Modelling sentence deficits in Arabic agrammatism: A machine learning approach to the Tree Pruning Hypothesis

Postoperative recovery following lumbar fusion surgery in patients aged 75 years and older often requires a prolonged length of stay (PLOS) in the hospital. Accurately predicting the risk of PLOS and assessing its risk factors for preoperative optimization are crucial to guide clinical decision-making. The aim of this study was to select the risk factors for PLOS and develop a machine learning (ML) model to estimate the likelihood of PLOS based on comprehensive geriatric assessment (CGA) domains in older patients undergoing lumbar fusion surgery. An observational cohort of 242 patients aged ≥ 75 years (median age 80 years) undergoing lumbar fusion surgery at a single center from March 2019 to December 2021 was retrospectively reviewed. Predictor variables consisted of clinical characteristics, CGA variables, and intraoperative variables. The primary outcome was PLOS, defined as a hospital LOS above the 75th percentile in the overall study population. Patients were randomly divided into two groups (7:3) for model training and validation. Ensemble ML algorithms were used to select the significant variables associated with PLOS, and 9 ML models were used to develop predictive models. The Shapley Additive Explanations (SHAP) method was used for model interpretation and feature importance ranking. Three ensemble ML algorithms selected 9 CGA and clinical variables as influential factors of PLOS. The random forest (RF) model had the best predictive performance among the models evaluated, with an area under the receiver operating characteristic curve of 0.822 (95% CI 0.727-0.917) and F1-score of 0.571. SHAP values indicated that the duration of surgery, the number of fusion levels, and age were the most important predictors, while the Fried frailty phenotype was the most important CGA variable for PLOS. The RF model and its SHAP interpretations were deployed online for clinical utility. This ML model could facilitate individual risk prediction and risk factor identification for PLOS in older patients undergoing lumbar fusion surgery, with the potential to improve preoperative optimization.

Patients with growth hormone (GH)-secreting pituitary adenomas (PAs) experience various symptoms and comorbidities, which can ultimately lead to increased mortality. This study aimed to develop and validate a machine learning (ML) model for predicting long-term outcomes in patients with GH-secreting PAs following endonasal transsphenoidal surgery (ETS). The authors conducted a retrospective three-institution cohort study that included patients with GH-secreting PAs treated with ETS between 2013 and 2023. Clinical, radiological, and biochemical data were collected. The main outcome of interest was the intervention-free rate (IFR) after primary ETS. Supervised ML algorithms, including decision trees and random forests, were developed to predict the IFR. Model performance was evaluated using area under the receiver operating characteristic curve (AUROC) and Shapley Additive Explanations (SHAP) values. The median follow-up for 100 patients with GH-secreting PAs (53% female) was 64 months (range 1-130 months). Additional intervention for persistent or recurrent acromegaly was required in 32% of patients. Following primary ETS alone, the 3-year IFR was 70% and the 5-year IFR was 67%. Multiple ML models were developed and evaluated using AUROCs. The decision tree analysis achieved an accuracy of 81% and emphasized the importance of both gross-total resection (GTR) and patient age in determining the long-term IFR. To better understand the factors that contributed to model performance, SHAP analysis was applied to the best-performing model. The SHAP dependence plots showed that key factors associated with a longer IFR included tumor size < 9 mm, GTR, patient age > 65 years, and Knosp grade 0. This ML model offers a more nuanced and potentially more accurate approach to identify patients more likely to develop recurrent or persistent acromegaly following primary ETS and require additional treatment. Following external validation, this ML model could improve personalized treatment planning and follow-up strategies and enhance patient care and resource allocation in clinical practice.

OBJECTIVE:To train and test supervised machine learning (ML) models to predict low cardiac output syndrome (LCOS) within the first 48 postoperative hours in neonates undergoing cardiothoracic surgery.DESIGN:Retrospective observational study. An efficient tree-based gradient-boosting algorithm (LightGBM) ML models were developed to predict LCOS occurrence at 2-, 4-, 6-, and 12-hour forecasting horizons, incorporating data from the prediction time and the two preceding hours. SHapley Additive exPlanations (SHAP) analyses were used for feature importance analyses.SETTING:Single center, January 2012 to April 2023.PATIENTS:Neonates 28 days old or younger who underwent cardiothoracic surgery.INTERVENTIONS:None.MEASUREMENTS AND MAIN RESULTS:A total of 181 neonates were included, with 14.9% experiencing LCOS. A multivariate time-series dataset was constructed using hourly clinical and laboratory variables recorded during the first 48 postoperative hours. The LightGBM ML models achieved area under the receiver operating characteristic curve values ranging from 0.91 to 0.98 and area under the precision-recall curve values ranging from 0.60 to 0.80 for LCOS prediction across 2-, 4-, 6-, and 12-hour forecasting horizons. SHAP analyses identified higher vasoactive inotrope score, lower urine output, and higher serum lactate as the most influential predictors.CONCLUSIONS:This study demonstrates that the supervised machine learning models can accurately predict LCOS in neonates, offering high interpretability. The findings support further validation in multicenter settings and integration into clinical workflows to enhance postoperative critical cardiac care neonates.

Explainable machine learning model predicting neurological deterioration in Wilson's disease via MRI radiomics and clinical features.

ObjectiveTo develop an interpretable machine learning (ML) model for predicting severe Mycoplasma pneumoniae pneumonia (SMPP) in order to provide reliable factors for predicting the clinical type of the disease.MethodsWe collected clinical data from 483 school-aged children with M. pneumoniae pneumonia (MPP) who were hospitalized at the Children's Hospital of Soochow University between September 2021 and June 2024. Difference analysis and univariate logistic regression were employed to identify predictors for training features in ML. Eight ML algorithms were used to build models based on the selected features, and their effectiveness was validated. The area under the curve (AUC), accuracy, five-fold cross-validation, and decision curve analysis (DCA) were utilized to evaluate model performance. Finally, the best-performing ML model was selected, and the Shapley Additive Explanations (SHAP) method was applied to rank the importance of clinical features and interpret the final model.ResultsAfter feature selection, 30 variables remained. We constructed eight ML models and assessed their effectiveness, finding that the CatBoost model exhibited the best predictive performance, with an AUC of 0.934 and an accuracy of 0.9175. DCA was used to compare the clinical benefits of the models, revealing that the CatBoost model provided greater net benefits than the other ML models within the threshold probability range of 34% to 75%. Additionally, we applied the SHAP method to interpret the CatBoost model, and the SHAP diagram was used to visually show the influence of predictor variables on the outcome. The results identified the top six risk factors as the number of days with fever, D-dimer, platelet count (PLT), C-reactive protein (CRP), lactate dehydrogenase (LDH), and the neutrophil-to-lymphocyte ratio (NLR).ConclusionsThe interpretable CatBoost model can help physicians accurately identify school-aged children with SMPP. This early identification facilitates better treatment options and timely prevention of complications. Furthermore, the SHAP algorithm enhances the model's transparency and increases its trustworthiness in practical applications.

The realm of cybersecurity places significant importance on early ransomware detection. Feature selection is critical in this context, as it enhances detection accuracy, mitigates overfitting, and reduces training time by eliminating irrelevant and redundant data. However, iterative feature selection techniques tend to select the best-performing subset of features through an iterative process which leaves chance for a crucial feature not being selected and the number of selected features may not always be the optimal or the most suitable for a given problem. Hence, this study aims to conduct a performance comparison analysis of an iterative feature selection technique- Recursive Feature Elimination with Cross-Validation (RFECV) with six supervised Machine Learning (ML) models to evaluate its efficiency in classifying ransomware utilizing the Application Programming Interface (API) call and network traffic features. The study employs an Explainable Artificial Intelligence (XAI) framework called SHapley Additive exPlanations (SHAP) to derive the crucial features when RFECV is not integrated with the ML models. These features are then compared with RFECV-selected features when it is integrated. Results show that without RFECV the ML models achieve better classification accuracies on two datasets. Again, RFECV falls short of selecting impactful features, leading to more false alarms. Moreover, it lacks the capability to rank the features based on their importance, reducing its efficiency in ransomware classification overall. Thus, this study underscores the importance of integrating explainability techniques to identify critical features, rather than solely relying on iterative feature selection methods, to enhance the resilience of ransomware detection systems.

Predicting rheumatoid arthritis (RA) progression in undifferentiated arthritis (UA) patients remains a challenge. Traditional approaches combining clinical assessments and ultrasonography (US) often lack accuracy due to the complex interaction of clinical variables, and routine extensive US is impractical. Machine learning (ML) models, particularly those integrating the 18-joint ultrasound scoring system (US18), have shown potential to address these issues but remain underexplored. This study aims to evaluate ML models integrating US18 with clinical data to improve early identification of high-risk patients and support personalized treatment strategies. In this prospective cohort, 432 UA patients were followed for 1year to track progression to RA. Four ML algorithms and one deep learning model were developed using baseline clinical and US18 data. Comparative experiments on a testing cohort identified the optimal model. SHAP (SHapley Additive exPlanations) analysis highlighted key variables, validated through an ablation experiment. Of the 432 patients, 152 (35.2%) progressed to the RA group, while 280 (64.8%) remained in the non-RA group. The Random Forest (RnFr) model demonstrated the highest accuracy and sensitivity. SHAP analysis identified joint counts at US18 Grade 2, total US18 score, and swollen joint count as the most influential variables. The ablation experiment confirmed the importance of US18 in enhancing early RA detection. Integrating the US18 assessment with clinical data in an RnFr model significantly improves early detection of RA progression in UA patients, offering potential for earlier and more personalized treatments. Key Points •A machine learning model integrating clinical and ultrasound features effectively predicts rheumatoid arthritis progression in undifferentiated arthritis patients. •The 18-joint ultrasound scoring system (US18) enhances predictive accuracy, particularly when incorporated with clinical variables in a Random Forest model. •SHAP analysis underscores that joint severity levels in US18 contribute significantly to early identification of high-risk patients. •This study offers a feasible and efficient approach for clinical implementation, supporting more personalized and timely RA treatment strategies.

BackgroundAccurate prediction of mortality in critically ill patients with hypertension admitted to the Intensive Care Unit (ICU) is essential for guiding clinical decision-making and improving patient outcomes. Traditional prognostic tools often fall short in capturing the complex interactions between clinical variables in this high-risk population. Recent advances in machine learning (ML) and deep learning (DL) offer the potential for developing more sophisticated and accurate predictive models.ObjectiveThis study aims to evaluate the performance of various ML and DL models in predicting mortality among critically ill patients with hypertension, with a particular focus on identifying key clinical predictors and assessing the comparative effectiveness of these models.MethodsWe conducted a retrospective analysis of 30,096 critically ill patients with hypertension admitted to the ICU. Various ML models, including logistic regression, decision trees, and support vector machines, were compared with advanced DL models, including 1D convolutional neural networks (CNNs) and long short-term memory (LSTM) networks. Model performance was evaluated using area under the receiver operating characteristic curve (AUC) and other performance metrics. SHapley Additive exPlanations (SHAP) values were used to interpret model outputs and identify key predictors of mortality.ResultsThe 1D CNN model with an initial selection of predictors achieved the highest AUC (0.7744), outperforming both traditional ML models and other DL models. Key clinical predictors of mortality identified across models included the APS-III score, age, and length of ICU stay. The SHAP analysis revealed that these predictors had a substantial influence on model predictions, underscoring their importance in assessing mortality risk in this patient population.ConclusionDeep learning models, particularly the 1D CNN, demonstrated superior predictive accuracy compared to traditional ML models in predicting mortality among critically ill patients with hypertension. The integration of these models into clinical workflows could enhance the early identification of high-risk patients, enabling more targeted interventions and improving patient outcomes. Future research should focus on the prospective validation of these models and the ethical considerations associated with their implementation in clinical practice.

Despite increasing interest in using Artificial Intelligence (AI) and Machine Learning (ML) models for drug development, effectively interpreting their predictions remains a challenge, which limits their impact on clinical decisions. We address this issue by providing a practical guide to SHapley Additive exPlanations (SHAP), a popular feature‐based interpretability method, which can be seamlessly integrated into supervised ML models to gain a deeper understanding of their predictions, thereby enhancing their transparency and trustworthiness. This tutorial focuses on the application of SHAP analysis to standard ML black‐box models for regression and classification problems. We provide an overview of various visualization plots and their interpretation, available software for implementing SHAP, and highlight best practices, as well as special considerations, when dealing with binary endpoints and time‐series models. To enhance the reader's understanding for the method, we also apply it to inherently explainable regression models. Finally, we discuss the limitations and ongoing advancements aimed at tackling the current drawbacks of the method.

Skeletal muscle loss during treatment is associated with poor survival outcomes in patients with ovarian cancer. Although changes in muscle mass can be assessed on computed tomography (CT) scans, this labour-intensive process can impair its utility in clinical practice. This study aimed to develop a machine learning (ML) model to predict muscle loss based on clinical data and to interpret the ML model by applying SHapley Additive exPlanations (SHAP) method. This study included the data of 617 patients with ovarian cancer who underwent primary debulking surgery and platinum-based chemotherapy at a tertiary centre between 2010 and 2019. The cohort data were split into training and test sets based on the treatment time. External validation was performed using 140 patients from a different tertiary centre. The skeletal muscle index (SMI) was measured from pre- and post-treatment CT scans, and a decrease in SMI≥5% was defined as muscle loss. We evaluated five ML models to predict muscle loss, and their performance was determined using the area under the receiver operating characteristic curve (AUC) and F1 score. The features for analysis included demographic and disease-specific characteristics and relative changes in body mass index (BMI), albumin, neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR). The SHAP method was applied to determine the importance of the features and interpret the ML models. The median (inter-quartile range) age of the cohort was 52 (46-59) years. After treatment, 204 patients (33.1%) experienced muscle loss in the training and test datasets, while 44 (31.4%) patients experienced muscle loss in the external validation dataset. Among the five evaluated ML models, the random forest model achieved the highest AUC (0.856, 95% confidence interval: 0.854-0.859) and F1 score (0.726, 95% confidence interval: 0.722-0.730). In the external validation, the random forest model outperformed all ML models with an AUC of 0.874 and an F1 score of 0.741. The results of the SHAP method showed that the albumin change, BMI change, malignant ascites, NLR change, and PLR change were the most important factors in muscle loss. At the patient level, SHAP force plots demonstrated insightful interpretation of our random forest model to predict muscle loss. Explainable ML model was developed using clinical data to identify patients experiencing muscle loss after treatment and provide information of feature contribution. Using the SHAP method, clinicians may better understand the contributors to muscle loss and target interventions to counteract muscle loss.

BackgroundDiabetic retinopathy (DR), a prevalent complication in patients with type 2 diabetes, has attracted increasing attention. Recent studies have explored a plausible association between retinopathy and significant liver fibrosis. The aim of this investigation was to develop a sophisticated machine learning (ML) model, leveraging comprehensive clinical datasets, to forecast the likelihood of significant liver fibrosis in patients with retinopathy and to interpret the ML model by applying the SHapley Additive exPlanations (SHAP) method.MethodsThis inquiry was based on data from the National Health and Nutrition Examination Survey 2005–2008 cohort. Utilizing the Fibrosis-4 index (FIB-4), liver fibrosis was stratified across a spectrum of grades (F0-F4). The severity of retinopathy was determined using retinal imaging and segmented into four discrete gradations. A ten-fold cross-validation approach was used to gauge the propensity towards liver fibrosis. Eight ML methodologies were used: Extreme Gradient Boosting, Random Forest, multilayer perceptron, Support Vector Machines, Logistic Regression (LR), Plain Bayes, Decision Tree, and k-nearest neighbors. The efficacy of these models was gauged using metrics, such as the area under the curve (AUC). The SHAP method was deployed to unravel the intricacies of feature importance and explicate the inner workings of the ML model.ResultsThe analysis included 5,364 participants, of whom 2,116 (39.45%) exhibited notable liver fibrosis. Following random allocation, 3,754 individuals were assigned to the training set and 1,610 were allocated to the validation cohort. Nine variables were curated for integration into the ML model. Among the eight ML models scrutinized, the LR model attained zenith in both AUC (0.867, 95% CI: 0.855–0.878) and F1 score (0.749, 95% CI: 0.732–0.767). In internal validation, this model sustained its superiority, with an AUC of 0.850 and an F1 score of 0.736, surpassing all other ML models. The SHAP methodology unveils the foremost factors through importance ranking.ConclusionSophisticated ML models were crafted using clinical data to discern the propensity for significant liver fibrosis in patients with retinopathy and to intervene early.Practice implicationsImproved early detection of liver fibrosis risk in retinopathy patients enhances clinical intervention outcomes.

We sought to develop and validate a machine learning (ML) model for predicting multidimensional frailty based on clinical and laboratory data. Moreover, an explainable ML model utilizing SHapley Additive exPlanations (SHAP) was constructed. This study enrolled 622 patients hospitalized due to decompensating episodes at a tertiary hospital. The cohort data were randomly divided into training and test sets. External validation was carried out using 131 patients from other tertiary hospitals. The frail phenotype was defined according to a self-reported questionnaire (Frailty Index). The area under the receiver operating characteristics curve was adopted to compare the performance of five ML models. The importance of the features and interpretation of the ML models were determined using the SHAP method. The proportions of cirrhotic patients with nonfrail and frail phenotypes in combined training and test sets were 87.8% and 12.2%, respectively, while they were 88.5% and 11.5% in the external validation dataset. Five ML algorithms were used, and the random forest (RF) model exhibited substantially predictive performance. Regarding the external validation, the RF algorithm outperformed other ML models. Moreover, the SHAP method demonstrated that neutrophil-to-lymphocyte ratio, age, lymphocyte-to-monocyte ratio, ascites, and albumin served as the most important predictors for frailty. At the patient level, the SHAP force plot and decision plot exhibited a clinically meaningful explanation of the RF algorithm. We constructed an ML model (RF) providing accurate prediction of frail phenotype in decompensated cirrhosis. The explainability and generalizability may foster clinicians to understand contributors to this physiologically vulnerable situation and tailor interventions.

Internal iliac and obturator lymph nodes are common sites of metastasis in rectal cancer. This study developed a machine learning (ML) model using clinical data to predict lymph node metastasis and applied the Shapley Additive explanations (SHAP) method for interpretation. Retrospectively, data from patients with rectal cancer at four Chinese centers-who underwent total mesorectal excision and lateral pelvic lymph node dissection without neoadjuvant therapy-were collected. Two centers provided training/test sets (3:1 ratio) and two centers supplied external validation. Lymph node enlargement was determined by imaging and confirmed by pathology. Five ML models were evaluated by AUC, accuracy, and F1 score. Key features included demographics, tumor stage, tumor-to-anal verge distance, imaging measurements, tumor histological differentiation, preoperative carcinoembryonic antigen, and carbohydrate antigen 19-9. SHAP was used to assess feature importance. Of the 411 cases (174 positives) in the training/test sets and 109 cases (43 positives) in external validation, the random forest (RF) model ranked second in terms of AUC and accuracy in the training set (0.999, 0.995), whereas it achieved the highest AUC and accuracy (0.877 and 0.788) in the test set. In the external validation, the RF model outperformed all other ML models (AUC of 0.899, accuracy of 0.827). Overall, the RF model demonstrates the superior overall performance. According to the SHAP analysis, the most important predictors of internal iliac and obturator lymph node metastasis were, in descending order, the short-axis diameter of enlarged lymph nodes, regional lymph node metastasis, and tumor-to-anal verge distance. At the individual patient level, SHAP force plots provided explanations of the RF model predictions for internal iliac and obturator lymph node metastasis. An interpretable ML model was developed that accurately predicts internal iliac and obturator lymph node metastasis using clinical data. SHAP analysis enhances understanding of feature contributions, supporting personalized treatment planning.

Background This study aimed to establish and validate a machine learning (ML) model for predicting in-hospital mortality in critically ill patients with chronic kidney disease (CKD). Methods This study collected data on CKD patients from 2008 to 2019 using the Medical Information Mart for Intensive Care IV. Six ML approaches were used to build the model. Accuracy and area under the curve (AUC) were used to choose the best model. In addition, the best model was interpreted using SHapley Additive exPlanations (SHAP) values. Results There were 8527 CKD patients eligible for participation; the median age was 75.1 (interquartile range: 65.0–83.5) years, and 61.7% (5259/8527) were male. We developed six ML models with clinical variables as input factors. Among the six models developed, the eXtreme Gradient Boosting (XGBoost) model had the highest AUC, at 0.860. According to the SHAP values, the sequential organ failure assessment score, urine output, respiratory rate, and simplified acute physiology score II were the four most influential variables in the XGBoost model. Conclusions In conclusion, we successfully developed and validated ML models for predicting mortality in critically ill patients with CKD. Among all ML models, the XGBoost model is the most effective ML model that can help clinicians accurately manage and implement early interventions, which may reduce mortality in critically ill CKD patients with a high risk of death.

BackgroundThe risk of developing atherosclerotic cardiovascular disease (ASCVD) varies among individuals and is related to a variety of lifestyle factors in addition to the presence of chronic diseases.ObjectiveWe aimed to assess the predictive accuracy of machine learning (ML) models incorporating lifestyle risk behaviors for ASCVD risk using the Korean nationwide database.MethodsUsing data from the Korea National Health and Nutrition Examination Survey, 5 ML algorithms were used for the prediction of high ASCVD risk: logistic regression (LR), support vector machine, random forest, extreme gradient boosting, and light gradient boosting models. ASCVD risk was assessed using the pooled cohort equations, with a high-risk threshold of ≥7.5% over 10 years. Among the 8573 participants aged 40‐79 years, propensity score matching (PSM) was used to adjust for demographic confounders. We divided the dataset into a training and a test dataset in an 8:2 ratio. We also used bootstrapping to train the ML model with the area under the receiver operating characteristics curve score. Shapley additive explanations were used to identify the models’ important variables in assessing high ASCVD risks. In sensitivity analysis, we additionally performed binary LR analysis, in which the ML model’s results were consistent with the conventional statistical model.ResultsOf the 8573 participants, 41.7% (n=3578) had high ASCVD risk. Before PSM, age and sex differed significantly between groups. PSM (1:1) yielded 1976 patients with balanced demographics. After PSM, the high ASCVD risk group had higher alcohol or tobacco use, lower omega-3 intake, higher BMI, less physical activity, and spent less time sitting. In 5 ML models, the extreme gradient boosting model showed the highest area under the receiver operating characteristics curve, indicating superior overall discrimination between high and low ASCVD risk groups. However, the light gradient boosting model demonstrated better performance in accuracy, recall, and F1-score. Variable importance analysis using Shapley additive explanations identified smoking and age as the strongest predictors, while BMI, sodium or omega-3 intake, and low-density lipoprotein cholesterol also had significant variables. Sensitivity analysis using multivariable LR analysis also confirmed these findings, showing that smoking, BMI, and low-density lipoprotein cholesterol increased ASCVD risk, whereas omega-3 intake and physical activity were associated with lower risk.ConclusionsAnalyzing lifestyle behavioral factors in ASCVD risk with an ML model improves the predictive performance compared to traditional models. Personalized prevention strategies tailored to an individual’s lifestyle can effectively reduce ASCVD risk.