Machine Learning-based Models Research Articles

Predicting Hospitalization Risk Among Home Care Residents in the United Kingdom: Development and Validation of a Machine Learning-Based Predictive Model

Preventable hospital admissions in elderly home care residents are a major socioeconomic burden, whilst early detection of deterioration may improve outcomes. Our goal was to develop and validate a machine learning-based algorithm to predict hospitalization risk among home care users. Our primary outcome was hospitalization. An existing risk score (1-5) was assessed for its discriminatory capacity over time. We subsequently developed a new machine learning model using carer concerns, service user demographics, and other home care data between January and July 2021. We randomly selected 150 service user records for validation, which were evaluated by both the model and 10 clinicians (9 doctors and 1 nurse) to compare prediction time and accuracy to human experts. Comparison between model and human was via area under the receiver operating characteristic curve (AUC). A score of 5 conferred an 8x higher likelihood of hospitalization in the subsequent 7 days (15.4% vs 1.8%, p < .05), compared to a score of 1. The new model and risk score increased performance, detecting 182 hospitalizations/month (3.7x chance). The AUC for the model was significantly higher than for clinicians (0.87 vs 0.41-0.57, respectively; p < .05). The model took <1 minute, while clinicians typically took over 40 minutes. A risk prediction model using carer concerns and other home care data features detects 3.7x more hospitalizations than chance. The model is faster and more accurate than human clinicians, enabling low-cost scale-up. This study supports linking the model to a triage and intervention service to reduce preventable hospitalizations in the home care sector.

Machine learning-based model to predict delirium in patients with advanced cancer treated with palliative care: a multicenter, patient-based registry cohort

This study aimed to present a new approach to predict to delirium admitted to the acute palliative care unit. To achieve this, this study employed machine learning model to predict delirium in patients in palliative care and identified the significant features that influenced the model. A multicenter, patient-based registry cohort study in South Korea between January 1, 2019, and December 31, 2020. Delirium was identified by reviewing the medical records based on the criteria of the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition. The study dataset included 165 patients with delirium among 2314 patients with advanced cancer admitted to the acute palliative care unit. Seven machine learning models, including extreme gradient boosting, adaptive boosting, gradient boosting, light gradient boosting, logistic regression, support vector machine, and random forest, were evaluated to predict delirium in patients with advanced cancer admitted to the acute palliative care unit. An ensemble approach was adopted to determine the optimal model. For k-fold cross-validation, the combination of extreme gradient boosting and random forest provided the best performance, achieving the following accuracy metrics: 68.83% sensitivity, 70.85% specificity, 69.84% balanced accuracy, and 74.55% area under the receiver operating characteristic curve. The performance of the isolated testing dataset was also validated, and the machine learning model was successfully deployed on a public website (http://ai-wm.khu.ac.kr/Delirium/) to provide public access to delirium prediction results in patients with advanced cancer. Furthermore, using feature importance analysis, sex was determined to be the top contributor in predicting delirium, followed by a history of delirium, chemotherapy, smoking status, alcohol consumption, and living with family. Based on a large-scale, multicenter, patient-based registry cohort, a machine learning prediction model for delirium in patients with advanced cancer was developed in South Korea. We believe that this model will assist healthcare providers in treating patients with delirium and advanced cancer.

An Intelligent Model and Methodology for Predicting Length of Stay and Survival in a Critical Care Hospital Unit

This paper describes the design and methodology for the development and validation of an intelligent model in the healthcare domain. The generated model relies on artificial intelligence techniques, aiming to predict the length of stay and survival rate of patients admitted to a critical care hospitalization unit with better results than predictive systems using scoring. The proposed methodology is based on the following stages: preliminary data analysis, analysis of the architecture and systems integration model, the big data model approach, information structure and process development, and the application of machine learning techniques. This investigation substantiates that automated machine learning models significantly surpass traditional prediction techniques for patient outcomes within critical care settings. Specifically, the machine learning-based model attained an F1 score of 0.351 for mortality forecast and 0.615 for length of stay, in contrast to the traditional scoring model’s F1 scores of 0.112 for mortality and 0.412 for length of stay. These results strongly support the advantages of integrating advanced computational techniques in critical healthcare environments. It is also shown that the use of integration architectures allows for improving the quality of the information by providing a data repository large enough to generate intelligent models. From a clinical point of view, obtaining more accurate results in the estimation of the ICU stay and survival offers the possibility of expanding the uses of the model to the identification and prioritization of patients who are candidates for admission to the ICU, as well as the management of patients with specific conditions.

Establishment and validation of an artificial intelligence web application for predicting postoperative in-hospital mortality in patients with hip fracture: a National cohort study of 52,707 cases.

In-hospital mortality following hip fractures is a significant concern, and accurate prediction of this outcome is crucial for appropriate clinical management. Nonetheless, there is a lack of effective prediction tools in clinical practice. By utilizing artificial intelligence and machine learning techniques, this study aims to develop a predictive model that can assist clinicians in identifying geriatric hip fracture patients at a higher risk of in-hospital mortality. A total of 52,707 geriatric hip fracture patients treated with surgery from 90 hospitals were included in this study. The primary outcome was postoperative in-hospital mortality. The patients were randomly divided into two groups, with a ratio of 7:3. The majority of patients, assigned to the training cohort, were used to develop the AI models. The remaining patients, assigned to the validation cohort, were used to validate the models. Various machine learning algorithms, including logistic regression (LR), decision tree (DT), naïve Bayesian (NB), neural network (NN), eXGBoosting machine (eXGBM), and random forest (RF), were employed for model development. A comprehensive scoring system, incorporating 10 evaluation metrics, was developed to assess the prediction performance, with higher scores indicating superior predictive capability. Based on the best machine learning-based model, an AI application was developed on the Internet. In addition, a comparative testing of prediction performance between doctors and the AI application. The eXGBM model exhibited the best prediction performance, with an AUC of 0.908 (95% CI: 0.881-0.932), as well as the highest accuracy (0.820), precision (0.817), specificity (0.814), and F1 score (0.822), and the lowest Brier score (0.120) and log loss (0.374). Additionally, the model showed favorable calibration, with a slope of 0.999 and an intercept of 0.028. According to the scoring system incorporating 10 evaluation metrics, the eXGBM model achieved the highest score (56), followed by the RF model (48) and NN model (41). The LR, DT, and NB models had total scores of 27, 30, and 13, respectively. The AI application has been deployed online at https://in-hospitaldeathinhipfracture-l9vhqo3l55fy8dkdvuskvu.streamlit.app/, based on the eXGBM model. The comparative testing revealed that the AI application's predictive capabilities significantly outperformed those of the doctors in terms of AUC values (0.908 vs. 0.682, P<0.001). The eXGBM model demonstrates promising predictive performance in assessing the risk of postoperative in-hospital mortality among geriatric hip fracture patients. The developed AI model serves as a valuable tool to enhance clinical decision-making.

Development and application of a machine learning-based predictive model for obstructive sleep apnea screening

ObjectiveTo develop a robust machine learning prediction model for the automatic screening and diagnosis of obstructive sleep apnea (OSA) using five advanced algorithms, namely Extreme Gradient Boosting (XGBoost), Logistic Regression (LR), Support Vector Machine (SVM), Light Gradient Boosting Machine (LightGBM), and Random Forest (RF) to provide substantial support for early clinical diagnosis and intervention.MethodsWe conducted a retrospective analysis of clinical data from 439 patients who underwent polysomnography at the Affiliated Hospital of Xuzhou Medical University between October 2019 and October 2022. Predictor variables such as demographic information [age, sex, height, weight, body mass index (BMI)], medical history, and Epworth Sleepiness Scale (ESS) were used. Univariate analysis was used to identify variables with significant differences, and the dataset was then divided into training and validation sets in a 4:1 ratio. The training set was established to predict OSA severity grading. The validation set was used to assess model performance using the area under the curve (AUC). Additionally, a separate analysis was conducted, categorizing the normal population as one group and patients with moderate-to-severe OSA as another. The same univariate analysis was applied, and the dataset was divided into training and validation sets in a 4:1 ratio. The training set was used to build a prediction model for screening moderate-to-severe OSA, while the validation set was used to verify the model's performance.ResultsAmong the four groups, the LightGBM model outperformed others, with the top five feature importance rankings of ESS total score, BMI, sex, hypertension, and gastroesophageal reflux (GERD), where Age, ESS total score and BMI played the most significant roles. In the dichotomous model, RF is the best performer of the five models respectively. The top five ranked feature importance of the best-performing RF models were ESS total score, BMI, GERD, age and Dry mouth, with ESS total score and BMI being particularly pivotal.ConclusionMachine learning-based prediction models for OSA disease grading and screening prove instrumental in the early identification of patients with moderate-to-severe OSA, revealing pertinent risk factors and facilitating timely interventions to counter pathological changes induced by OSA. Notably, ESS total score and BMI emerge as the most critical features for predicting OSA, emphasizing their significance in clinical assessments. The dataset will be publicly available on my Github.

Optimization of Sparse Matrix Computation for Algebraic Multigrid on GPUs

AMG is one of the most efficient and widely used methods for solving sparse linear systems. The computational process of AMG mainly consists of a series of iterative calculations of generalized sparse matrix-matrix multiplication (SpGEMM) and sparse matrix-vector multiplication (SpMV). Optimizing these sparse matrix calculations is crucial for accelerating solving linear systems. In this paper, we first focus on optimizing the SpGEMM algorithm in AmgX, a popular AMG library for GPUs. We propose a new algorithm called SpGEMM-upper, which achieves an average speedup of 2.02x on Tesla V100 and 1.96x on RTX 3090 against the original algorithm. Next, through experimental investigation, we conclude that no single SpGEMM library or algorithm performs optimally for most sparse matrices, and the same holds true for SpMV. Therefore, we build machine learning-based models to predict the optimal SpGEMM and SpMV used in the AMG calculation process. Finally, we integrate the prediction models, SpGEMM-upper, and other selected algorithms into a framework for adaptive sparse matrix computation in AMG. Our experimental results prove that the framework achieves promising performance improvements on the test set.

Machine learning-based model for the intelligent estimation of critical heat flux in nanofluids

The rising demand for advanced energy systems requires enhanced thermal management strategies to maximize resource utilization and productivity. This is quite an important industrial and academic trend as the efficiency of energy systems depends on the cooling systems. This study intends to address the critical need for efficient heat transfer mechanisms in industrial energy systems, particularly those relying on pool boiling conditions, by mainly focusing on Critical Heat Flux (CHF). In fact, CHF keeps a limit in thermal system design, beyond which the efficiency of the system drops. Recent research materials have highlighted nanofluids’ superior heat transfer properties over conventional pure fluids, like water, which makes them a considerable substitution for improving CHF in cooling systems. However, the broad variability in experimental outcomes challenges the development of a unified predictive model. Besides, Machine Learning (ML) based prediction has shown great accuracy for modeling of the designing parameters, including CHF. Utilizing ML algorithms—Cascade Forward Neural Network (CFNN), Extreme Gradient Boosting (XGBoost), Extra Tree, and Light Gradient Boosting Method (LightGBM)— four predictive models have been developed and the benchmark shows CFNN’s superior accuracy with an average goodness of fit of 89.32%, significantly higher than any available model in the literature. Also, the iterative stability analysis demonstrated that this model with a 0.0348 standard deviation and 0.0268 mean absolute deviation is the most stable and robust method that its performance minorly changes with input data. The novelty of the work mainly lies in the prediction of CHF with these advanced algorithm models to enhance the reliability and accuracy of CHF prediction for designing purposes, which are capable of considering many effective parameters into account with much higher accuracy than mathematical fittings. This study not only explains the complex interplay of nanofluid parameters affecting CHF but also offers practical implications for the design of more efficient thermal management systems, thereby contributing to the broader field of energy system enhancement through innovative cooling solutions.

Shear strength model of FRP-reinforced concrete beams without shear reinforcement

The current design codes for evaluating the shear strength of concrete beams reinforced with fiber-reinforced polymer (FRP) bars predominantly rely on a semi-empirical approach through test data calibration without a robust theoretical background. This approach may lead to either overestimation or underestimation, resulting in significant scattering in strength predictions. This study proposed a modified approach to enhance the existing unified shear-strength model developed for steel-reinforced concrete (RC) beams based on the compression zone failure mechanism, for predicting the shear strength of FRP-RC beams without shear reinforcement. The proposed model integrates strain and stress approaches, accounting for the distinctive characteristics associated with FRP reinforcement. Additionally, the overall shear resistance capacity of the FRP-RC beam is considered as the sum of contributions from the intact concrete of the compression zone and the aggregate interlock mechanism. The reliability of the proposed model is verified by comparing its predictions with a comprehensive database comprising 264 FRP-RC beam specimens without shear reinforcement reported by 41 research groups. Furthermore, the accuracy of the proposed model was assessed through a comparative analysis with existing evaluation methods, encompassing machine learning-based models, empirical models, theoretical models, and design codes. The results indicate that the proposed model satisfactorily predicts the shear strength of FRP-RC beams within a wide range of design parameters. In comparison with machine learning-based models, the proposed theoretical model exhibits a similar level of accuracy in terms of the mean shear strength ratio and coefficient of determination (R2) values but with less scattering, indicated by lower coefficient of variation (COV) and root mean square error (RMSE) values. In addition, the proposed model outperforms existing empirical and theoretical models, as well as current design codes, by significantly enhancing prediction accuracy, demonstrated by lower COV, RMSE, and higher R2 values. Finally, a parametric study was conducted to investigate the effects of the key parameters on the shear strength of FRP-RC beams using both the proposed model and representative existing evaluation methods.

Status of the Deep Learning-Based Shattered Pellet Injection Shard Tracking at ASDEX Upgrade

Plasma disruptions pose an intolerable risk to large tokamaks, such as ITER. If a disruption can no longer be avoided, ITER’s last line of defense will be the Shattered Pellet Injection. An experimental test bench was created at ASDEX Upgrade to inform the design decisions for controlling the shattering of the pellets and develop the techniques for the generation of the fragment distributions necessary for optimal disruption mitigation. In an effort to analyze the videos resulting from the more than 1000 tests and determine the impact of different settings on the resulting shard cloud, an analysis pipeline, based on traditional computer vision (CV), was created. This pipeline enabled the analysis of 173 of the videos, but at the same time showed the limits of traditional CV when applied in applications with a highly heterogeneous dataset such as this. We created a machine learning-based (ML) alternative as a drop-in replacement to the original image processing code using a semantic segmentation model to exploit the innate adaptability and robustness of deep learning models. This model is capable of labeling the entire dataset quickly, accurately and reliably. This contribution details the implementation of the ML model and the current state and future plans of the project.

Emergency Department Risk Model: Timely Identification of Patients for Outpatient Care Coordination

Emergency Department Risk Model: Timely Identification of Patients for Outpatient Care Coordination

Identifying potential (re)hemorrhage among sporadic cerebral cavernous malformations using machine learning

The (re)hemorrhage in patients with sporadic cerebral cavernous malformations (CCM) was the primary aim for CCM management. However, accurately identifying the potential (re)hemorrhage among sporadic CCM patients in advance remains a challenge. This study aims to develop machine learning models to detect potential (re)hemorrhage in sporadic CCM patients. This study was based on a dataset of 731 sporadic CCM patients in open data platform Dryad. Sporadic CCM patients were followed up 5 years from January 2003 to December 2018. Support vector machine (SVM), stacked generalization, and extreme gradient boosting (XGBoost) were used to construct models. The performance of models was evaluated by area under receiver operating characteristic curves (AUROC), area under the precision-recall curve (PR-AUC) and other metrics. A total of 517 patients with sporadic CCM were included (330 female [63.8%], mean [SD] age at diagnosis, 42.1 [15.5] years). 76 (re)hemorrhage (14.7%) occurred during follow-up. Among 3 machine learning models, XGBoost model yielded the highest mean (SD) AUROC (0.87 [0.06]) in cross-validation. The top 4 features of XGBoost model were ranked with SHAP (SHapley Additive exPlanations). All-Elements XGBoost model achieved an AUROCs of 0.84 and PR-AUC of 0.49 in testing set, with a sensitivity of 0.86 and a specificity of 0.76. Importantly, 4-Elements XGBoost model developed using top 4 features got a AUROCs of 0.83 and PR-AUC of 0.40, a sensitivity of 0.79, and a specificity of 0.72 in testing set. Two machine learning-based models achieved accurate performance in identifying potential (re)hemorrhages within 5 years in sporadic CCM patients. These models may provide insights for clinical decision-making.

Machine Learning-Based Predictive Models for Patients with Venous Thromboembolism: A Systematic Review.

Venous thromboembolism (VTE) is a chronic disorder with a significant health and economic burden. Several VTE-specific clinical prediction models (CPMs) have been used to assist physicians in decision-making but have several limitations. This systematic review explores if machine learning (ML) can enhance CPMs by analyzing extensive patient data derived from electronic health records. We aimed to explore ML-CPMs' applications in VTE for risk stratification, outcome prediction, diagnosis, and treatment. Three databases were searched: PubMed, Google Scholar, and IEEE electronic library. Inclusion criteria focused on studies using structured data, excluding non-English publications, studies on non-humans, and certain data types such as natural language processing and image processing. Studies involving pregnant women, cancer patients, and children were also excluded. After excluding irrelevant studies, a total of 77 studies were included. Most studies report that ML-CPMs outperformed traditional CPMs in terms of receiver operating area under the curve in the four clinical domains that were explored. However, the majority of the studies were retrospective, monocentric, and lacked detailed model architecture description and external validation, which are essential for quality audit. This review identified research gaps and highlighted challenges related to standardized reporting, reproducibility, and model comparison. ML-CPMs show promise in improving risk assessment and individualized treatment recommendations in VTE. Apparently, there is an urgent need for standardized reporting and methodology for ML models, external validation, prospective and real-world data studies, as well as interventional studies to evaluate the impact of artificial intelligence in VTE.

Investigating the influence of clustering techniques and parameters on a hybrid PSO-driven ANFIS model for electricity prediction

The availability of reliable electrical power, which is essential for a comfortable lifestyle worldwide, requires realistic power usage projections for electric utilities and policymakers, leading to the adoption of machine learning-based modelling tools due to the limitations of traditional power usage projection approaches. However, successful modeling of power usage in neuro-fuzzy models depends on the optimal selection of hyper-parameters. Consequently, this research looked at the major impact clustering methods and hyper-parameter modifications on a particle swarm optimization (PSO)-based adaptive neuro-fuzzy inference system (ANFIS) model. The study examined two distinct clustering methods and other key hyperparameters such as the number of clusters and cluster radius, resulting in a total of 10 sub-models. The performance of the developed models was assessed using four widely recognized performance indicators: root mean square error, mean absolute percentage error (MAPE), mean absolute error (MAE), and coefficient of variation of the root mean square error (CVRMSE). Additionally, the robustness of the optimal sub-model was evaluated by comparing it with other hybrid models based on three different PSO variants. The results revealed that the combination of the ANFIS approach and PSO, specifically with two clusters, yielded the most accurate forecasting scheme with the optimal values for MAPE (7.7778%), MAE (712.6094), CVRMSE (9.5464), and RMSE (909.4998).

Development of a machine learning-based model to predict prognosis of alpha-fetoprotein-positive hepatocellular carcinoma

BackgroundPatients with alpha-fetoprotein (AFP)-positive hepatocellular carcinoma (HCC) have aggressive biological behavior and poor prognosis. Therefore, survival time is one of the greatest concerns for patients with AFP-positive HCC. This study aimed to demonstrate the utilization of six machine learning (ML)-based prognostic models to predict overall survival of patients with AFP-positive HCC.MethodsData on patients with AFP-positive HCC were extracted from the Surveillance, Epidemiology, and End Results database. Six ML algorithms (extreme gradient boosting [XGBoost], logistic regression [LR], support vector machine [SVM], random forest [RF], K-nearest neighbor [KNN], and decision tree [ID3]) were used to develop the prognostic models of patients with AFP-positive HCC at one year, three years, and five years. Area under the receiver operating characteristic curve (AUC), confusion matrix, calibration curves, and decision curve analysis (DCA) were used to evaluate the model.ResultsA total of 2,038 patients with AFP-positive HCC were included for analysis. The 1-, 3-, and 5-year overall survival rates were 60.7%, 28.9%, and 14.3%, respectively. Seventeen features regarding demographics and clinicopathology were included in six ML algorithms to generate a prognostic model. The XGBoost model showed the best performance in predicting survival at 1-year (train set: AUC = 0.771; test set: AUC = 0.782), 3-year (train set: AUC = 0.763; test set: AUC = 0.749) and 5-year (train set: AUC = 0.807; test set: AUC = 0.740). Furthermore, for 1-, 3-, and 5-year survival prediction, the accuracy in the training and test sets was 0.709 and 0.726, 0.721 and 0.726, and 0.778 and 0.784 for the XGBoost model, respectively. Calibration curves and DCA exhibited good predictive performance as well.ConclusionsThe XGBoost model exhibited good predictive performance, which may provide physicians with an effective tool for early medical intervention and improve the survival of patients.

A machine learning-based ensemble model for securing the IoT network

A machine learning-based ensemble model for securing the IoT network

Machine learning in predicting pathological complete response to neoadjuvant chemoradiotherapy in rectal cancer using MRI: A systematic review and meta-analysis.

To evaluate the performance of machine learning models in predicting treatment response to neoadjuvant chemoradiotherapy (nCRT) in rectal cancer using computed tomography (CT) and magnetic resonance imaging (MRI). We searched PubMed, Embase, Cochrane Library, and Web of Science for studies published before January 2023. The Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) was used to assess the methodological quality of the included studies, random-effects models were used to calculate sensitivity and specificity, I2 values were used for heterogeneity measurements, and subgroup analyses were carried out to detect potential sources of heterogeneity. A total of 1690 patients from 24 studies were included. The meta-analysis calculated a pooled area under the curve (AUC) of 0.92 (95%CI-0.89-0.94), pooled sensitivity of 0.81 (95%CI-0.73-0.88), and pooled specificity of 0.88 (95%CI-0.82-0.92). We investigated 4 studies that mainly contributed to heterogeneity. After performing meta-analysis again excluding these 4 studies, the heterogeneity was significantly reduced. In subgroup analysis, the pooled AUC of the deep learning model was 0.95 and was 0.88 for the traditional statistical model; the pooled AUC of studies that used diffusion-weighted imaging (DWI) was 0.90, and was 0.92 in studies that did not use DWI; the pooled AUC of studies conducted in China was 0.94, and was 0.83 in studies conducted in other countries. Machine learning has promising potential in predicting tumor response to nCRT in patients with locally advanced rectal cancer. Together with clinical information, machine-learning based models may bring us closer toward precision medicine. Compared to traditional machine learning models, deep learning-based studies are able to obtain higher AUC, although they are less predominant and more heterogeneous. Together with clinical information, machine-learning based models may bring us closer toward precision medicine.

Explainable machine learning approach for cancer prediction through binarilization of RNA sequencing data.

In recent years, researchers have proven the effectiveness and speediness of machine learning-based cancer diagnosis models. However, it is difficult to explain the results generated by machine learning models, especially ones that utilized complex high-dimensional data like RNA sequencing data. In this study, we propose the binarilization technique as a novel way to treat RNA sequencing data and used it to construct explainable cancer prediction models. We tested our proposed data processing technique on five different models, namely neural network, random forest, xgboost, support vector machine, and decision tree, using four cancer datasets collected from the National Cancer Institute Genomic Data Commons. Since our datasets are imbalanced, we evaluated the performance of all models using metrics designed for imbalance performance like geometric mean, Matthews correlation coefficient, F-Measure, and area under the receiver operating characteristic curve. Our approach showed comparative performance while relying on less features. Additionally, we demonstrated that data binarilization offers higher explainability by revealing how each feature affects the prediction. These results demonstrate the potential of data binarilization technique in improving the performance and explainability of RNA sequencing based cancer prediction models.

Machine learning-based prediction models affecting the recovery of postoperative bowel function for patients undergoing colorectal surgeries

PurposeThe debate surrounding factors influencing postoperative flatus and defecation in patients undergoing colorectal resection prompted this study. Our objective was to identify independent risk factors and develop prediction models for postoperative bowel function in patients undergoing colorectal surgeries.MethodsA retrospective analysis of medical records was conducted for patients who undergoing colorectal surgeries at Peking University People’s Hospital from January 2015 to October 2021. Machine learning algorithms were employed to identify risk factors and construct prediction models for the time of the first postoperative flatus and defecation. The prediction models were evaluated using sensitivity, specificity, the Youden index, and the area under the receiver operating characteristic curve (AUC) through logistic regression, random forest, Naïve Bayes, and extreme gradient boosting algorithms.ResultsThe study included 1358 patients for postoperative flatus timing analysis and 1430 patients for postoperative defecation timing analysis between January 2015 and December 2020 as part of the training phase. Additionally, a validation set comprised 200 patients who undergoing colorectal surgeries from January to October 2021. The logistic regression prediction model exhibited the highest AUC (0.78) for predicting the timing of the first postoperative flatus. Identified independent risk factors influencing the time of first postoperative flatus were Age (p < 0.01), oral laxatives for bowel preparation (p = 0.01), probiotics (p = 0.02), oral antibiotics for bowel preparation (p = 0.02), duration of operation (p = 0.02), postoperative fortified antibiotics (p = 0.02), and time of first postoperative feeding (p < 0.01). Furthermore, logistic regression achieved an AUC of 0.72 for predicting the time of first postoperative defecation, with age (p < 0.01), oral antibiotics for bowel preparation (p = 0.01), probiotics (p = 0.01), and time of first postoperative feeding (p < 0.01) identified as independent risk factors.ConclusionsThe study suggests that he use of probiotics and early recovery of diet may enhance the recovery of bowel function in patients undergoing colorectal surgeries. Among the various analytical methods used, logistic regression emerged as the most effective approach for predicting the timing of the first postoperative flatus and defecation in this patient population.

Machine learning with electrocardiograms to optimize mortality risk stratification in patients with suspected acute coronary syndrome

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): National Heart, Lung, and Blood Institute (NHLBI) and National Center for Advancing Translational Sciences (NCATS). Background The importance of risk stratification in patients with suspected acute coronary syndrome (ACS) extends beyond diagnosis and immediate treatment. It influences the precision of care delivery and the allocation of resources to those at highest risk of adverse events. Purpose We sought to evaluate the prognostic value of electrocardiogram (ECG) feature-based machine learning models to risk-stratify long-term mortality in those with suspected ACS. Methods This was a multicenter prospective observational cohort study of consecutive, non-traumatic patients evaluated at the emergency department for suspected ACS. The derivation cohort included 4,015 patients from a University Medical Center (age 59±16 years, 47% women, 80% training with 10-fold cross-validation and 20% testing). Ascertainment of all-cause death was based on numerous data sources, including the Centers for Disease Control and Prevention’s National Death Index registry. We trained six machine learning methods for survival analysis using 73 morphological ECG features from presenting prehospital 10-second 12-lead ECGs. We used predicted risk scores in a variational Bayesian Gaussian mixture model to define three risk groups corresponding to low, moderate, and high-risk and compared the resulting classification performance against the HEART score. The external testing cohort included 3,095 patients from a University (age 59±15 years, 44% women). Results The mortality rate was 20.3% in the derivation cohort after an average follow-up period of 3.53 years (IQR 1.75 - 5.32). Extra Survival Trees outperformed other forecasting models during cross-validation. In the internal testing cohort, the derived risk groups were significantly predictive of survival (Kaplan-Meier log-rank test statistic = 121.14, p&amp;lt;0.001), outperforming the classification performance using the HEART score (Figure 1). Our machine learning-based risk stratification model detected &amp;gt;90% of death events missed by low-risk HEART score (Figure 2). Ruling-out the low-risk group, it achieved a negative predictive value of 93.4% with a sensitivity of 85.9% (compared to 89.0% and 75.0%, respectively, for the HEART score). This classification performance generalized well to our external testing cohort. Compared to those in the low-risk group, patients at moderate (OR = 3.62 [1.35-9.74]) and high (OR = 6.12 [2.38-15.75]) risk had significantly higher odds of 30-day cardiovascular death (n=109, 3.5%). Conclusions Using only features from the 10-second 12-lead ECG, we derived and externally validated a machine learning model that stratifies the mortality risk of patients during long-term follow up. This model outperformed current standard of care based on the HEART score. Future work should validate the potential of this decision-support tool in influencing treatment plans and resource allocation.Figure 1.Clustering performanceFigure 2.Sankey diagram

Elevating sustainability with a multi-renewable hydrogen generation system empowered by machine learning and multi-objective optimization

The global energy landscape is rapidly shifting toward cleaner, lower-carbon electricity generation, necessitating a transition to alternate energy sources. Hydrogen, particularly green hydrogen, looks to be a significant solution for facilitating this transformation, as it is produced by water electrolysis with renewable energy sources such as solar irradiations, wind speed, and biomass residuals. Traditional energy systems are costly and produce energy slowly due to unpredictability in resource supply. To address this challenge, this work provides a novel technique that integrates a multi-renewable energy system using multi objective optimization algorithm to meets the machine learning-based forecasted load model. Several forecasting models, including Autoregressive Integrated Moving Average (ARIMA), Random Forest and Long Short-Term Memory Recurrent Neural Network (LSTM-RNN), are assessed for develop the statistical metrics values such as RMSE, MAE, and MAPE. The selected Non-Sorting Moth Flame Optimization (NSMFO) algorithm demonstrates technological prowess in efficiently achieving global optimization, particularly when handling multiple objective functions. This integrated method shows enormous promise in technological, economic, and environmental terms, emphasizing its ability to promote energy sustainability targets.