phenotypic data, the proposed pipeline provides a significant advantage. Hence, the described approach, when implemented on pre-trained models, could potentially allow for rapid patient prioritization. While promising, confirmation of the clinical value of this methodology mandates larger data sets and further systematic validation. Within the Github repository, https//github.com/inab-certh/Predicting-COVID-19-severity-through-interpretable-AI-analysis-of-plasma-proteomics, you will find the code enabling prediction of COVID-19 severity using interpretable AI and plasma proteomics data.
Improvements in medical care are often linked to the rising use of electronic systems within the healthcare sector. Yet, the broad application of these advancements culminated in a dependency which can hinder the physician-patient rapport. Digital scribes, acting as automated clinical documentation systems within this context, record physician-patient conversations at appointments and subsequently produce the necessary documentation, freeing physicians to fully focus on their patients. Our review of the relevant literature focused on intelligent approaches to automatic speech recognition (ASR) coupled with automatic documentation of medical interviews, utilizing a systematic methodology. Rapid-deployment bioprosthesis Original research, and only original research, was the boundary of the project, specifically addressing systems for detecting, transcribing, and structuring speech in a natural and organized way in sync with doctor-patient exchanges, while excluding solely speech-to-text conversion applications. Initial results from the search encompassed 1995 titles, but only eight met the criteria for both inclusion and exclusion. A core component of the intelligent models was an ASR system with natural language processing capabilities, complemented by a medical lexicon and structured text output. Each of the articles, at the time of their release, lacked mention of a commercially produced item and instead detailed the constricted real-world experience. Reproductive Biology Prospective validation and testing of the applications within large-scale clinical studies remains incomplete to date.