Employing a multifaceted approach, this paper presents XAIRE, a new methodology. XAIRE quantifies the relative importance of input variables within a predictive system, leveraging multiple models to broaden its applicability and reduce the biases of a specific learning method. We present an ensemble method that aggregates outputs from various prediction models for determining a relative importance ranking. Methodology includes statistical tests to demonstrate any significant discrepancies in how important the predictor variables are relative to one another. 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. The case study's results show the relative priorities of the predictors, as suggested by the extracted knowledge.
A method emerging for diagnosing carpal tunnel syndrome, a disorder caused by the median nerve being compressed at the wrist, is high-resolution ultrasound. The purpose of this systematic review and meta-analysis was to explore and collate findings regarding the performance of deep learning algorithms applied to automatic sonographic assessments of the median nerve at the carpal tunnel.
A database search including PubMed, Medline, Embase, and Web of Science was conducted to find studies evaluating deep neural network applications for the assessment of the median nerve in carpal tunnel syndrome, ranging from the earliest records to May 2022. The Quality Assessment Tool for Diagnostic Accuracy Studies was employed to assess the quality of the incorporated studies. The outcome variables consisted of precision, recall, accuracy, the F-score, and the Dice coefficient.
In the study, seven articles with 373 participants were analyzed in totality. The diverse and sophisticated deep learning algorithms, including 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 extensively used. The aggregate values for precision and recall were 0.917 (95% confidence interval [CI] 0.873-0.961) and 0.940 (95% CI 0.892-0.988), respectively. Accuracy, when pooled, yielded a value of 0924 (95% CI: 0840-1008). The Dice coefficient, in comparison, scored 0898 (95% CI: 0872-0923). The summarized F-score, meanwhile, was 0904 (95% CI: 0871-0937).
The deep learning algorithm facilitates automated localization and segmentation of the median nerve at the carpal tunnel in ultrasound images with acceptable levels of accuracy and precision. Subsequent investigations are anticipated to affirm the efficacy of deep learning algorithms in the identification and delineation of the median nerve throughout its entirety, encompassing data from diverse ultrasound production sources.
An acceptable level of accuracy and precision is demonstrated by the deep learning algorithm, which enables automated localization and segmentation of the median nerve in carpal tunnel ultrasound images. Future research endeavors are projected to confirm the accuracy of deep learning algorithms in detecting and precisely segmenting the median nerve over its entire course, including data gathered from various ultrasound manufacturing companies.
In accordance with the paradigm of evidence-based medicine, the best current knowledge found in the published literature must inform medical decision-making. The existing body of evidence is often condensed into systematic reviews or meta-reviews, and is rarely accessible in a structured format. A high price is paid for manual compilation and aggregation, and a systematic review process demands a noteworthy investment of time and effort. Clinical trials are not the sole context demanding evidence aggregation; pre-clinical animal studies also necessitate its application. For the successful transition of promising pre-clinical therapies into clinical trials, effective evidence extraction is essential, enabling optimized trial design and improved outcomes. The development of methods to aggregate evidence from pre-clinical studies is addressed in this paper, which introduces a new system automatically extracting structured knowledge and storing it within a domain knowledge graph. The approach to model-complete text comprehension leverages a domain ontology to generate a deep relational data structure. This structure embodies the core concepts, protocols, and key findings of the studies. A single pre-clinical outcome measurement in spinal cord injury research involves as many as 103 different parameters. We propose a hierarchical architecture, given the intractability of extracting all these variables at once, which incrementally predicts semantic sub-structures, based on a given data model, in a bottom-up manner. To infer the most probable domain model instance, our strategy employs a statistical inference method relying on conditional random fields, starting from the text of a scientific publication. A semi-integrated modeling of the interdependencies among the different variables describing a study is enabled by this approach. A comprehensive evaluation of our system's analytical abilities regarding a study's depth is presented, with the objective of elucidating its capacity for enabling the generation of novel knowledge. To conclude, we offer a succinct account of some applications of the populated knowledge graph, demonstrating the potential influence of our work on evidence-based medicine.
The SARS-CoV-2 pandemic underscored the critical requirement for software applications capable of streamlining patient triage, assessing potential disease severity, or even imminent mortality. This article evaluates a collection of Machine Learning algorithms, taking plasma proteomics and clinical data as input, to forecast the severity of conditions. A review of AI-enhanced techniques for managing COVID-19 patients is presented, illustrating the current range of relevant technological advancements. The review underscores the development and implementation of an ensemble machine learning algorithm, analyzing clinical and biological data (plasma proteomics included) from COVID-19 patients, to assess the application of AI for early patient triage. Using three openly available datasets, the proposed pipeline is evaluated for training and testing performance. Three machine learning tasks have been established, and a hyperparameter tuning method is used to test a number of algorithms, identifying the ones with the best 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. Within the evaluation protocol, recall scores exhibited a spectrum from 0.06 to 0.74, while F1-scores spanned the range of 0.62 to 0.75. Multi-Layer Perceptron (MLP) and Support Vector Machines (SVM) algorithms are observed to yield the best performance. Input data, comprising proteomics and clinical information, were ranked using corresponding Shapley additive explanations (SHAP) values, and their prognostic capacity and immunobiologic significance were evaluated. Using an interpretable analysis, our machine learning models found that critical COVID-19 cases were primarily determined by patient age and plasma proteins relating to B-cell dysfunction, heightened activation of inflammatory pathways such as Toll-like receptors, and diminished activity within 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 use of datasets with less than 1000 observations and a large number of input features in this study generates a high-dimensional low-sample (HDLS) dataset, thereby posing a risk of overfitting in the presented machine learning pipeline. see more The proposed pipeline's effectiveness stems from its combination of plasma proteomics biological data and clinical-phenotypic data. Consequently, the proposed method, when applied to pre-existing trained models, has the potential to expedite patient prioritization. To establish the genuine clinical worth of this technique, a more substantial dataset and a detailed validation protocol are paramount. Within the repository located at https//github.com/inab-certh/Predicting-COVID-19-severity-through-interpretable-AI-analysis-of-plasma-proteomics, on Github, you'll find the code enabling the prediction of COVID-19 severity through an interpretable AI approach, specifically using plasma proteomics data.
Healthcare systems are now significantly reliant on electronic systems, frequently resulting in enhancements to medical treatment. Although this is true, the wide-scale implementation of these technologies ultimately cultivated a dependent relationship which can disrupt the doctor-patient rapport. Digital scribes, which are automated clinical documentation systems in this context, capture the entire physician-patient conversation during each appointment, then produce the required documentation, enabling full physician engagement with patients. Our systematic review addressed the pertinent literature concerning intelligent systems for automatic speech recognition (ASR) in medical interviews, coupled with automatic documentation. Genetic material damage The scope of this research encompassed only original studies focusing on speech detection and transcription systems that could produce natural and structured outputs in real-time conjunction with the doctor-patient dialogue, with the exclusion of mere speech-to-text conversion tools. The search query produced 1995 entries, of which only eight articles satisfied the stringent inclusion and exclusion parameters. An ASR system, coupled with natural language processing, a medical lexicon, and structured text output, formed the fundamental architecture of the intelligent models. No commercially available product accompanied any of the articles released at that point in time; each focused instead on the constrained spectrum of practical applications. exudative otitis media Prospective validation and testing of the applications within large-scale clinical studies remains incomplete to date.