Comparing Explainable Machine Learning Approaches With Traditional Statistical Methods for Evaluating Stroke Risk Models: Retrospective Cohort Study.

BACKGROUND: Stroke has multiple modifiable and nonmodifiable risk factors and represents a leading cause of death globally. Understanding the complex interplay of stroke risk factors is thus not only a scientific necessity but a critical step toward improving global health outcomes. OBJECTIVE: We aim to assess the performance of explainable machine learning models in predicting stroke risk factors using real-world cohort data by comparing explainable machine learning models with conventional statistical methods. METHODS: This retrospective cohort included high-risk patients from Ramathibodi Hospital in Thailand between January 2010 and December 2020. We compared the performance and explainability of logistic regression (LR), Cox proportional hazard, Bayesian network (BN), tree-augmented Naïve Bayes (TAN), extreme gradient boosting (XGBoost), and explainable boosting machine (EBM) models. We used multiple imputation by chained equations for missing data and discretized continuous variables as needed. Models were evaluated using C-statistics and F1-scores. RESULTS: Out of 275,247 high-risk patients, 9659 (3.5%) experienced a stroke. XGBoost demonstrated the highest performance with a C-statistic of 0.89 and an F1-score of 0.80 followed by EBM and TAN with C-statistics of 0.87 and 0.83, respectively; LR and BN had similar C-statistics of 0.80. Significant factors associated with stroke included atrial fibrillation (AF), hypertension (HT), antiplatelets, HDL, and age. AF, HT, and antihypertensive medication were common significant factors across most models, with AF being the strongest factor in LR, XGBoost, BN, and TAN models. CONCLUSIONS: Our study developed stroke prediction models to identify crucial predictive factors such as AF, HT, or systolic blood pressure or antihypertensive medication, anticoagulant medication, HDL, age, and statin use in high-risk patients. The explainable XGBoost was the best model in predicting stroke risk, followed by EBM.

Prediction of seroma after total mastectomy using an artificial neural network algorithm.

Seroma is a common complication after mastectomy. To the best of our knowledge, no prediction models have been developed for this. Henceforth, medical records of total mastectomy patients were retrospectively reviewed. Data consisting of 120 subjects were divided into a training-validation data set (96 subjects) and a testing data set (24 subjects). Data was learned by using a 9-layer artificial neural network (ANN), and the model was validated using 10-fold cross-validation. The model performance was assessed by a confusion matrix in the validating data set. The receiver operating characteristic curve was constructed, and the area under the curve (AUC) was also calculated. Pathology type, presence of hypertension, presence of diabetes, receiving of neoadjuvant chemotherapy, body mass index, and axillary lymph node (LN) management (i.e., sentinel LN biopsy and axillary LN dissection) were selected as predictive factors in a model developed from the neural network algorithm. The model yielded an AUC of 0.760, which corresponded with a level of acceptable discrimination. Sensitivity, specificity, accuracy, and positive and negative predictive values were 100%, 52.9%, 66.7%, 46.7%, and 100%, respectively. Our model, which was developed from the ANN algorithm can predict seroma after total mastectomy with high sensitivity. Nevertheless, external validation is still needed to confirm the performance of this model.

C-reactive protein in prediction of ventriculoperitoneal shunt-related infection in high-risk patients.

BACKGROUND: C-reactive protein (CRP) is an inflammatory marker believed to be of value in the early detection of meningitis. We evaluated its potential as a marker for prediction of shunt-related infection in high-risk subjects. METHODS: We conducted a prospective pilot study in 26 ventriculoperitoneal shunt procedures; 18 of the patients were considered to be at high risk of infection at the time of shunt insertion. All patients were screened for other disease that could cause, an increase in CRP. RESULTS: The serum CRP medians were 3.90 mg/L in the whole sample and 5.36 mg/L in the high-risk participants. All four shunt infections occurred in the high-risk group (22.2% of the group), three (75%) of which were in patients with meningitis. The logistic regression model showed that CRP concentrations above the cut-off value of ≥ 7 mg/L were related to shunt infection (p=0.042). The receiving-operating characteristic curve revealed a cutoff point at ≥ 10.1 mg/L (sensitivity 0.75, 1 - specificity 0.18). The calculated area under the curve was 0.744. The sensitivity and specificity in the whole sample and high-risk group were not different (75% and 79%-80%, respectively). The positive post-test probability was 40% in the whole sample and 50% in the high-risk group. The negative post-test probability was 5% and 7%, respectively. CONCLUSION: Our data suggest that in a patient at high risk of shunt-related infection, the serum CRP concentration can be a valuable predictor of the risk of infection. Further studies in larger samples would be worthwhile.