Set Of Algorithms Research Articles

Machine learning prediction of stalk lignin content using Fourier transform infrared spectroscopy in large scale maize germplasm

Lignin has been recognized as a major factor contributing to lignocellulosic recalcitrance in biofuel production and attracted attentions as a high-value product in the biorefinery field. As the traditional wet chemical methods for detecting lignin content are labor-intensive, time-consuming and environment-toxic, it is an urgent need to develop high-throughput and environment-friendly techniques for large-scale crop germplasms screening. In this study, we conducted a Fourier transform infrared (FTIR) assay on 150 maize germplasms with a diverse lignin composition to build predictive models for lignin content in maize stalk. Principal component analysis (PCA) was applied to the FTIR spectra for use as model inputs. Classification and advanced gradient boosting machine (GBM) algorithms demonstrated higher predictive accuracy (0.82–0.96) compared to traditional linear and regularization algorithms (0.03–0.04) in the training set. Notably, two optimal models, built using the extreme gradient boosting (XGBoost) and light gradient boosting machine (LightGBM) algorithms, achieved R2 values of over 0.91 in the training set and over 0.82 in the test set. Overall, the combination of FTIR and machine learning (ML) algorithms offers a high-throughput and efficient method for predicting lignin content. This approach holds significant potential for genetic breeding and the effective utilization of maize in industrial production.

Diagnostic precision in thyroid-associated ophthalmopathy using multi-center radiomics with 99mTc-DTPA SPECT/CT

To explore the performance of 99mTc-diethylene triaminepentaacetic acid (DTPA) SPECT/CT texture analysis in evaluating the activity of thyroid-associated ophthalmopathy (TAO) . This retrospective study examined 115 TAO patients from a single institution as an internal cohort and 58 TAO patients from another institution as an external validation set. Patients in the internal cohort were randomly divided into training (n = 81) and internal validation sets (n = 34). Radiomics signatures were constructed with the minimal redundancy maximal relevance and least absolute shrinkage and selection operator algorithms in training set. Multivariate logistic regression analysis was used to develop a clinical model and a combined clinical–radiomics model. Diagnostic performance of models was evaluated using receiver operating characteristic curve analysis, calibration curves and decision curve analysis. Compared with CT and SPECT radiomics models, Rad-scoreSPECT/CT demonstrated the best performance with areas under the receiver operating characteristic curve of 0.94 and 0.91 in the training and test sets, respectively. The combined clinical-radiomics model exhibited significantly better performance in evaluating TAO activity. Our results demonstrate the validity of a multimodal radiomic model of 99mTc-DTPA-SPECT/CT to assess TAO activity. The combined clinical-radiomics model exhibited significantly better diagnostic performance than the clinical model.

Barriers and facilitators for implementation of AI algorithms for ECG analysis and triage in patients with chest pain

Abstract Introduction Numerous artificial intelligence (AI) algorithms have been developed to assist in clinical decision-making, with a focus on areas like electrocardiogram (ECG) analysis due to the signal complexity and the varying levels of expertise among healthcare professionals. However, ensuring acceptance of these algorithms by end-users is paramount for successful implementation. Purpose This study aimed to identify barriers and facilitators related to implementation of an AI algorithm for ECG analysis and triage in patients with chest pain. Methods A three-round modified electronic Delphi study was conducted among Dutch potential end-users of an ECG-AI algorithm, including physicians, nurses and ambulance professionals. During the first round, participants brainstormed on barriers and facilitators, which were subsequently mapped to the Consolidated Framework for Implementation Research (CFIR). In the following two rounds, participants reviewed the identified barriers and facilitators for relevance and ranked them according to their importance. Results Twenty-five out of the initial forty respondents completed all three rounds, resulting in a dropout rate of 38%. Four barriers and twelve facilitators were identified. The most important facilitator was "Faster recognition of subtle ECG abnormalities", while "Poor model performance" emerged as the most important barrier. The most frequently mentioned corresponding CFIR domains were the innovation and inner setting domains. Conclusion The identification of barriers and facilitators provides valuable insights into the acceptance and utilisation of ECG-AI algorithms by end-users. By considering the CFIR domains, including innovation and inner setting, this study offers a comprehensive understanding of the contextual factors that influence the successful implementation of AI algorithms in healthcare settings. Addressing these barriers and facilitators will be crucial to facilitating future implementation processes.Barriers and facilitators

Componentizing autonomous underwater vehicles by physical-running algorithms

Autonomous underwater vehicles (AUV) constitute a specific type of cyber-physical system that utilize electronic, mechanical, and software components. A component-based approach can address the development complexities of these systems through composable and reusable components and their integration, simplifying the development process and contributing to a more systematic, disciplined, and measurable engineering approach. In this article, we propose an architecture to design and describe the optimal performance of components for an AUV engineering process. The architecture involves a computing approach that carries out the automatic control of a testbed using genetic algorithms, where components undergo a ‘physical-running’ evaluation. The procedure, defined from a method engineering perspective, complements the proposed architecture by demonstrating its application. We conducted an experiment to determine the optimal operating modes of an AUV thruster with a flexible propeller using the proposed method. The results indicate that it is feasible to design and assess physical components directly using genetic algorithms in real-world settings, dispensing with the corresponding computational model and associated engineering stages for obtaining an optimized and tested operational scope. Furthermore, we have developed a cost-based model to illustrate that designing an AUV from a physical-running perspective encompasses extensive feasibility zones, where it proves to be more cost-effective than an approach based on simulation.

Numerical simulation on multi-well fracturing considering multiple thin layers in vertical direction

Multiwell fracturing is a key technology for developing shale gas and shale oil reservoirs. In this study, a multiple planar 3D (PL3D) fracture simulator that can capture multiple thin layers was developed to examine the propagation of multiple fractures during multicluster fracturing in multiple horizontal wells. The simulator considers multiple thin layers in the vertical direction. The results of the model are validated against the analytical solution of a single radial fracture and the implicit level set algorithm (ILSA). Using the simulator, a series of numerical simulations based on the field case are performed to investigate the fracture propagation mechanism of multiwell fracturing. The completion sequence, well placement pattern, well spacing, and cluster spacing are investigated to optimize the treatment parameters. The effective fracture area is used to quantitatively describe the stimulation effect. The adaptability of the completion sequence and well placement pattern is also analysed from the perspective of “frac hits”. The results show that the completion sequence has a critical influence on the stimulation effect and fracture geometry. From the perspective of avoiding “frac-hit” fractures, fracturing the low-stress layer can form an “artificial stress barrier”, which slightly protects the well from interference from other fractures. The staggered well pattern is better than the stacked well pattern. Compared with the stacked pattern, the staggered pattern can reduce the overlap area of fractures by 80 %, which greatly reduces the probability of “frac-hits”. With increasing well spacing from 200 m to 500 m, the fracture area increases by 25 %, and the degree of uneven stimulation between the two pay zones also increases by 6 %. Considering that a small well spacing is prone to “frac hits”, a large well spacing leads to an unstimulated area between two wells, and a 350 m well spacing is optimal. The effective fracture area decreases slightly with increasing perforation cluster spacing, but the fracture geometry becomes much more regular. The results can be helpful for the field design of multiwell fracturing.

Detection of hypertension from pharyngeal images using deep learning algorithm in primary care settings in Japan

BackgroundThe early detection of hypertension using simple visual images in a way that does not require physical interaction or additional devices may improve quality of care in the era of...

MDSA: A Dynamic and Greedy Approach to Solve the Minimum Dominating Set Problem

The graph theory is one of the fundamental structures in computer science used to model various scientific and engineering problems. Many problems within the graph theory are categorized as NP-hard and NP-complete. One such problem is the minimum dominating set (MDS) problem, which seeks to identify the minimum possible subsets in a graph such that every other node in the subset is directly connected to a node in this subset. Due to its inherent complexity, developing an efficient polynomial-time method to address the MDS problem remains a significant challenge in graph theory. This paper introduces a novel algorithm that utilizes a centrality measure known as the Malatya Centrality to effectively address the MDS problem. The proposed algorithm, called the Malatya Dominating Set Algorithm (MDSA), leverages centrality values to identify dominating sets within a graph. It extends the Malatya centrality by incorporating a second-level centrality measure, which enhances the identification of dominating nodes. Through a systematic and algorithmic approach, these centrality values are employed to pinpoint the elements of the dominating set. The MDSA uniquely integrates greedy and dynamic programming strategies. At each step, the algorithm selects the most optimal (or near-optimal) node based on the centrality values (greedy approach) while updating the neighboring nodes’ criteria to influence subsequent decisions (dynamic programming). The proposed algorithm demonstrates efficient performance, particularly in large-scale graphs, with time and space requirements scaling proportionally with the size of the graph and its average degree. Experimental results indicate that our algorithm outperforms existing methods, especially in terms of time complexity when applied to large datasets, showcasing its effectiveness in addressing the MDS problem.

Optimal estimation of SoC, SoH using machine learning models and design of control algorithm for active cell balancing in lithium ion batteries

Abstract Electric vehicles transform the automotive industry by replacing traditional vehicles powered by fossil fuels with less polluting and efficient vehicles. They are powered by rechargeable Li-ion batteries. While there are drawbacks associated with Li-ion battery technology, it's important to note that these challenges can be addressed or mitigated effectively. A battery management system ensures the tracking of all functions performed by the battery. An advanced battery management system should accurately estimate the battery's state of health and state of charge. This paper aims to develop machine learning models for the estimation of the state of health and state of charge and to implement cell balancing in a battery management system. A dataset of battery charging and discharging profiles was used to train various machine learning models for estimation. These methods are computationally less complex than conventional methods. In addition, a cell balancing algorithm is implemented to control the charging and discharging of individual cells in a battery pack and balance the state of charge of each cell. The machine learning solution is created using several machine learning models, including Gradient Boosting, Random Forest, Linear Regression, AdaBoost, and Multi-layer Perceptron. Among the models Gradient Boosting and Random Forest provide good MSE and R2 score. The use of machine learning algorithms for the assessment of state of health and charge estimation combined with the design of an efficient control algorithm for active cell balancing offers significant advancements in the battery management system to optimise the performance, reliability, and useful life of Li-ion batteries. The paper also presents a case study utilising a combination of deep learning-based SoC, SoH estimation algorithms in a simulated data set. A well-designed control algorithm for active cell balancing presents a holistic and effective approach to optimise the performance, reliability, and lifespan of Li-ion batteries.

Prediction of hemorrhage rate after tonsil surgery in children based on random forest model

Objective:Hemorrhage after tonsil surgery in children is a serious and potentially life-threatening complication. The purpose of this study was to establish a risk warning model for hemorrhage after tonsil surgery in children through a national multi-center retrospective study, providing a basis for hierarchical management after tonsil surgery in children. Methods:Stratified sampling was performed on 8 854 children who underwent tonsillectomy under general anesthesia from 15 research centers in different provinces from January 15, 2022 to May 15, 2023. The sample size of this study was 2 724 cases, including 1 096 males and 1 628 females. Children were divided into bleeding and non-bleeding groups according to whether or not they had bleeding after surgery. The random forest algorithm was used to build a risk warning model. By continuously exploring the optimized model, the accuracy of predicting the postoperative bleeding rate of tonsils in children was improved, and the prediction effectiveness of the model was verified by ten-fold cross-validation. Results:Among 2 724 children, 117 had postoperative bleeding after tonsillectomy, with a bleeding rate of 4.30%. The model constructed by the random forest algorithm for the training set was verified in the test set, and the obtained prediction accuracy was 98.72%, the recall rate was 78.95%, and the area under the ROC curve AUC was 0.96. Conclusion:Although the recall rate of the random forest model needs to be improved, the overall accuracy is quite excellent. It can effectively avoid misjudging positive cases as negative cases. It is a useful tool that can be used to predict the postoperative bleeding rate of tonsils and clinical medical decision-making, laying a good foundation for subsequent optimization and improvement.

Unravelling the protective effects of peptide isolated from marine sponge extract MS01 against SH-SY5Y cell line and its in-silico pharmacokinetic analysis

Parkinson's disease (PD) is characterised by developing postural instability, resting seizures, tremors, and a movement problem coupled with stiffness. All the available drugs can improve motor function considerably, but they can also have negative side effects, especially if the problem gets worse. The structure-activity relation was performed in the DISCOVERY STUDIO V2.5.5 pharmacophore model using the HypoGen algorithm for a training set of 15 compounds. Here, xenin peptide fits well with a least cost difference and a fit value of 10.46, indicating a favourable pharmacological characteristic. Therefore, we tried applying gene network analysis in cytoHubba to find the hub gene for PD in Danio rerio and Homo sapiens, as zebrafish and humans share many disease proteins and processes. Molecular docking studies for the hub gene polyubiquitin B from Danio rerio and Parkin from Homo sapiens, as well as the peptide xenin obtained from the marine sponge extract MS01 was performed. The peptide exhibits a substantial binding affinity with the receptor UBB through 8 and PRKN through 4 intermolecular hydrogen bonds in their bonded and non-bonded interactions, although it has little effect on the protein structure, according to simulation studies and dynamical free energy calculations. The protein structure has also been stabilised in terms of energy, secondary structure, and flexibility by the peptide binding. In addition to in-silico analysis the extract was tested in-vitro on SH-SY5Y cells for its effectiveness against ROS and cell viability, which proved its qualitative effect.

A temperature-sensitive points selection method for machine tool based on rough set and multi-objective adaptive hybrid evolutionary algorithm

Reasonable deployment of temperature sensors is the key to accurately monitoring the temperature field of machine tools and improving the accuracy of thermal error prediction and compensation models. To determine the optimal deployment location of sensors, this paper proposes a temperature-sensitive points selection method tightly coupled with rough set and multi-objective optimization. Firstly, the importance of each temperature measurement point to the thermal error is calculated based on the rough set, and information entropy is introduced to amplify the importance difference among adjacent measurement points at the same heat source. Then, with the temperature measurement points groups as the variables, the number of temperature measurement points in the group, and the information importance of the group as the objectives, a multi-objective attribute reduction model is established, which transforms the temperature-sensitive points selection problem into a discrete multi-objective optimization problem. Finally, a multi-objective adaptive hybrid evolutionary algorithm is proposed, which designs a population initialization method based on mutual information and interval probability, and dynamic adaptive evolutionary parameters to achieve optimal temperature-sensitive points selection. Experiments on the high-speed dry hobbing machine verify the superiority and effectiveness of the proposed method.

In-Memory modelling and simulations

A mechanism for modeling faults as addresses on smart data structures is proposed, which excludes the algorithm for modeling input test sets to obtain a test map of logical functionality. Smart data structures are represented by a logical vector and its derivatives in the form of truth tables and matrices. The test map is a matrix whose coordinates are defined by the combinations of all logical faults that are tested on the binary sets of the exhaustive test. The construction of the test map is focused on the architecture of in- memory computing based on read-write transactions, which makes the simulation mechanism economical in terms of simulation time and energy consumption due to the absence of a central processor. A logical vector as a single component of input data does not require synthesis into a technologically permitted structure of elements. Synthesis of smart data structures based on four matrix operations creates a fault test map like addresses for any logic. The proposed mechanism is focused on the service of SoC IP-cores under the control of the IEEE 1500 standard. The proposed mechanism has no analogues in the design and test industry in terms of simplicity and predictability of data structure sizes and the absence of a test set modeling algorithm.

Good Prophets Know When the End Is Near

We consider a class of online decision-making problems with exchangeable actions, where in each period a controller is presented an input type drawn from some stochastic arrival process. The controller must choose an action, and the final objective depends only on the aggregate type-action counts. Such a framework encapsulates many online stochastic variants of common optimization problems with knapsack, bin packing, and generalized assignment as canonical examples. In such settings, we study a natural model-predictive control algorithm. We introduce general conditions under which this algorithm obtains uniform additive loss (independent of the horizon) compared with an optimal solution with full knowledge of arrivals. Our condition builds on the recently introduced compensated coupling technique, providing a unified view of how uniform additive loss arises as a consequence of the geometry of the underlying decision-making problem. Our characterization allows us to derive uniform loss algorithms for several new settings, including the first such algorithm for online stochastic bin packing. It also lets us study the effect of other modeling assumptions, including choice of horizon, batched decisions, and limited computation. In particular, we show that our condition is fulfilled by the above-mentioned problems when the end of the time horizon is known sufficiently long before the end. In contrast, if at a late stage, there is still uncertainty about the end of the time horizon we show that such uniform loss guarantees are impossible to achieve. We use real and synthetic data to illustrate our findings. This paper was accepted by J. George Shanthikumar, data science. Funding: This work was supported by the Division of Mathematical Sciences [Grant 1839346]; the Division of Electrical, Communications and Cyber Systems [Grant 1847393]; and the Army Research Laboratory [Grant W911NF-17-1-0094]. The authors also gratefully acknowledge support from the NSF [Grants ECCS-1847393, CNS-195599, and DMS-1839346], AFOSR [Grant FA9550-23-1-0068], an ARL award [Grant W911NF-17-1-0094], and ARO MURI [Grant W911NF-19-1-0217]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.04307 .

Rewiring cattle movements to limit infection spread

Cattle tracing databases have become major resources for representing demographic processes of livestock and assessing potential risk of infections spreading by trade. The herds registered in these databases are nodes of a network of commercial movements, which can be altered to lower the risk of disease transmission. In this study, we develop an algorithm aimed at reducing the number of infected animals and herds, by rewiring specific movements responsible for trade flows from high- to low-prevalence herds. The algorithm is coupled with a generic computational model based on the French cattle movement tracing database (BDNI), and used to describe different scenarios for the spread of infection within and between herds from a recent outbreak (epidemic) or a five-year-old outbreak (endemic). Results show that rewiring successfully contains infections to a limited number of herds, especially if the outbreak is recent and the estimation of disease prevalence frequent, while the respective impact of the parameters of the algorithm depend on the infection parameters. Allowing any animal movement from high to low-prevalence herds reduces the effectiveness of the algorithm in epidemic settings, while frequent and fine-grained prevalence assessments improve the impact of the algorithm in endemic settings. Our approach focusing on a few commercial movements is expected to lead to substantial improvements in the control of a targeted disease, although changes in the network structure should be monitored for potential vulnerabilities to other diseases. This general algorithm could be applied to any network of controlled individual movements liable to spread disease.

Design and Development of an Automatic Layout Algorithm for Laser GNSS RTK.

At the current stage, the automation level of GNSS RTK equipment is low, and manual operation leads to decreased accuracy and efficiency in setting out. To address these issues, this paper has designed an algorithm for automatic setting out that resolves the common problem of reduced accuracy in conventional RTK. First, the calculation of the laser rotation center is conducted using relevant parameters to calibrate the instrument's posture and angle. Then, by analyzing the posture information, the relative position and direction of the instrument to the point to be set out are determined, and the rotation angles in the horizontal and vertical directions are calculated. Following this, the data results are analyzed, and the obtained rotation angles are output to achieve automatic control of the instrument. Finally, a rotating laser composed of servo motors and laser modules is used to control the GNSS RTK equipment to locate the set-out point, thereby determining its position on the ground and displaying it in real-time. Compared to traditional GNSS RTK equipment, the proposed automatic setting out algorithm and the developed GNSS laser RTK equipment reduce the setting out error from 15 mm to 10.3 mm. This reduces the barrier to using GNSS RTK equipment, minimizes human influence, enhances the work efficiency of setting out measurements, and ensures high efficiency and stability under complex conditions.

Advancing forest carbon stocks’ mapping using a hierarchical approach with machine learning and satellite imagery

Remote sensing of forests is a powerful tool for monitoring the biodiversity of ecosystems, maintaining general planning, and accounting for resources. Various sensors bring together heterogeneous data, and advanced machine learning methods enable their automatic handling in wide territories. Key forest properties usually under consideration in environmental studies include dominant species, tree age, height, basal area and timber stock. Being proxies of stand productivity, they can be utilized for forest carbon stock estimation to analyze forests’ status and proper climate change mitigation measures on a global scale. In this study, we aim to develop an effective machine learning-based pipeline for automatic carbon stock estimation using solely freely available and regularly updated satellite observations. We employed multispectral Sentinel-2 remote sensing data to predict forest structure characteristics and produce their detailed spatial maps. Using the Extreme Gradient Boosting (XGBoost) algorithm in classification and regression settings and management-level inventory data as reference measurements, we achieved quality of predictions of species equal to 0.75 according to the F1-score, and for stand age, height, and basal area, we achieved an accuracy of 0.75, 0.58 and 0.56, respectively, according to the R2. We focused on the growing stock volume as the main proxy to estimate forest carbon stocks on the example of the stem pool. We explored two approaches: a direct approach and a hierarchical approach. The direct approach leverages the remote sensing data to create the target maps, and the hierarchical approach calculates the target forest properties using predicted inventory characteristics and conversion equations. We estimated stem carbon stock based on the same approach: from Earth observation imagery directly and using biomass and conversion factors developed for the northern regions. Thus, our study proposes an end-to-end solution for carbon stock estimations based on the complexation of inventory data at the forest stand level, Earth observation imagery, machine learning predictions and conversion equations for the region. The presented approach enables more robust and accurate large-scale assessments using limited annotated datasets.

Efficient learning with projected histograms

High dimensional learning is a perennial problem due to challenges posed by the “curse of dimensionality”; learning typically demands more computing resources as well as more training data. In differentially private (DP) settings, this is further exacerbated by noise that needs adding to each dimension to achieve the required privacy. In this paper, we present a surprisingly simple approach to address all of these concerns at once, based on histograms constructed on a low-dimensional random projection (RP) of the data. Our approach exploits RP to take advantage of hidden low-dimensional structures in the data, yielding both computational efficiency, and improved error convergence with respect to the sample size—whereby less training data suffice for learning. We also propose a variant for efficient differentially private (DP) classification that further exploits the data-oblivious nature of both the histogram construction and the RP based dimensionality reduction, resulting in an efficient management of the privacy budget. We present a detailed and rigorous theoretical analysis of generalisation of our algorithms in several settings, showing that our approach is able to exploit low-dimensional structure of the data, ameliorates the ill-effects of noise required for privacy, and has good generalisation under minimal conditions. We also corroborate our findings experimentally, and demonstrate that our algorithms achieve competitive classification accuracy in both non-private and private settings.

Faecal source apportionment using molecular methods: A proof of concept using the FEAST algorithm

Faecal contamination of freshwater and marine environments represents a significant risk for public health, recreational activity and food safety, and tools for evaluating complex multi-source contamination remain largely in the development phase. We evaluated the efficacy of the Fast Expectation Maximization (FEAST) microbial source tracking (MST) algorithm to apportion sources of faecal contamination among four mammalian species of interest in coastal waters in New Zealand. Using 16S ribosomal DNA metabarcoding of faecal samples from cows, fur seals, and sheep, as well as human wastewater, we aimed to differentiate and quantify the contribution of these sources in mixed faecal samples. Multivariate analysis confirmed significant differences in the microbial communities associated with each mammalian source, with specific bacterial classes indicative of different sources. The FEAST algorithm was tested using mixed DNA and mixed faecal samples, and we found that the algorithm correctly assigned the dominant source from all samples, but underestimated the dominant source's proportional contribution. This underestimation suggests the need for further refinement and validation to ensure accurate source apportionment in environmental samples where the faecal signal is likely to be a minor component. Despite these limitations, the findings of our study, in combination with the evidence from others who have tested the FEAST algorithm in environmental settings, indicates that it represents an advance on existing tools for microbial source tracking and may become a useful addition to the toolbox for environmental management.

New horizons in prediction modelling using machine learning in older people's healthcare research.

Machine learning (ML) and prediction modelling have become increasingly influential in healthcare, providing critical insights and supporting clinical decisions, particularly in the age of big data. This paper serves as an introductory guide for health researchers and readers interested in prediction modelling and explores how these technologies support clinical decisions, particularly with big data, and covers all aspects of the development, assessment and reporting of a model using ML. The paper starts with the importance of prediction modelling for precision medicine. It outlines different types of prediction and machine learning approaches, including supervised, unsupervised and semi-supervised learning, and provides an overview of popular algorithms for various outcomes and settings. It also introduces key theoretical ML concepts. The importance of data quality, preprocessing and unbiased model performance evaluation is highlighted. Concepts of apparent, internal and external validation will be introduced along with metrics for discrimination and calibration for different types of outcomes. Additionally, the paper addresses model interpretation, fairness and implementation in clinical practice. Finally, the paper provides recommendations for reporting and identifies common pitfalls in prediction modelling and machine learning. The aim of the paper is to help readers understand and critically evaluate research papers that present ML models and to serve as a first guide for developing, assessing and implementing their own.

PREDICTING RISK OF EMPLOYEE INJURY IN A PEDIATRIC HOSPITAL

PURPOSEHealthcare has one of the highest rates of non-fatal occupational injury as compared to other industries. Yet, evaluation of risk determinants and prediction algorithms in hospital settings remains limited. METHODSThis study examines risk factors for employee injuries in a large pediatric hospital and evaluates prediction algorithms using hospital surveillance data, incident reports, and work unit measures such as patient density and employee workload. We employed multiple statistical models and machine learning tools, including logistic regression (LR), random forest (RF), penalized logistic regression (PLR), Naïve Bayes, neural network (NN), XGBoost (XG), and mixed-effects logistic regression (GLMER) to predict employee injury risk for specific time periods. We used cross-validation and receiver-operator characteristic (ROC) curve analyses to assess model performance. RESULTSGLMER, LR, and PLR were superior to other models, with higher AUC values (∼0.76), indicating good discrimination ability, though accuracy and specificity varied across models. RF showed high accuracy and specificity and comparable AUC with the top performing models. Further analyses using GLMER revealed variability in employee injury risk across months, days, and hospital units, identifying peaks on Tuesdays and Saturdays and in April and July, with lows in March and June. CONCLUSIONOur findings highlight the significance of monitoring specific risk factors within pediatric hospital settings and pairing them with appropriate predictive algorithms to effectively predict and mitigate employee injuries. These insights indicate that continuous monitoring may help enhance employee safety. Future work should evaluate additional predictors that may be obtained from individual hospital units, which may inform targeted prevention strategies.