A Resampling Method to Improve the Prognostic Model of End-Stage Kidney Disease: A Better Strategy for Imbalanced Data

Race and Ethnicity Disparities in Cardiovascular and Cancer Mortality: the Role of Socioeconomic Status-a Systematic Review and Meta-analysis

To clarify the role of socioeconomic status (SES) in cardiovascular and cancer mortality disparities observed between Black, Hispanic, and Asian compared to White adults, we conducted a meta-analysis of the longitudinal research in the USA. A PubMed, Ovid Medline, Web of Science, and EBSCO search was performed from January 1995 to May 2023. Two authors independently screened the studies and conducted risk assessments, with conflicts resolved via consensus. Studies were required to analyze mortality data using Cox proportional hazard regression. Random-effects models were used to pool hazard ratios (HR) and reporting followed PRISMA guidelines. Twenty-two studies with cardiovascular mortality (White and Black (n = 22), Hispanic (n = 7), and Asian (n = 3) adults) and twenty-three with cancer mortality endpoints (White and Black (n = 23), Hispanic (n = 11), and Asian (n = 10) adults) were included. The meta-analytic sample for cardiovascular mortality endpoints was 6,199,049 adults (White = 4,891,735; Black = 935,002; Hispanic = 295,623; Asian = 76,689), while for cancer-specific mortality endpoints was 7,745,180 adults (White = 5,988,392; Black= 1,070,447; Hispanic= 484,848; Asian = 201,493). Median follow-up was 10 and 11 years in cohorts with cardiovascular and cancer mortality endpoints, respectively. Adjustments for SES attenuated the higher risk for cardiovascular (HR, 1.46; 95% CI, 1.30-1.64) and cancer mortality (HR, 1.35; 95% CI, 1.32-1.38) of Black compared to White adults by 25% (HR, 1.21; 95% CI, 1.15-1.28) and 19% (HR, 1.16; 95% CI, 1.13-1.18), respectively. However, the Hispanic cardiovascular (HR, 0.79; 95% CI, 0.73-0.85) and Asian cancer mortality (HR, 0.81; 95% CI, 0.76-0.86) advantage were independent of SES. These findings emphasize the need to develop strategies focused on SES to reduce cardiovascular and cancer mortality in Black adults.

Assessment of Soybean Lodging Using UAV Imagery and Machine Learning

Plant lodging is one of the most essential phenotypes for soybean breeding programs. Soybean lodging is conventionally evaluated visually by breeders, which is time-consuming and subject to human errors. This study aimed to investigate the potential of unmanned aerial vehicle (UAV)-based imagery and machine learning in assessing the lodging conditions of soybean breeding lines. A UAV imaging system equipped with an RGB (red-green-blue) camera was used to collect the imagery data of 1266 four-row plots in a soybean breeding field at the reproductive stage. Soybean lodging scores were visually assessed by experienced breeders, and the scores were grouped into four classes, i.e., non-lodging, moderate lodging, high lodging, and severe lodging. UAV images were stitched to build orthomosaics, and soybean plots were segmented using a grid method. Twelve image features were extracted from the collected images to assess the lodging scores of each breeding line. Four models, i.e., extreme gradient boosting (XGBoost), random forest (RF), K-nearest neighbor (KNN) and artificial neural network (ANN), were evaluated to classify soybean lodging classes. Five data preprocessing methods were used to treat the imbalanced dataset to improve classification accuracy. Results indicate that the preprocessing method SMOTE-ENN consistently performs well for all four (XGBoost, RF, KNN, and ANN) classifiers, achieving the highest overall accuracy (OA), lowest misclassification, higher F1-score, and higher Kappa coefficient. This suggests that Synthetic Minority Oversampling-Edited Nearest Neighbor (SMOTE-ENN) may be a good preprocessing method for using unbalanced datasets and the classification task. Furthermore, an overall accuracy of 96% was obtained using the SMOTE-ENN dataset and ANN classifier. The study indicated that an imagery-based classification model could be implemented in a breeding program to differentiate soybean lodging phenotype and classify lodging scores effectively.

Explainable ensemble machine learning model for prediction of 28-day mortality risk in patients with sepsis-associated acute kidney injury

Background

Sepsis-associated acute kidney injury (S-AKI) is a major contributor to mortality in intensive care units (ICU). Early prediction of mortality risk is crucial to enhance prognosis and optimize clinical decisions. This study aims to develop a 28-day mortality risk prediction model for S-AKI utilizing an explainable ensemble machine learning (ML) algorithm.

Methods

This study utilized data from the Medical Information Mart for Intensive Care IV (MIMIC-IV 2.0) database to gather information on patients with S-AKI. Univariate regression, correlation analysis and Boruta were combined for feature selection. To construct the four ML models, hyperparameters were tuned via random search and five-fold cross-validation. To evaluate the performance of all models, ROC, K-S, and LIFT curves were used. The discrimination of ML models and traditional scoring systems was compared using area under the receiver operating characteristic curve (AUC). Additionally, the SHapley Additive exPlanation (SHAP) was utilized to interpret the ML model and identify essential variables. To investigate the relationship between the top nine continuous variables and the risk of 28-day mortality. COX regression-restricted cubic splines were utilized while controlling for age and comorbidities.

Results

The study analyzed data from 9,158 patients with S-AKI, dividing them into a 28-day mortality group of 1,940 and a survival group of 7,578. The results showed that XGBoost was the best performing model of the four ML models with AUC of 0.873. All models outperformed APS-III 0.713 and SAPS-II 0.681. The K-S and LIFT curves indicated XGBoost as the most effective predictor for 28-day mortality risk. The model's performance was evaluated using ROCpr curves, calibration curves, accuracy, precision, and F1 scores. SHAP force plots were utilized to interpret and visualize the personalized predictive power of the 28-day mortality risk model. Additionally, COX regression restricted cubic splines revealed an interesting non-linear relationship between the top nine variables and 28-day mortality.

Conclusion

The use of ensemble ML models has shown to be more effective than the LR model and conventional scoring systems in predicting 28-day mortality risk in S-AKI patients. By visualizing the XGBoost model with the best predictive performance, clinicians are able to identify high-risk patients early on and improve prognosis.

Multilayer perceptron-based self-care early prediction of children with disabilities

Early identification of children with self-care impairments is one of the key challenges professional therapists face due to the complex and time-consuming detection process using relevant self-care activities. Due to the complex nature of the problem, machine-learning methods have been widely applied in this area. In this study, a feed-forward artificial neural network (ANN)-based self-care prediction methodology, called multilayer perceptron (MLP)-progressive, has been proposed. The proposed methodology integrates unsupervised instance-based resampling and randomizing preprocessing techniques to MLP for improved early detection of self-care disabilities in children. Preprocessing of the dataset affects the MLP performance; hence, randomization and resampling of the dataset improves the performance of the MLP model. To confirm the usefulness of MLP-progressive, three experiments were conducted, including validating MLP-progressive methodology over multi-class and binary-class datasets, impact analysis of the proposed preprocessing filters on the model performance, and comparing the MLP-progressive results with state-of-the-art studies. The evaluation metrics accuracy, precision, recall, F-measure, TP rate, FP rate, and ROC were used to measure performance of the proposed disability detection model. The proposed MLP-progressive model outperforms existing methods and attains a classification accuracy of 97.14% and 98.57% on multi-class and binary-class datasets, respectively. Additionally, when evaluated on the multi-class dataset, significant improvements in accuracies ranging from 90.00% to 97.14% were observed when compared to state-of-the-art methods.

Construct and Validate a Predictive Model for Surgical Site Infection after Posterior Lumbar Interbody Fusion Based on Machine Learning Algorithm

Purpose. Surgical site infection is one of the serious complications after lumbar fusion. Early prediction and timely intervention can reduce the harm to patients. The aims of this study were to construct and validate a machine learning model for predicting surgical site infection after posterior lumbar interbody fusion, to screen out the most important risk factors for surgical site infection, and to explore whether synthetic minority oversampling technique could improve the model performance. Method. This study reviewed 584 patients who underwent posterior lumbar interbody fusion for degenerative lumbar disease at our center from January 2019 to August 2021. Clinical information and laboratory test data were collected from the electronic medical records. The original dataset was divided into training set and validation set in a 1 : 1 ratio. Seven machine learning algorithms were used to develop predictive models; the training set of each model was resampled using synthetic minority oversampling technique. Finally, the model performance was assessed in the validation set. Results. Of the 584 patients, 33 (5.65%) occurred surgical site infection. Stepwise logistic regression showed that preoperative albumin level (OR 0.659, 95% CI 0.563-0.756), diabetes (OR 9.129, 95% CI 3.816-23.126), intraoperative dural tear (OR 8.436, 95% CI 2.729-25.334), and rheumatic disease (OR 8.471, 95% CI 1.743-39.567) were significant predictors associated with surgical site infection. The performance of the AdaBoost Classification Trees model was the best among the seven machine learning models, and synthetic minority oversampling technique improved the performance of all models. Conclusion. The prediction model we constructed based on machine learning and synthetic minority oversampling technique can accurately predict surgical site infection, which is conducive to clinical decision-making and optimization of perioperative management.