Resting-state functional connectome-based prediction of valence bias.

Providence Bracing in Idiopathic Scoliosis: A Scoping Review.

This scoping review assesses machine learning (ML)-based prediction models for autism spectrum disorder (ASD) in early childhood, with the aim of providing a technical and conceptual foundation for improving early ASD detection. Relevant studies on ML-driven ASD prediction models were systematically retrieved from eight databases: PubMed, Embase, Web of Science Core Collection, Cochrane Library, China National Knowledge Infrastructure (CNKI), China Biomedical Database (CBM), Wanfang Data Knowledge Service Platform (WF), and VIP Chinese Science and Technology Journal Database. The scoping review methodology was strictly followed for data extraction and analysis. A total of 16 studies focusing on the application of diverse machine learning algorithms for ASD identification and prediction were included. Among these, 4 studies (25%) employed multiple algorithms for predictive modeling. The most frequently utilized algorithms were tree-based methods (7 studies, 44%), neural networks (NNs) (7 studies, 44%), support vector machines (SVMs) (5 studies, 31%), and regularized logistic regression (3 studies, 19%). Twelve studies (75%) reported Area Under the Curve (AUC) values, all exceeding the 0.7 threshold. Notably, 7 studies (44%) achieved excellent predictive performance with AUC values surpassing 0.9. ML-based models hold substantial promise for the early identification of ASD, which is critical for improving patient outcomes. Future research should focus on standardizing ML model frameworks, refining theoretical underpinnings to enhance practical applicability, and promoting clinical implementation following rigorous validation. These efforts will further enhance the accuracy and utility of such predictive models.

Prediction of atypical health trajectories may enable early intervention. We systematically reviewed the existing literature on models for predicting longitudinal depression and/or anxiety trajectories. MEDLINE, Embase and APA PsycINFO were searched (from inception to 31-Jan-2025). We included population-based studies of children and adults (aged 3-65years). Risk of bias was assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST-AI) tool. Seven of the nine included studies were in adult populations with a diagnosis of depression or anxiety at baseline; two focused on child and adolescent populations. Only one study included anxiety trajectories. Identified trajectories typically comprised three to four groups including: chronic/persistent-high, stable-low, increasing/worsening, and improved/remitted groups. Various supervised predictive modelling methods were used. The number of final predictors included in models ranged from three to 152. Family and own/personal psychiatric history were the most common predictors but were not always important for model performance. Models including more predictors did not always perform better. Overall risk of bias was high in all studies. No studies were externally validated and no studies assessed the clinical utility of models. This review highlights a need for robust, validated models that can forecast future risk of persistent or worsening anxiety and depression, especially in young people where early intervention is possible.

A long short-term memory networks-informer based prediction model in coal management of thermal units

Heave motion prediction with variational bayesian-based strong tracking Kalman filter

• Synthetic data from generative AI preserves privacy in healthcare modeling. • Generative AI adapts dynamically, surpassing static traditional CEA models. • Enhanced scenario simulations by generative AI aid robust decision-making. • Generative AI integrates real-world evidence, refining predictive accuracy. • Non-linear modeling in AI captures complex healthcare cost-outcome relations. Healthcare economic evaluation increasingly relies on predictive modeling to inform resource allocation decisions. Traditional cost-effectiveness analysis (CEA) methodologies face significant challenges when processing complex, heterogeneous healthcare datasets and accommodating dynamic system variables. This review examines how generative artificial intelligence technologies may transform predictive modeling frameworks in healthcare economics, specifically focusing on potential improvements in accuracy, adaptability, and efficiency in cost-effectiveness analyses. A literature search was conducted across PubMed, Scopus, Web of Science, and IEEE Xplore between October 2024 and January 2025, examining publications from 2018-2024. Critically, we identified a near absence of empirical studies that directly apply and validate generative AI technologies within formal health economic modeling or health technology assessment contexts. Most identified literature addresses general AI/ML applications in healthcare or synthetic data generation in adjacent domains, rather than demonstrating validated use in cost-effectiveness analysis. Generative AI demonstrates promising theoretical capabilities in handling non-linear healthcare relationships, generating privacy-preserving synthetic datasets, and enabling dynamic scenario exploration based on performance in related fields. However, direct empirical evidence comparing generative AI to traditional CEA approaches in real-world health technology assessment remains virtually non-existent. Potential advantages include automated model support, enhanced integration of real-world evidence, and improved handling of missing data scenarios. Technologies such as Generative Adversarial Networks and Variational Autoencoders show early-stage promise in addressing traditional modeling limitations in adjacent applications. Generative AI represents a conceptually significant potential advancement in healthcare economic modeling. However, claims presented are predominantly forward-looking and conceptual rather than empirically validated. Implementation challenges including model interpretability, regulatory frameworks, validation requirements, and ethical considerations require substantial empirical research before successful integration into healthcare decision-making processes.

Effect of hammer mill particle size on the performance of NIR prediction models for whole-plant corn constituents

Mild autonomous cortisol secretion (MACS) is present in approximately 20% to 50% of adrenal incidentalomas. These patients do not exhibit the clinical manifestations of overt Cushing's syndrome, and differentiation from nonfunctional adrenal incidentalomas (NFAI) is typically made after a low-dose dexamethasone suppression test. The objective of this study was to develop predictive models to distinguish MACS from NFAI using clinical and radiological parameters. This retrospective study included patients evaluated in an adrenal incidentaloma clinic between February 2022 and August 2024 who were diagnosed with either NFAI or MACS. Demographic characteristics, medical and medication history, and radiological features were collected. Patients were randomly divided into training and test cohorts in a 3:1 ratio. Predictive models for MACS were developed using Least Absolute Shrinkage and Selection Operator (LASSO) regression and random forest (RF) algorithms. A total of 397 patients were included, with 297 allocated to the training set and the remaining 100 to the test set. The mean age was 62.3 years, and 55% of participants (n = 220) were women. MACS was present in 34% of the study population (n = 136). The most influential predictive factors of MACS were BMI, Posterior Adiposity Index, and the number of antihypertensive medications. The LASSO and RF models achieved discrimination with area under the curve values of 0.686 and 0.736, respectively. At Youden Index thresholds balancing sensitivity and selectivity, the LASSO model had 58.8% sensitivity, 75.8% specificity, and 70% accuracy, whereas the RF model had 64.7% sensitivity, 75.8% specificity, and 72% accuracy. Predictive models incorporating clinical and radiological characteristics offer a promising approach for distinguishing MACS from NFAI in patients with adrenal incidentalomas.

Traffic prediction based on real-world traffic data is a crucial task in Intelligent Transportation Systems (ITS). However, the issue of missing observations due to real-world disturbances undermines the robustness and accuracy of traffic prediction. This problem necessitates the development of a prediction model that integrates the imputation mechanism to be compatible with missing observations. This paper introduces ATTST, a self-imputation-assisted prediction model specifically designed to address the challenge of missing observations in the traffic prediction task. Unlike traditional approaches utilizing an additional supervised imputation model before prediction, the imputation unit in our model does not need the extra label for the missing observations. Our model employs a self-imputation unit to impute the missing observations by partially masking the observed data as the ground true labels. Thus, the self-imputation unit along with an encoder–decoder architecture and a graph evolving unit together directly predict future traffic data with multi-level missing observations. The effectiveness of ATTST is validated using several real-world traffic datasets, including speed and flow data, across various multi-step prediction scenarios with diverse missing observations. These validations demonstrate the model’s robustness and practical applicability in real-world traffic prediction tasks. The results show that ATTST can reliably predict traffic conditions even with incomplete data, making it a valuable tool for traffic management and planning. • The problem of imputation and prediction for traffic speed and flow data with missing observations is formulated within an end-to-end framework. • The proposed AttSt model addresses this problem by incorporating a self-imputation mechanism to effectively handle missing observations. • The model employs a graph network to capture and process spatiotemporal correlations within the traffic data. • Comprehensive evaluations on four real-world traffic speed and flow datasets validate the effectiveness of the proposed approach.

Objective:To analyze the factors influencing the low fall awareness in patients with Meniere's disease, construct a prediction model, and validate it, providing a reference for clinical medical staff to identify patients with Meniere's disease who have low fall awareness. Methods:A total of 341 patients diagnosed with Meniere's disease who received treatment in our hospital's Department of Otolaryngology, Head and Neck Surgery from June 2020 to June 2024 were selected for the study and randomly assigned into two groups: a training set consisting of 238 individuals and a validation set comprising 103 individuals, following a 7︰3 ratio. Patients were assessed using a general information questionnaire, the Social Frailty Screening Tool, the Tilburg Frailty Indicator, and the Fall Awareness Scale. Multivariate logistic regression analysis was employed to identify the factors influencing low fall awareness patients with Meniere's disease. RStudio was used to construct a nomogram for predicting low fall awareness in these patients. The model's discrimination, calibration, and clinical net benefit were validated using receiver operating characteristic（ROC） curves, calibration plots, and Decision Curve Analysis（DCA）. Results:The results of the multivariate logistic regression analysis indicated that age, history of falls in the past year, severity of vertigo symptoms, social frailty score, and Tilburg Frailty Indicator score were independent risk factors for low fall awareness patients with Meniere's disease. The Hosmer-Lemeshow χ² test showed χ²=8.863, P=0.354, indicating good calibration of the predictive model. The area under the ROC curve for the training and validation sets was 0.868 and 0.878, respectively, demonstrating good discrimination of the model. The DCA decision curve indicated that the clinical utility of the model was satisfactory. Conclusion:The constructed nomogram performed well and can assist clinical medical staff in quickly and effectively screening patients with Meniere's disease who are at risk of low fall awareness.

Urban traffic network congestion propagation prediction model: A case of non-recurrent congestion

Thermomechanical coupling mechanism and prediction model of flexible composite pipes under the combined action of internal and external temperatures

Tryptophan metabolism in neonicotinoids exposure-induced diabetes: Emerging insights and predictive implications.

Predictive modeling and multi-objective optimization of CI engine performance, combustion and emissions parameters with water-emulsified diesel using SVR and SHO

A predictive model based on the systemic immune-inflammation index combined with other hematologic indices: A dynamic web-based nomogram for early detection of massive acute pulmonary embolism.

Hydraulic conductivity characteristics of woven geotextiles used in geotextile sand containers: experimental investigation and a predictive model

The MetALD-ALD Prediction Index: A Phosphatidylethanol-Driven Biomarker Panel for Identifying Individuals With Steatotic Liver Disease and Excessive Alcohol Use.

A data-driven model for ship trajectory prediction with integrated hydrometeorological conditions: A case study in complex waters

Management of high anal fistula (HAF) remains challenging due to risks of recurrence and anal incontinence. This study was undertaken to assess the effectiveness of the cutting of the intersphincteric space (COIS) in HAF and to develop a predictive model for postoperative functional decline. A prospective single-center cohort study of 146 consecutive patients with HAF who underwent COIS at the Nanjing Hospital of Chinese Medicine Affiliated to Nanjing University of Chinese Medicine from January 2021 to July 2024, with an 18-month follow-up was conducted. COIS is a sphincter-preserving technique targeting intersphincteric space dissection to maintain anal sphincter integrity. The primary outcome was the change in anal functional status, measured by the Wexner incontinence score, at 3 months postoperatively. Secondary outcomes included recurrence rate and identification of independent predictors of functional decline via multivariate logistic regression. Significant anal functional decline occurred in 11.6% (17 of 146) of patients, with a recurrence rate of 4.8%. Sex, age, and neutrophil ratio were independent predictors of functional decline (area under the curve 0.739, 95% CI 0.645 to 0.833). A nomogram incorporating these biomarkers demonstrated excellent calibration and clinical use. Single-center design may limit generalizability; external validation in multicenter cohorts is warranted. COIS represents a functionally optimized approach for HAF, balancing radical cure and sphincter integrity. The predictive model integrating systemic biomarkers advances personalized risk assessment, guiding surgical decision-making for patients prioritizing continence. These findings underscore the importance of metabolic and inflammatory profiling in prognostication, shifting the paradigm from anatomy-driven to function-preserving strategies.