Search Results for « Algorithm-driven prediction »

The mental health challenges of college students have drawn extensive attention, and the accurate prediction of psychological resilience is the key to targeted mental health intervention. Traditional subjective scale assessment has technical defects such as low timeliness and poor predictive accuracy, while existing machine learning-based mental health prediction models suffer from single data dimension, low training efficiency and poor model interpretability. To address these technical problems in computer-aided mental health assessment, this study develops a machine learning algorithm-driven predictive model for college students' psychological resilience, and achieves three key computer technology advancements in the research: First, an optimized multi-source heterogeneous data preprocessing framework is proposed, which integrates LASSO regression feature dimensionality reduction and TF-IDF sentiment polarity analysis to realize efficient cleaning and feature extraction of mixed structured/unstructured psychological data, and the internal consistency of the dataset (Cronbach's α) is improved from 0.72 to 0.89. Second, the XGBoost ensemble learning algorithm is optimized by combining Bayesian optimization for hyperparameter tuning, which solves the low efficiency problem of traditional grid search; the model's R² score on the validation set is increased by 12.7%, and the training time is reduced to 35% of the grid search method. Third, the SHAP (SHapley Additive exPlanations) value interpretation method is introduced into the psychological resilience prediction model, which makes up for the defect of poor interpretability of complex ensemble learning models and quantifies the contribution of core features to prediction results. This study elaborates on the technical implementation details including data preprocessing procedures, algorithm parameter setting, model training strategies and performance evaluation metrics. Experimental results show that the proposed model achieves a predictive correlation coefficient of 0.84 and an AUC (Area Under ROC Curve) of 0.89 on the test set, which is significantly superior to traditional linear regression and mainstream machine learning algorithms (SVM, Random Forest) (p<0.01). Case studies further verify the technical robustness and practical applicability of the model in complex scenarios. The research results not only provide objective and quantifiable technical support for university mental health services, but also optimize the application framework of machine learning algorithms in the field of psychological assessment, and provide a feasible technical solution for the integration of multi-source heterogeneous data in mental health research.

Background: Tacrolimus (TAC) dosing presents a persistent challenge in postoperative care owing to its narrow therapeutic window and high inter‑patient variability, which often leads to suboptimal exposure with increasing risks of nephrotoxicity or graft rejection. Algorithm‑based personalized dosing strategies offer a promising approach to support clinical decision and improve long‑term outcomes. Methods: Unlike approaches relying on a wide range of variables and local clinical scopes, this study proposed a novel and versatile algorithm-driven strategy to predict TAC doses. A hybrid optimization method was first employed to identify a minimal set of key clinical factors. These factors were then used to construct a cascaded deep forest model capable of predicting both follow‑up and initial TAC doses in adult kidney transplant recipients. Results: When validated on 615 patients using leave-one-subject-out cross-validation, it achieved predictions within ±20% of actual values, with an accuracy of 89.8% for follow-up doses and 83.2% for initial doses. Independent external validation confirmed its robustness. A Shapley additive explanation analysis revealed significant correlations between input features and predictive doses. To support real-time clinical use, an open‑access web platform was provided (http://www.jcu-qiulab.com/tacp/). Conclusion: This approach offers a practical, effective, and algorithm-driven pipeline for automated drug dose analysis and prediction in clinical practice.

As an important energy storage device, lithium-ion batteries (LIBs) have been widely used in various fields due to their remarkable advantages. The high level of precision in estimating the battery’s state of health greatly enhances the safety and dependability of the application process. In contrast to traditional model-based prediction methods that are complex and have limited accuracy, data-driven prediction methods, which are considered mainstream, rely on direct data analysis and offer higher accuracy. Therefore, this paper reviews how to use the latest data-driven algorithms to predict the SOH of LIBs, and proposes a general prediction process, including the acquisition of datasets for the charging and discharging process of LIBs, the processing of data and features, and the selection of algorithms. The advantages and limitations of various processing methods and cutting-edge data-driven algorithms are summarized and compared, and methods with potential applications are proposed. Effort was also made to point out their application methods and application scenarios, providing guidance for researchers in this area.

This paper presents the capability of a decision tree algorithm to realize a data-driven resistance spot welding (RSW) weldability prediction. Although RSW provides commendable advantages, such as low cost and high speed/high volume operations, the RSW processes are often inconsistent and these significant inconsistencies are a well-known reliability issue. RSW process and data challenges including inconsistency often hinder the utilization of the data-driven weldability prediction. In this paper, we apply a decision tree algorithm on the RSW dataset collected from an automotive OEM to plot regression trees and to extract decision rules for the weld nugget width prediction. With three RSW test datasets, we conclude that the decision trees help in predicting the nugget width and in determining the impact of design and process parameters to the nugget width response variable.

AIMS: To compute the financial and mortality impact of InSight, an algorithm-driven biomarker, which forecasts the onset of sepsis with minimal use of electronic health record data. METHODS: This study compares InSight with existing sepsis screening tools and computes the differential life and cost savings associated with its use in the inpatient setting. To do so, mortality reduction is obtained from an increase in the number of sepsis cases correctly identified by InSight. Early sepsis detection by InSight is also associated with a reduction in length-of-stay, from which cost savings are directly computed. RESULTS: InSight identifies more true positive cases of severe sepsis, with fewer false alarms, than comparable methods. For an individual ICU with 50 beds, for example, it is determined that InSight annually saves 75 additional lives and reduces sepsis-related costs by $560,000. LIMITATIONS: InSight performance results are derived from analysis of a single-center cohort. Mortality reduction results rely on a simplified use case, which fixes prediction times at 0, 1, and 2 h before sepsis onset, likely leading to under-estimates of lives saved. The corresponding cost reduction numbers are based on national averages for daily patient length-of-stay cost. CONCLUSIONS: InSight has the potential to reduce sepsis-related deaths and to lead to substantial cost savings for healthcare facilities.

Recently, high-precision trajectory prediction of ballistic missiles in the boost phase has become a research hotspot. This paper proposes a trajectory prediction algorithm driven by data and knowledge (DKTP) to solve this problem. Firstly, the complex dynamics characteristics of ballistic missile in the boost phase are analyzed in detail. Secondly, combining the missile dynamics model with the target gravity turning model, a knowledge-driven target three-dimensional turning (T3) model is derived. Then, the BP neural network is used to train the boost phase trajectory database in typical scenarios to obtain a data-driven state parameter mapping (SPM) model. On this basis, an online trajectory prediction framework driven by data and knowledge is established. Based on the SPM model, the three-dimensional turning coefficients of the target are predicted by using the current state of the target, and the state of the target at the next moment is obtained by combining the T3 model. Finally, simulation verification is carried out under various conditions. The simulation results show that the DKTP algorithm combines the advantages of data-driven and knowledge-driven, improves the interpretability of the algorithm, reduces the uncertainty, which can achieve high-precision trajectory prediction of ballistic missile in the boost phase.

This work represents the first step toward a DDDAS for Wildland Fire Prediction where our main efforts are oriented to take advantage of the computing power provided by High Performance Computing systems to, on the one hand, propose computational data driven steering strategies to overcome input data uncertainty and, on the other hand, to reduce the execution time of the whole prediction process in order to be reliable during real-time crisis. In particular, this work is focused on the description of a Dynamic Data Driven Genetic Algorithm used as steering strategy to automatic adjust certain input data values of forest fire simulators taking into account the underlying propagation model and the real fire behavior. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Li-ion battery state of health (SOH) is a key parameter for characterizing actual battery life. SOH cannot be measured directly. In order to further improve the accuracy of Li-ion battery SOH estimation, a combined model based on health feature parameters combined with EMD-ICA-GRU is proposed to predict the SOH of Li-ion batteries. The capacity regeneration phenomenon and data noise are decomposed by empirical mode decomposition (EMD), and then the SOH-related health indicators are deeply mined using incremental capacity analysis (ICA), and the peaks of IC curves and their corresponding voltages are extracted as the input of the model. Then, gated recurrent units (GRUs) are formed into a combined SOH estimation model by adaptive weighting factors. Finally, it is validated against the NASA lithium battery dataset. Experimental results show that the mean squared error (MSE) of the proposed combined model can reach about 0.3%, and it has stronger generalization and prediction accuracy than other algorithms driven by independent estimation data.

Abstract To address the substantial variability in immune checkpoint blockade (ICB) therapy effectiveness, we developed an innovative R package called integrated Machine Learning and Genetic Algorithm‐driven Multiomics analysis (iMLGAM), which establishes a comprehensive scoring system for predicting treatment outcomes through advanced multi‐omics data integration. Our research demonstrates that iMLGAM scores exhibit superior predictive performance across independent cohorts, with lower scores correlating significantly with enhanced therapeutic responses and outperforming existing clinical biomarkers. Detailed analysis revealed that tumors with low iMLGAM scores display distinctive immune microenvironment characteristics, including increased immune cell infiltration and amplified antitumor immune responses. Critically, through clustered regularly interspaced short palindromic repeats screening, we identified Centrosomal Protein 55 (CEP55) as a key molecule modulating tumor immune evasion, mechanistically confirming its role in regulating T cell‐mediated antitumor immune responses. These findings not only validate iMLGAM as a powerful prognostic tool but also propose CEP55 as a promising therapeutic target, offering novel strategies to enhance ICB treatment efficacy. The iMLGAM package is freely available on GitHub ( https://github.com/Yelab1994/iMLGAM ), providing researchers with an innovative approach to personalized cancer immunotherapy prediction.

Concrete is one of the most popular materials for building all types of structures, and it has a wide range of applications in the construction industry. Cement production and use have a significant environmental impact due to the emission of different gases. The use of fly ash concrete (FAC) is crucial in eliminating this defect. However, varied features of cementitious composites exist, and understanding their mechanical characteristics is critical for safety. On the other hand, for forecasting the mechanical characteristics of concrete, machine learning approaches are extensively employed algorithms. The goal of this work is to compare ensemble deep neural network models, i.e., the super learner algorithm, simple averaging, weighted averaging, integrated stacking, as well as separate stacking ensemble models, and super learner models, in order to develop an accurate approach for estimating the compressive strength of FAC and reducing the high variance of the predictive models. Separate stacking with the random forest meta-learner received the most accurate predictions (97.6%) with the highest coefficient of determination and the lowest mean square error and variance.