Impact of gist intervention on automated system interpretability and user decision making.

As they become more common, automated systems are also becoming increasingly opaque, challenging their users' abilities to explain and interpret their outputs. In this study, we test the predictions of fuzzy-trace theory-a leading theory of how people interpret quantitative information-on user decision making after interacting with an online decision aid. We recruited a sample of 205 online crowdworkers and asked them to use a system that was designed to detect URLs that were part of coordinated misinformation campaigns. We examined how user endorsements of system interpretability covaried with performance on this coordinated misinformation detection task and found that subjects who endorsed system interpretability displayed enhanced discernment. This interpretability was, in turn, associated with both objective mathematical ability and mathematical self-confidence. Beyond these individual differences, we evaluated the impact of a theoretically motivated intervention that was designed to promote sensemaking of system output. Participants provided with a "gist" version of system output, expressing the bottom-line meaning of that output, were better able to identify URLs that might have been part of a coordinated misinformation campaign, compared to users given the same information presented as verbatim quantitative metrics. This work highlights the importance of enabling users to grasp the essential, gist meaning of the information they receive from automated systems, which benefits users regardless of individual differences.

Multitask learning to predict successful weaning in critically ill ventilated patients: A retrospective analysis of the MIMIC-IV database.

Objective: Weaning is an essential issue in critical care. This study explores the efficacy of multitask learning models in predicting successful weaning in critically ill ventilated patients using the Medical Information Mart for Intensive Care (MIMIC) IV database. Methods: We employed a multitask learning framework with a shared bottom network to facilitate common knowledge extraction across all tasks. We used the Shapley additive explanations (SHAP) plot and partial dependence plot (PDP) for model explainability. Furthermore, we conducted an error analysis to assess the strength and limitation of the model. Area under receiver operating characteristic curve (AUROC), calibration plot and decision curve analysis were used to determine the performance of the model. Results: A total of 7758 critically ill patients were included in the analyses, and 78.5% of them were successfully weaned. Multitask learning combined with spontaneous breath trial achieved a higher performance to predict successful weaning compared with multitask learning combined with shock and mortality (area under receiver operating characteristic curve, AUROC, 0.820 ± 0.002 vs 0.817 ± 0.001, p < 0.001). We assessed the performance of the model using calibration and decision curve analyses and further interpreted the model through SHAP and PDP plots. The error analysis identified a relatively high error rate among those with low disease severities, including low mean airway pressure and high enteral feeding. Conclusion: We demonstrated that multitask machine learning increased predictive accuracy for successful weaning through combining tasks with a high inter-task relationship. The model explainability and error analysis should enhance trust in the model.

Visual interpretation of deep learning model in ECG classification: A comprehensive evaluation of feature attribution methods.

Feature attribution methods can visually highlight specific input regions containing influential aspects affecting a deep learning model's prediction. Recently, the use of feature attribution methods in electrocardiogram (ECG) classification has been sharply increasing, as they assist clinicians in understanding the model's decision-making process and assessing the model's reliability. However, a careful study to identify suitable methods for ECG datasets has been lacking, leading researchers to select methods without a thorough understanding of their appropriateness. In this work, we conduct a large-scale assessment by considering eleven popular feature attribution methods across five large ECG datasets using a model based on the ResNet-18 architecture. Our experiments include both automatic evaluations and human evaluations. Annotated datasets were utilized for automatic evaluations and three cardiac experts were involved for human evaluations. We found that Guided Grad-CAM, particularly when its absolute values are utilized, achieves the best performance. When Guided Grad-CAM was utilized as the feature attribution method, cardiac experts confirmed that it can identify diagnostically relevant electrophysiological characteristics, although its effectiveness varied across the 17 different diagnoses that we have investigated.

Deep-Learning-Based Disease Classification in Patients Undergoing Cine Cardiac MRI.

BACKGROUND: Automated approaches may allow for fast, reproducible clinical assessment of cardiovascular diseases from MRI. PURPOSE: To develop an MRI-based deep learning (DL) disease classification algorithm to distinguish among normal subjects (NORM), patients with dilated cardiomyopathy (DCM), hypertrophic cardiomyopathy (HCM), and ischemic heart disease (IHD). STUDY TYPE: Retrospective. POPULATION: A total of 1337 subjects (55% female), comprising normal subjects (N = 568), and patients with DCM (N = 151), HCM (N = 177), and IHD (N = 441). FIELD STRENGTH/SEQUENCE: Balanced steady-state free precession cine sequence at 1.5/3.0 T. ASSESSMENT: Bi-ventricular morphological and functional features and global and segmental left ventricular strain features were automatically extracted from short- and long-axis cine images. Variational autoencoder models were trained on the extracted features and compared against consensus disease label provided by two expert readers (13 and 14 years of experience). Adding unlabeled, normal data to the training was explored to increase specificity of NORM class. STATISTICAL TESTS: Tenfold cross-validation for model development; mean, standard deviation (SD) for measurements; classification metrics: area under the curve (AUC), confusion matrix, accuracy, specificity, precision, recall; 95% confidence intervals; Mann-Whitney U test for significance. RESULTS: AUCs of 0.952 for NORM, 0.881 for DCM, 0.908 for HCM, and 0.856 for IHD and overall accuracy of 0.778 were obtained, with specificity of 0.908 for the NORM class using both SAX and LAX features. Longitudinal strain features slightly improved classification metrics by 0.001 to 0.03 points, except for HCM-AUC. Differences in accuracy, metrics for NORM class and HCM-AUC were statistically significant. Cotraining using unlabeled data increased the specificity for the NORM class to 0.961. DATA CONCLUSION: Cardiac function features automatically extracted from cine MRI have potential to be used for disease classification, especially for normal-abnormal classification. Feature analyses showed that strain features were important for disease labeling. Cotraining using unlabeled data may help to increase specificity for normal-abnormal classification. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 1.

An integrated framework for driving risk evaluation that combines lane-changing detection and an attention-based prediction model.

OBJECTIVE: In recent years, the increase in traffic accidents has emerged as a significant social issue that poses a serious threat to public safety. The objective of this study is to predict risky driving scenarios to improve road safety. METHODS: On the basis of data collected from naturalistic driving real-vehicle experiments, a comprehensive framework for identifying and analyzing risky driving scenarios, which combines an integrated lane-changing detection model and an attention-based long short-term memory (LSTM) prediction model, is proposed. The performance of the 4 machine learning methods on the CULane data set is compared in terms of model running time and running speed as evaluation metrics, and the ultrafast network with the best performance is selected as the method for lane line detection. We compared the performance of LSTM and attention-based LSTM on the basis of the prediction accuracy, recall, precision, and F1 value and selected the better model (attention-based LSTM) for risky scenario prediction. Furthermore, Shapley additive explanation analysis (SHAP) is used to understand and interpret the prediction results of the model. RESULTS: In terms of algorithm efficiency, the running time of the ultrafast lane detection network only requires 4.1 ms, and the average detection speed reaches 131 fps. For prediction performance, the accuracy rate of attention-based LSTM reaches 96%, the precision rate is 98%, the recall rate is 96%, and the F1 value is 97%. CONCLUSIONS: The improved attention-based LSTM model is significantly better than the LSTM model in terms of convergence speed and prediction accuracy and can accurately identify risky scenarios that occur during driving. The importance of factors varies by risky scenario. The characteristics of the yaw rate, speed stability, vehicle speed, acceleration, and lane change significantly influence the driving risk, among which lane change has the greatest impact. This study can provide real-time risky scenario prediction, warnings, and scientific decision guidance for drivers.

PDNAPred: Interpretable prediction of protein-DNA binding sites based on pre-trained protein language models.

Protein-DNA interactions play critical roles in various biological processes and are essential for drug discovery. However, traditional experimental methods are labor-intensive and unable to keep pace with the increasing volume of protein sequences, leading to a substantial number of proteins lacking DNA-binding annotations. Therefore, developing an efficient computational method to identify protein-DNA binding sites is crucial. Unfortunately, most existing computational methods rely on manually selected features or protein structure information, making these methods inapplicable to large-scale prediction tasks. In this study, we introduced PDNAPred, a sequence-based method that combines two pre-trained protein language models with a designed CNN-GRU network to identify DNA-binding sites. Additionally, to tackle the issue of imbalanced dataset samples, we employed focal loss. Our comprehensive experiments demonstrated that PDNAPred significantly improved the accuracy of DNA-binding site prediction, outperforming existing state-of-the-art sequence-based methods. Remarkably, PDNAPred also achieved results comparable to advanced structure-based methods. The designed CNN-GRU network enhances its capability to detect DNA-binding sites accurately. Furthermore, we validated the versatility of PDNAPred by training it on RNA-binding site datasets, showing its potential as a general framework for amino acid binding site prediction. Finally, we conducted model interpretability analysis to elucidate the reasons behind PDNAPred's outstanding performance.

Enhancing health assessments with large language models: A methodological approach.

Health assessments have long been a significant research topic within the field of health psychology. By analyzing the results of subject scales, these assessments effectively evaluate physical and mental health status. Traditional methods, based on statistical analysis, are limited in accuracy due to their reliance on linear scoring methods. Meanwhile, machine learning approaches, despite their potential, have not been widely adopted due to their poor interpretability and dependence on large amounts of training data. Recently, large language models (LLMs) have gained widespread attention for their powerful natural language understanding capabilities, offering a viable solution to these issues. This study investigates the application of LLMs in enhancing physical and mental health assessments, introducing ScaleLLM. ScaleLLM employs language and knowledge alignment to turn LLMs into expert evaluators for health psychology scales. Experimental results indicate that ScaleLLM can improve the accuracy and interpretability of health assessments.

A multi-task learning model for clinically interpretable sesamoiditis grading.

Sesamoiditis is a common equine disease with varying severity, leading to increased injury risks and performance degradation in horses. Accurate grading of sesamoiditis is crucial for effective treatment. Although deep learning-based approaches for grading sesamoiditis show promise, they remain underexplored and often lack clinical interpretability. To address this issue, we propose a novel, clinically interpretable multi-task learning model that integrates clinical knowledge with machine learning. The proposed model employs a dual-branch decoder to simultaneously perform sesamoiditis grading and vascular channel segmentation. Feature fusion is utilized to transfer knowledge between these tasks, enabling the identification of subtle radiographic variations. Additionally, our model generates a diagnostic report that, along with the vascular channel mask, serves as an explanation of the model's grading decisions, thereby increasing the transparency of the decision-making process. We validate our model on two datasets, demonstrating its superior performance compared to state-of-the-art models in terms of accuracy and generalization. This study provides a foundational framework for the interpretable grading of similar diseases.

Should AI models be explainable to clinicians?

In the high-stakes realm of critical care, where daily decisions are crucial and clear communication is paramount, comprehending the rationale behind Artificial Intelligence (AI)-driven decisions appears essential. While AI has the potential to improve decision-making, its complexity can hinder comprehension and adherence to its recommendations. "Explainable AI" (XAI) aims to bridge this gap, enhancing confidence among patients and doctors. It also helps to meet regulatory transparency requirements, offers actionable insights, and promotes fairness and safety. Yet, defining explainability and standardising assessments are ongoing challenges and balancing performance and explainability can be needed, even if XAI is a growing field.

Interpretable machine learning for allergic rhinitis prediction among preschool children in Urumqi, China.

This study aimed to investigate the advantages and applications of machine learning models in predicting the risk of allergic rhinitis (AR) in children aged 2-8, compared to traditional logistic regression. The study analyzed questionnaire data from 7131 children aged 2-8, which was randomly divided into training, validation, and testing sets in a ratio of 55:15:30, repeated 100 times. Predictor variables included parental allergy, medical history during the child's first year (cfy), and early life environmental factors. The time of first onset of AR was restricted to after the age of 1 year to establish a clear temporal relationship between the predictor variables and the outcome. Feature engineering utilized the chi-square test and the Boruta algorithm, refining the dataset for analysis. The construction utilized Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting Tree (XGBoost) as the models. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC), and the optimal decision threshold was determined by weighing multiple metrics on the validation sets and reporting results on the testing set. Additionally, the strengths and limitations of the different models were comprehensively analyzed by stratifying gender, mode of birth, and age subgroups, as well as by varying the number of predictor variables. Furthermore, methods such as Shapley additive explanations (SHAP) and purity of node partition in Random Forest were employed to assess feature importance, along with exploring model stability through alterations in the number of features. In this study, 7131 children aged 2-8 were analyzed, with 524 (7.35%) diagnosed with AR, with an onset age ranging from 2 to 8 years. Optimal parameters were refined using the validation set, and a rigorous process of 100 random divisions and repeated training ensured robust evaluation of the models on the testing set. The model construction involved incorporating fourteen variables, including the history of allergy-related diseases during the child's first year, familial genetic factors, and early-life indoor environmental factors. The performance of LR, SVM, RF, and XGBoost on the unstratified data test set was 0.715 (standard deviation = 0.023), 0.723 (0.022), 0.747 (0.015), and 0.733 (0.019), respectively; the performance of each model was stable on the stratified data, and the RF performance was significantly better than that of LR (paired samples t-test: p < 0.001). Different techniques for evaluating the importance of features showed that the top5 variables were father or mother with AR, having older siblings, history of food allergy and father's educational level. Utilizing strategies like stratification and adjusting the number of features, this study constructed a random forest model that outperforms traditional logistic regression. Specifically designed to detect the occurrence of allergic rhinitis (AR) in children aged 2-8, the model incorporates parental allergic history and early life environmental factors. The selection of the optimal cut-off value was determined through a comprehensive evaluation strategy. Additionally, we identified the top 5 crucial features that greatly influence the model's performance. This study serves as a valuable reference for implementing machine learning-based AR prediction in pediatric populations.

Predicting BRCA mutation and stratifying targeted therapy response using multimodal learning: a multicenter study.

BACKGROUND: The status of BRCA1/2 genes plays a crucial role in the treatment decision-making process for multiple cancer types. However, due to high costs and limited resources, a demand for BRCA1/2 genetic testing among patients is currently unmet. Notably, not all patients with BRCA1/2 mutations achieve favorable outcomes with poly (ADP-ribose) polymerase inhibitors (PARPi), indicating the necessity for risk stratification. In this study, we aimed to develop and validate a multimodal model for predicting BRCA1/2 gene status and prognosis with PARPi treatment. METHODS: We included 1695 slides from 1417 patients with ovarian, breast, prostate, and pancreatic cancers across three independent cohorts. Using a self-attention mechanism, we constructed a multi-instance attention model (MIAM) to detect BRCA1/2 gene status from hematoxylin and eosin (H&E) pathological images. We further combined tissue features from the MIAM model, cell features, and clinical factors (the MIAM-C model) to predict BRCA1/2 mutations and progression-free survival (PFS) with PARPi therapy. Model performance was evaluated using area under the curve (AUC) and Kaplan-Meier analysis. Morphological features contributing to MIAM-C were analyzed for interpretability. RESULTS: Across the four cancer types, MIAM-C outperformed the deep learning-based MIAM in identifying the BRCA1/2 genotype. Interpretability analysis revealed that high-attention regions included high-grade tumors and lymphocytic infiltration, which correlated with BRCA1/2 mutations. Notably, high lymphocyte ratios appeared characteristic of BRCA1/2 mutations. Furthermore, MIAM-C predicted PARPi therapy response (log-rank p < 0.05) and served as an independent prognostic factor for patients with BRCA1/2-mutant ovarian cancer (p < 0.05, hazard ratio:0.4, 95% confidence interval: 0.16-0.99). CONCLUSIONS: The MIAM-C model accurately detected BRCA1/2 gene status and effectively stratified prognosis for patients with BRCA1/2 mutations.

Increasing the explainability and success in classification: many-objective classification rule mining based on chaos integrated SPEA2.

Classification rule mining represents a significant field of machine learning, facilitating informed decision-making through the extraction of meaningful rules from complex data. Many classification methods cannot simultaneously optimize both explainability and different performance metrics at the same time. Metaheuristic optimization-based solutions, inspired by natural phenomena, offer a potential paradigm shift in this field, enabling the development of interpretable and scalable classifiers. In contrast to classical methods, such rule extraction-based solutions are capable of classification by taking multiple purposes into consideration simultaneously. To the best of our knowledge, although there are limited studies on metaheuristic based classification, there is not any method that optimize more than three objectives while increasing the explainability and interpretability for classification task. In this study, data sets are treated as the search space and metaheuristics as the many-objective rule discovery strategy and study proposes a metaheuristic many-objective optimization-based rule extraction approach for the first time in the literature. Chaos theory is also integrated to the optimization method for performance increment and the proposed chaotic rule-based SPEA2 algorithm enables the simultaneous optimization of four different success metrics and automatic rule extraction. Another distinctive feature of the proposed algorithm is that, in contrast to classical random search methods, it can mitigate issues such as correlation and poor uniformity between candidate solutions through the use of a chaotic random search mechanism in the exploration and exploitation phases. The efficacy of the proposed method is evaluated using three distinct data sets, and its performance is demonstrated in comparison with other classical machine learning results.

Enhancing the Interpretability of Malaria and Typhoid Diagnosis with Explainable AI and Large Language Models.

Malaria and Typhoid fever are prevalent diseases in tropical regions, and both are exacerbated by unclear protocols, drug resistance, and environmental factors. Prompt and accurate diagnosis is crucial to improve accessibility and reduce mortality rates. Traditional diagnosis methods cannot effectively capture the complexities of these diseases due to the presence of similar symptoms. Although machine learning (ML) models offer accurate predictions, they operate as "black boxes" with non-interpretable decision-making processes, making it challenging for healthcare providers to comprehend how the conclusions are reached. This study employs explainable AI (XAI) models such as Local Interpretable Model-agnostic Explanations (LIME), and Large Language Models (LLMs) like GPT to clarify diagnostic results for healthcare workers, building trust and transparency in medical diagnostics by describing which symptoms had the greatest impact on the model's decisions and providing clear, understandable explanations. The models were implemented on Google Colab and Visual Studio Code because of their rich libraries and extensions. Results showed that the Random Forest model outperformed the other tested models; in addition, important features were identified with the LIME plots while ChatGPT 3.5 had a comparative advantage over other LLMs. The study integrates RF, LIME, and GPT in building a mobile app to enhance the interpretability and transparency in malaria and typhoid diagnosis system. Despite its promising results, the system's performance is constrained by the quality of the dataset. Additionally, while LIME and GPT improve transparency, they may introduce complexities in real-time deployment due to computational demands and the need for internet service to maintain relevance and accuracy. The findings suggest that AI-driven diagnostic systems can significantly enhance healthcare delivery in environments with limited resources, and future works can explore the applicability of this framework to other medical conditions and datasets.

Metabolomics Biomarker Discovery to Optimize Hepatocellular Carcinoma Diagnosis: Methodology Integrating AutoML and Explainable Artificial Intelligence.

Background: This study aims to assess the efficacy of combining automated machine learning (AutoML) and explainable artificial intelligence (XAI) in identifying metabolomic biomarkers that can differentiate between hepatocellular carcinoma (HCC) and liver cirrhosis in patients with hepatitis C virus (HCV) infection. Methods: We investigated publicly accessible data encompassing HCC patients and cirrhotic controls. The TPOT tool, which is an AutoML tool, was used to optimize the preparation of features and data, as well as to select the most suitable machine learning model. The TreeSHAP approach, which is a type of XAI, was used to interpret the model by assessing each metabolite's individual contribution to the categorization process. Results: TPOT had superior performance in distinguishing between HCC and cirrhosis compared to other AutoML approaches AutoSKlearn and H2O AutoML, in addition to traditional machine learning models such as random forest, support vector machine, and k-nearest neighbor. The TPOT technique attained an AUC value of 0.81, showcasing superior accuracy, sensitivity, and specificity in comparison to the other models. Key metabolites, including L-valine, glycine, and DL-isoleucine, were identified as essential by TPOT and subsequently verified by TreeSHAP analysis. TreeSHAP provided a comprehensive explanation of the contribution of these metabolites to the model's predictions, thereby increasing the interpretability and dependability of the results. This thorough assessment highlights the strength and reliability of the AutoML framework in the development of clinical biomarkers. Conclusions: This study shows that AutoML and XAI can be used together to create metabolomic biomarkers that are specific to HCC. The exceptional performance of TPOT in comparison to traditional models highlights its capacity to identify biomarkers. Furthermore, TreeSHAP boosted model transparency by highlighting the relevance of certain metabolites. This comprehensive method has the potential to enhance the identification of biomarkers and generate precise, easily understandable, AI-driven solutions for diagnosing HCC.

Olfactory Diagnosis Model for Lung Health Evaluation Based on Pyramid Pooling and SHAP-Based Dual Encoders.

This study introduces a novel deep learning framework for lung health evaluation using exhaled gas. The framework synergistically integrates pyramid pooling and a dual-encoder network, leveraging SHapley Additive exPlanations (SHAP) derived feature importance to enhance its predictive capability. The framework is specifically designed to effectively distinguish between smokers, individuals with chronic obstructive pulmonary disease (COPD), and control subjects. The pyramid pooling structure aggregates multilevel global information by pooling features at four scales. SHAP assesses feature importance from the eight sensors. Two encoder architectures handle different feature sets based on their importance, optimizing performance. Besides, the model's robustness is enhanced using the sliding window technique and white noise augmentation on the original data. In 5-fold cross-validation, the model achieved an average accuracy of 96.40%, surpassing that of a single encoder pyramid pooling model by 10.77%. Further optimization of filters in the transformer convolutional layer and pooling size in the pyramid module increased the accuracy to 98.46%. This study offers an efficient tool for identifying the effects of smoking and COPD, as well as a novel approach to utilizing deep learning technology to address complex biomedical issues.

Interpretable machine learning models for the prediction of all-cause mortality and time to death in hemodialysis patients.

INTRODUCTION: The elevated mortality and hospitalization rates among hemodialysis (HD) patients underscore the necessity for the development of accurate predictive tools. This study developed two models for predicting all-cause mortality and time to death-one using a comprehensive database and another simpler model based on demographic and clinical data without laboratory tests. METHOD: A retrospective cohort study was conducted from January 2017 to June 2023. Two models were created: Model A with 85 variables and Model B with 22 variables. We assessed the models using random forest (RF), support vector machine, and logistic regression, comparing their performance via the AU-ROC. The RF regression model was used to predict time to death. To identify the most relevant factors for prediction, the Shapley value method was used. RESULTS: Among 359 HD patients, the RF model provided the most reliable prediction. The optimized Model A showed an AU-ROC of 0.86 ± 0.07, a sensitivity of 0.86, and a specificity of 0.75 for predicting all-cause mortality. It also had an R2 of 0.59 for predicting time to death. The optimized Model B had an AU-ROC of 0.80 ± 0.06, a sensitivity of 0.81, and a specificity of 0.70 for predicting all-cause mortality. In addition, it had an R2 of 0.81 for predicting time to death. CONCLUSION: Two new interpretable clinical tools have been proposed to predict all-cause mortality and time to death in HD patients using machine learning models. The minimal and readily accessible data on which Model B is based makes it a valuable tool for integrating into clinical decision-making processes.

PreAlgPro: Prediction of allergenic proteins with pre-trained protein language model and efficient neutral network.

Allergy is a prevalent phenomenon, involving allergens such as nuts and milk. Avoiding exposure to allergens is the most effective preventive measure against allergic reactions. However, current homology-based methods for identifying allergenic proteins encounter challenges when dealing with non-homologous data. Traditional machine learning approaches rely on manually extracted features, which lack important protein functional characteristics, including evolutionary information. Consequently, there is still considerable room for improvement in existing methods. In this study, we present PreAlgPro, a method for identifying allergenic proteins based on pre-trained protein language models and deep learning techniques. Specifically, we employed the ProtT5 model to extract protein embedding features, replacing the manual feature extraction step. Furthermore, we devised an Attention-CNN neural network architecture to identify potential features that contribute to the classification of allergenic proteins. The performance of our model was evaluated on four independent test sets, and the experimental results demonstrate that PreAlgPro surpasses existing state-of-the-art methods. Additionally, we collected allergenic protein samples to validate the robustness of the model and conducted an analysis of model interpretability.

Self-adaptive selection graph pooling based fault diagnosis method under few samples and noisy environment.

Neural network (NN)-based methods are extensively used for intelligent fault diagnosis in industrial systems. Nevertheless, due to the limited availability of faulty samples and the presence of noise interference, most existing NN-based methods perform limited diagnosis performance. In response to these challenges, a self-adaptive selection graph pooling method is proposed. Firstly, graph encoders with sharing parameters are designed to extract local structure-feature information (SFI) of multiple sensor-wise sub-graphs. Then, the temporal continuity of the SFI is maintained through time-by-time concatenation, resulting in a global sensor graph and reducing the dependency on data volume from the perspective of adding prior knowledge. Subsequently, leveraging a self-adaptive node selection mechanism, the noise interference of redundant and noisy sensor-wise nodes in the graph is alleviated, allowing the networks to concentrate on the fault-attention nodes. Finally, the local max pooling and global mean pooling of the node-selection graph are incorporated in the readout module to get the multi-scale graph features, which serve as input to a multi-layer perceptron for fault diagnosis. Two experimental studies involving different mechanical and electrical systems demonstrate that the proposed method not only achieves superior diagnosis performance with limited data, but also maintains strong anti-interference ability in noisy environments. Additionally, it exhibits good interpretability through the proposed self-adaptive node selection mechanism and visualization methods.

Interpretability and clinical utility of the strength and stressors in parenting questionnaire.

This study aimed to enhance the interpretability and clinical utility of the strength and stressors in parenting (SSF) questionnaire, a parent-reported questionnaire designed to assess strength, stress and associated risks of mental ill-health in parents of children with developmental disabilities. Responses to the SSF and a demographic questionnaire were collected from 576 parents of children with (n = 203) and without (n = 373) developmental disabilities. To enhance the interpretability of the SSF, a subset of 129 parents were invited to complete an additional questionnaire consisting of three free-text questions regarding recent help-seeking behavior, experiences of mental ill-health and experiences of parenthood. Parents' responses to the free-text questions were then categorized as indicative of higher or lower degrees of stress and compared to their SSF score distribution to derive empirical cut-offs for strength, stress and risk of mental ill-health as measured by the SSF. The credibility of these cut-offs was evaluated by comparing the cut-offs with SSF scores collected from the other 447 parents. Finally, SSF scores from parents of children without developmental disabilities (n = 373) were used to generate percentile values for the SSF to enable a standardized interpretation of SSF scores. To increase the utility of the SSF, we examined a recurring pattern of missing answers to items 23 and 33-38, noted in previous studies of the SSF and repeated in the present study. These items were excluded from further analysis since our examination revealed that they were not missing at random but rather constituted real differences in parental experiences, such as receiving a healthcare allowance, or caring for more than one child. The proposed empirical cut-offs performed well in discriminating between the two groups and yielded a specificity of 77-89% and a sensitivity of 68-76% for the strength, stress and risk of mental ill-health subscales of the SSF. This study also presents a conversion chart associating each SSF score with a corresponding percentile value. We propose modifications to the SSF, whereby items 23 and 33-38 are excluded, which will enable a more reliable assessment of parental experiences. This will, together with the empirical cut-offs and percentile values, enhance the interpretability and clinical utility of the SSF.