Diagnosis Model Research Articles

Delving into the Complexity of Analogical Reasoning: A Detailed Exploration with the Generalized Multicomponent Latent Trait Model for Diagnosis.

Research on analogical reasoning has facilitated the understanding of response processes such as pattern identification and creative problem solving, emerging as an intelligence predictor. While analogical tests traditionally combine various composition rules for item generation, current statistical models like the Logistic Latent Trait Model (LLTM) and Embretson's Multicomponent Latent Trait Model for Diagnosis (MLTM-D) face limitations in handling the inherent complexity of these processes, resulting in suboptimal model fit and interpretation. The primary aim of this research was to extend Embretson's MLTM-D to encompass complex multidimensional models that allow the estimation of item parameters. Concretely, we developed a three-parameter (3PL) version of the MLTM-D that provides more informative interpretations of participant response processes. We developed the Generalized Multicomponent Latent Trait Model for Diagnosis (GMLTM-D), which is a statistical model that extends Embretson's multicomponent model to explore complex analogical theories. The GMLTM-D was compared with LLTM and MLTM-D using data from a previous study of a figural analogical reasoning test composed of 27 items based on five composition rules: figure rotation, trapezoidal rotation, reflection, segment subtraction, and point movement. Additionally, we provide an R package (GMLTM) for conducting Bayesian estimation of the models mentioned. The GMLTM-D more accurately replicated the observed data compared to the Bayesian versions of LLTM and MLTM-D, demonstrating a better model fit and superior predictive accuracy. Therefore, the GMLTM-D is a reliable model for analyzing analogical reasoning data and calibrating intelligence tests. The GMLTM-D embraces the complexity of real data and enhances the understanding of examinees' response processes.

The Bayesian approach for real-world implementation of plasma p-tau217 in tertiary care memory clinics in Thailand.

Plasma phosphorylated tau (p-tau)217 is a promising biomarker for Alzheimer's disease (AD) diagnosis, but its clinical implementation remains challenging. We propose a strategy based on Bayes' theorem and test it in real-life memory clinics. Memory clinic patients were evaluated by neurocognitive specialists for prespecified diagnosis and subsequently underwent blood collection for p-tau217, cerebrospinal fluid, or amyloid positron emission tomography. Using cross-validation, the Bayesian approach (pretest probability × individualized likelihood ratio) was compared to other models for AD diagnosis. The Bayesian strategy demonstrated an area under the receiver operating characteristic curve (AUC) of 0.98 (95% confidence interval [CI]: 0.96-1.0), significantly outperforming multivariable logistic regression (p-tau217, age, apolipoprotein E; AUC 0.95, p=0.024) and p-tau217 alone (AUC=0.94, p=0.007). When applying the two-threshold approach, the Bayesian strategy yielded an accuracy of 0.94 (95% CI: 0.88-1.0) without requiring confirmatory tests in 62.9% of the iterations. The Bayesian strategy offers an effective and flexible approach to address the limitations of plasma p-tau217 in clinical practice. Incorporating pretest probability into the interpretation of plasma phosphorylated tau (p-tau)217 improves the diagnostic performance significantly. The strategy could obviate the need for confirmatory testing in most of the patients. Plasma p-tau217 proves useful as a biomarker for Alzheimer's disease in low- and middle-income country such as Thailand.

Fault diagnosis of a wave energy converter gearbox based on an Adam optimized CNN-LSTM algorithm

The complex structure and harsh operating environment of wave energy converters can result in various faults in transmission components. Environmental noise in practical operating situations may obscure the effective information in collected vibration signals, significantly increasing the difficulty of fault diagnosis. This paper presents a fault diagnosis model for the gearbox of the point absorber wave energy converter. The model integrates a convolutional neural network with long short-term memory to realize efficient extraction of local features from signals and enhance the performance in time-series analysis. Moreover, the model incorporates the Adaptive Moment Estimation algorithm to address the situations where gradients within tensors exhibit unstable changes in the model. A rigid-flexible coupled dynamics simulation model is developed to simulate vibration signals used to train and verify the fault diagnosis model. Experimental tests of the proposed model on a vibration dataset acquired from real vibration experiments demonstrate its efficacy in diagnosing various types of faults under interference of operating conditions. Comparative studies with other models demonstrate the superiority of the proposed model in terms of fault feature extraction, learning convergence efficiency, and diagnostic accuracy, indicating that the proposed model can achieve faster and more accurate fault diagnosis of wave energy converter gearboxes.

Development and validation of a deep learning-based model for diabetes diagnosis

Diabetes is a systemic disease caused by hyperglycemia, and the number of people with diabetes worldwide may reach 1.31 billion by 2050. The traditional diagnosis of early diabetes is difficult and inaccurate. Computer-aided automatic method has been widely applied in diabetes diagnosis at early stage. In order to realize automatic diagnosis of diabetes, this study proposed a deep neural network-based model for diabetes diagnosis, and its performance was compared with those of other classical machine leaning models. After data cleaning, Synthetic Minority Over-sampling Technique (SMOTE) sampling and feature selecting of the diabetes dataset, multiple models were applied to the features such as Body Mass Index (BMI), Age and other features for prediction. The results showed that Deep Neural Network (DNN) is the best diagnostic solution for diabetes with excellent performance with an accuracy of 99.5%. More specific features will be considered to ensure the accuracy and credibility of clinical diagnosis of diabetes.

Alzheimer’s disease detection and stage identification from magnetic resonance brain images using vision transformer

Machine learning techniques applied in neuroimaging have prompted researchers to build models for early diagnosis of brain illnesses such as Alzheimer’s disease (AD). Although this task is difficult, advanced deep-learning (DL) approaches can be used. These DL models are effective, but difficult to interpret, time-consuming, and resource-intensive. Therefore, neuroscientists are interested in employing novel, less complex structures such as transformers that have superior pattern-extraction capabilities. In this study, an automated framework for accurate AD diagnosis and precise stage identification was developed by employing vision transformers (ViTs) with fewer computational resources. ViT, which captures the global context as opposed to convolutional neural networks (CNNs) with local receptive fields, is more efficient for brain image processing than CNN because the brain is a highly complex network with connected parts. The self-attention mechanism in the ViT helps to achieve this goal. Magnetic resonance brain images belonging to four stages were utilized to develop the proposed model, which achieved 99.83% detection accuracy, 99.69% sensitivity, 99.88% specificity, and 0.17% misclassification rate. Moreover, to prove the ability of the model to generalize, the mean distances of the transformer blocks and attention heat maps were visualized to understand what the model learned from the MRI input image.

Physics-informed neural network estimation of material properties in soft tissue nonlinear biomechanical models

AbstractThe development of biophysical models for clinical applications is rapidly advancing in the research community, thanks to their predictive nature and their ability to assist the interpretation of clinical data. However, high-resolution and accurate multi-physics computational models are computationally expensive and their personalisation involves fine calibration of a large number of parameters, which may be space-dependent, challenging their clinical translation. In this work, we propose a new approach, which relies on the combination of physics-informed neural networks (PINNs) with three-dimensional soft tissue nonlinear biomechanical models, capable of reconstructing displacement fields and estimating heterogeneous patient-specific biophysical properties and secondary variables such as stresses and strains. The proposed learning algorithm encodes information from a limited amount of displacement and, in some cases, strain data, that can be routinely acquired in the clinical setting, and combines it with the physics of the problem, represented by a mathematical model based on partial differential equations, to regularise the problem and improve its convergence properties. Several benchmarks are presented to show the accuracy and robustness of the proposed method with respect to noise and model uncertainty and its great potential to enable the effective identification of patient-specific, heterogeneous physical properties, e.g. tissue stiffness properties. In particular, we demonstrate the capability of PINNs to detect the presence, location and severity of scar tissue, which is beneficial to develop personalised simulation models for disease diagnosis, especially for cardiac applications.

A comparative study of heterogeneous machine learning algorithms for arrhythmia classification using feature selection technique and multi-dimensional datasets

Arrhythmia, a common cardiovascular disorder, refers to the abnormal electrical activity within the heart, leading to irregular heart rhythms. This condition affects millions of people worldwide, with severe implications on cardiac function and overall health. Arrhythmias can strike anyone at any age which is a significant cause of morbidity and mortality on a global scale. About 80% of deaths related to heart disease are caused by ventricular arrhythmias. This research investigated the application of an optimized multi-objectives supervised Machine Learning (ML) models for early arrhythmia diagnosis. The authors evaluated the model’s performance on the arrhythmia dataset from the UCI ML repository with varying train-test splits (70:30, 80:20, and 90:10). Standard preprocessing techniques such as handling missing values, formatting, balancing, and directory analysis were applied along with Pearson correlation for feature selection, all aimed at enhancing model performance. The proposed optimized RF model achieved impressive performance metrics, including accuracy (95.24%), precision (100%), sensitivity (89.47%), and specificity (100%). Furthermore, the study compared the proposed approach to existing models, demonstrating significant improvements across various performance measures.

Explainable machine learning on baseline MRI predicts multiple sclerosis trajectory descriptors.

Multiple sclerosis (MS) is a multifaceted neurological condition characterized by challenges in timely diagnosis and personalized patient management. The application of Artificial Intelligence (AI) to MS holds promises for early detection, accurate diagnosis, and predictive modeling. The objectives of this study are: 1) to propose new MS trajectory descriptors that could be employed in Machine Learning (ML) regressors and classifiers to predict patient evolution; 2) to explore the contribution of ML models in discerning MS trajectory descriptors using only baseline Magnetic Resonance Imaging (MRI) studies. This study involved 446 MS patients who had a baseline MRI, at least two measurements of Expanded Disability Status Scale (EDSS), and a 1-year follow-up. Patients were divided into two groups: 1) for model development and 2) for evaluation. Three descriptors: β1, β2, and EDSS(t), were related to baseline MRI parameters using regression and classification XGBoost models. Shapley Additive Explanations (SHAP) analysis enhanced model transparency by identifying influential features. The results of this study demonstrate the potential of AI in predicting MS progression using the proposed patient trajectories and baseline MRI scans, outperforming classic Multiple Linear Regression (MLR) methods. In conclusion, MS trajectory descriptors are crucial; incorporating AI analysis into MRI assessments presents promising opportunities to advance predictive capabilities. SHAP analysis enhances model interpretation, revealing feature importance for clinical decisions.

Intelligent fault diagnosis of bearings driven by double-level data fusion based on multichannel sample fusion and feature fusion under time-varying speed conditions

Bearing fault diagnosis holds paramount importance in guaranteeing the smooth operation of mechanical systems. However, single-channel vibration sensor data poses limitations in providing a comprehensive representation of bearing fault characteristics, and the fault diagnosis model extracts fault features with insufficient representativeness, resulting in low reliability and poor generalization capability of the model in the face of variable operating conditions. Therefore, a novel intelligent fault diagnosis model of bearings driven by double-level data fusion (DLDF) is developed in this paper. Initially, the fusion weights of each channel's data are obtained using the information entropy method. Then, a novel multi-modal image fusion strategy is proposed to integrate the time-frequency representations of vibration data from channels X, Y, and Z at a specific measurement point. Finally, the shallow and deep feature components extracted from the CNN model and the feature components obtained through different mapping methods are fused to extract more representative fault features, achieving a reliable diagnosis of bearing faults under time-varying speed conditions. The efficacy and reliability of the proposed approach are demonstrated through the fault diagnosis outcomes of two sets of bearing experimental data under time-varying speed conditions.

Detection of minimal hepatic encephalopathy in patients with cirrhosis based on the Stroop-CN model (NCRCID-CHESS 2106): aprospective multicenter study.

Minimal hepatic encephalopathy (MHE) has a substantial impact on the clinical outcomes and quality of life (QOL) of patients with cirrhosis. However, timely diagnosis and intervention are challenging due to sophisticated diagnostic methods. In this study, 673 healthy controls and 905 patients with cirrhosis were screened, and 660 healthy controls and 757 patients with cirrhosis, divided into the test (292 patients) and validation (465 patients) cohort, were analyzed after screening. A diagnostic model of the Stroop test (Stroop-CN) was constructed by multivariate linear regression based on the results of healthy controls. The prevalence of MHE and the comparison results with psychometric hepatic encephalopathy score through the Stroop-CN model were stable in the test and validation cohorts. Moreover, the prevalence of MHE remained significantly higher in patients with worse disease conditions marked as high Child-Pugh grades and the Model for End-stage Liver Disease and Sodium (MELD-Na) scores in the test and validation cohort. The EuroQol 5-D questionnaire revealed that patients with MHE had a worse QOL than those without MHE both in the test and validation cohort. In conclusion, an easy and practical Stroop-CN model for MHE diagnosis based on the EncephalApp is established. It is found that a considerable number of Chinese patients with cirrhosis experience MHE, which significantly impacts their QOL.

Implementation of the Macao dementia policy: a scoping review for the way forward.

The implementation of dementia policy is a complex process of translating policy goals to actions to address the changing needs of people living with dementia. Leveraging on others' experiences would help policy decision-makers and actors better prepare for the challenges. This study explored the development, the implementation and the impact of the dementia policy in Macao, a "role model" recognized by the Alzheimer's Disease International. A scoping review of policies, strategies, and news articles, as well as scholarly work from 6 scientific databases dated till March 2023 was conducted under the guidance of the Health Policy Triangle Framework. According to 284 documents, the dementia policy in Macao, driven by government leadership and supported with public-private partnership, aimed to integrate health and social services to achieve the goals of "Early prevention, Early detection, Early diagnosis, Early treatment and Early support." Promoting the preparedness according to the dementia burden trajectory, empowering the public and the service providers with training and education, and encouraging services-related research were among the key actions. With major changes in dementia care configuration, a dementia service network, a dementia-friendly community and a one-stop service model for disease screening, diagnosis, treatment and support have been developed. Reconfiguring existing resources in the health and social services to form an integrated service network at the community level could be considered a priority of action. Continuous engagement, collaboration and empowerment at different levels across these sectors is crucial for the sustainability of a dementia policy.

CMACF: Transformer-based cross-modal attention cross-fusion model for systemic lupus erythematosus diagnosis combining Raman spectroscopy, FTIR spectroscopy, and metabolomics

As complex multi-omics data in the medical field tend to be multi-modal. Integrating these multimodal information into novel disease diagnosis models has become challenging. However, previous methods mainly focus on single omics, which cannot effectively capture the contributions between different combinations of multi-omics information. To solve this problem, based on Raman spectroscopy, FTIR spectroscopy, and metabolomics data, this paper proposes a new Cross-modal Cross-fusion network based on the Transformer self-attention mechanism (CMACF). The research focuses on effectively combining the feature patterns of different omics for disease prediction. Specifically, by constructing the Raman-IR, Raman-metabolomic, and IR spectral-metabolomic feature pairs and reasonably focusing on the information of different combination pairs through multiple stages of feature sub-network, attention cross-fusion, bimodal interaction, and sequence interaction feature level fusion, it is interesting to find that the information contribution between different pairs is different. We conducted extensive experiments on the systemic lupus erythematosus multi-omics dataset, and the accuracy and AUC values are as high as 99.44 % and 99.98 %, respectively, with the best classification effect. The results show that CMACF can efficiently fuse multi-omics medical data, provide an efficient baseline for processing medical multimodal data, and analyze the contribution of multi-omics data fusion.

Research on Transformer Fault Diagnosis by WOA‐SVM Based on Feature Selection and Data Balancing

AbstractOil‐immersed transformers as one of the most important equipment in the power system, the fault prediction of it in advance can effectively reduce the subsequent harm. Aiming at the selection of input features and data sample imbalance in the transformer fault diagnosis model, this paper adopts the recursive feature elimination (RFE) method combined with SMOTETomek comprehensive sampling method to optimize the above problems. First, RFE is used to traverse all the features and filter the optimal combination of them as input features, then SMOTETomek is used to perform balancing operation on the samples of the train set, and finally, whale optimization algorithm (WOA) is used to find the best hyperparameters for support vector machine (SVM), and the results are compared with the diagnostic models operated without processing and after single processing operation, respectively. After several sets of experiments, it is proved that the optimized comprehensive fault diagnosis model performs better on the test set than both the untreated and the singly processed models, which proves the effectiveness of the methodology used in this paper. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Machine Tool Fault Diagnosis Model Based on Discrete Wavelet Transform and Transfer Learning

Machine Tool Fault Diagnosis Model Based on Discrete Wavelet Transform and Transfer Learning

Cancer Diagnosis by Gene-Environment Interactions via Combination of SMOTE-Tomek and Overlapped Group Screening Approaches with Application to Imbalanced TCGA Clinical and Genomic Data

The complexity of cancer development involves intricate interactions among multiple biomarkers, such as gene-environment interactions. Utilizing microarray gene expression profile data for cancer classification is anticipated to be effective, thus drawing considerable interest in the fields of bioinformatics and computational biology. Due to the characteristics of genomic data, problems of high-dimensional interactions and noise interference do exist during the analysis process. When building cancer diagnosis models, we often face the dilemma of model adaptation errors due to an imbalance of data types. To mitigate the issues, we apply the SMOTE-Tomek procedure to rectify the imbalance problem. Following this, we utilize the overlapping group screening method alongside a binary logistic regression model to integrate gene pathway information, facilitating the identification of significant biomarkers associated with clinically imbalanced cancer or normal outcomes. Simulation studies across different imbalanced rates and gene structures validate our proposed method’s effectiveness, surpassing common machine learning techniques in terms of classification prediction accuracy. We also demonstrate that prediction performance improves with SMOTE-Tomek treatment compared to no imbalance treatment and SMOTE treatment across various imbalance rates. In the real-world application, we integrate clinical and gene expression data with prior pathway information. We employ SMOTE-Tomek and our proposed methods to identify critical biomarkers and gene-environment interactions linked to the imbalanced binary outcomes (cancer or normal) in patients from the Cancer Genome Atlas datasets of lung adenocarcinoma and breast invasive carcinoma. Our proposed method consistently achieves satisfactory classification accuracy. Additionally, we have identified biomarkers indicative of gene-environment interactions relevant to cancer and have provided corresponding estimates of odds ratios. Moreover, in high-dimensional imbalanced data, for achieving good prediction results, we recommend considering the order of balancing processing and feature screening.

Lightweight bearing fault diagnosis method based on cross-scale learning transformer under imbalanced data*

Abstract Due to the limited amount of failure data in rolling bearing faults, traditional fault diagnosis models encounter challenges such as low diagnostic accuracy and efficiency when dealing with imbalanced data. Additionally, many fault diagnosis models are overly complex and demand high computational resources. To address these issues, a lightweight bearing fault diagnosis method based on cross-scale learnable transformer (CSLT) is proposed for imbalanced data. For difficult-to-classify samples, a learnable generalized focal loss function is defined. The learnable parameters are employed to increase its flexibility, it better addresses the issue of bearing fault diagnosis under imbalanced data conditions. Then, a multi-head broadcasted self-attention mechanism is designed by capturing critical local features of the signal through one-dimensional convolution operations, which not only improves feature extraction capability but also reduces computational complexity. Finally, a dynamic label prediction pruning module is developed to trim redundant labels, which helps in lightening the model and enhancing both feature extraction and diagnostic efficiency. The experimental results demonstrate that the proposed diagnosis method exhibits superior diagnostic precision and efficiency by comparing with other methods.

Transformer partial discharge fault diagnosis based on improved adaptive local iterative filtering‐bidirectional long short‐term memory

AbstractInsulation deterioration, which is mainly caused by partial discharge (PD) occurring inside power transformers, is one of the prime reasons to cause transformer faults. Therefore, an effective diagnosis of PD is crucial to ensure the safe and stable operation of transformers. To extract more effective features that characterise transformers PD signals and enhance the recognition accuracy, a novel transformer PD fault diagnosis model based on improved adaptive local iterative filtering (ALIF) and bidirectional long short‐term memory (BILSTM) neural network is proposed. Addressing the issue of predetermined decomposition levels and accuracy in ALIF decomposition, the golden jackal optimisation (GJO) algorithm is introduced to optimise the parameters. The proposed fault diagnostic model extracts dominant PD features employing the improved ALIF and Refined Composite Multi‐Scale Dispersion Entropy and improves the diagnostic accuracy with the optimised BILSTM by introducing GJO. Experimental data evaluates the performance of support vector machine, long short‐term memory and BILSTM. The results verify the effectiveness and superiority of the proposed model.

A predictive model for ischemic colitis: Integrating clinical and laboratory parameters

AbstractObjectiveWe aimed to develop a predictive model for the clinical diagnosis of ischemic colitis (IC).MethodsClinical data were collected from patients with acute IC lesions who were diagnosed and admitted to Beijing Tsinghua Changgung Hospital from January 2016 to December 2022. These patients were included in the IC case group in this retrospective observational study. The control group comprised patients aged ≥40 years who were diagnosed with abdominal pain during the same period, excluding those with IC. All patients were divided into a training and test sets based on the time window. Least absolute shrinkage and selection operator regression was used to screen risk factors for the occurrence of IC. Logistic stepwise regression (maximum likelihood ratio method) was performed in multifactorial analysis, and a diagnostic prediction model for IC was established using R language. The area under the receiver operating characteristic (ROC) curve (AUC) was examined to assess differentiation using working ROC curves. We used bootstrap resampling (1000 times) for internal validation. Model calibration curves and decision curve analysis (DCA) were also applied.ResultsOur study indicates that constipation, hematochezia, neutrophil counts, and specific abdominal computed tomography (CT) (plain scan) findings, including intestinal wall edema and thickening, intestinal lumen stenosis, and dilation, are independent predictors of IC. The predictive model exhibited high discriminative ability with an AUC of 0.9788 in the training set, and the calibration and DCA curves demonstrated excellent model performance. After validation, the AUC remained robust at 0.9868, underscoring the model's reliability in predicting IC.ConclusionAccording to our model, constipation accompanied by hematochezia necessitates careful consideration of IC. Abdominal CT (plain scan) is an effective diagnostic tool for IC, and it is common for patients to exhibit elevated neutrophil counts. The predictive model, demonstrating high discriminative ability and accuracy, shows promise for practical application in clinical settings, aiding in the early diagnosis and management of IC.

Determining nitrogen status using a universal critical nitrogen dilution curve for predicting flue-cured tobacco yield and quality

The critical nitrogen (Nc) dilution curve is considered an effective method for crop nitrogen (N) diagnosis, fertilization decision-making, and crop modeling. The Nc dilution curve is typically described using a power function curve (Nc = A1W−A2). This study established a universal Nc dilution curve and evaluated its potential application in flue-cured tobacco (Nicotiana tabacum L.) N diagnosis, yield, and quality regulation. Field experiments were conducted involving three tobacco varieties (Honghuadajinyuan, Yunyan87, and K326) and four N application rates (0 to > 90 kg N ha−1). The study was conducted in Southwest China from 2021 to 2022. The universal Nc curve obtained across all experimental data was 3.45 W−0.39. The parameter A2 was more susceptible to genotype and environment interactions than parameter A1. The uncertainty first rapidly decreased and then remained at a lower value as biomass increased. The relationship between leaf biomass and days after transplantation and accumulated growth degree was robust. The N nutrition index (NNI) and accumulated N deficiency effectively determined leaf N status and N demand, respectively. There was an inverse correlation between the NNI, leaf yield, and between the superior and medium leaf ratios. The quantitative relationship can be used to adjust the synergistic production of tobacco yield and leaf quality.

Fault diagnosis of drivetrain systems based on vibration signals and convolutional neural networks

Accurate and fast fault diagnosis can improve the reliability of drivetrain systems. The fault diagnosis model based on convolutional neural networks is tricky because the accuracy and efficiency of diagnosis depend on the neural network parameters, such as layer size and number of layers. In this paper, we developed a data pre-processing technique to format vibrational data to be receptible to a neural network. First one will study the difference in performance by inputting a spectrogram of the vibrational data compared to the raw data itself. Second, the windowizing technique is used and its parameters are explored to train different configurations of convolutional neural networks. Finally, the optimal neural network parameters are obtained to improve the computational efficiency while the training model has the highest accuracy.