Given the racial disparities in cervical cancer screening, incidence, and mortality, the purpose of this study was to estimate cervical cancer screening behaviors through self-reported Pap testing among racial groups in the U.S. This cross-sectional study utilized the Behavioral Risk Factor Surveillance System (BRFSS) data to compare Pap testing behaviors among women of different racial groups. The BRFSS data from 2014, 2016, and 2018 were chosen because these were the most recent years of data capturing cervical cancer screening information. The primary outcome was self-reported Pap testing behavior (yes/no). Racial groups were analyzed with the original categorical responses for the race/ethnicity variable to investigate Pap testing behaviors across all racial groups. Statistical analyses included descriptive statistics and a multivariable binomial logistic regression model to assess differences of Pap testing by race after adjusting for covariates. Among the 538,218 females included, 88.81% (95% CI: 88.60–89.03) reported receiving a Pap test. Pap testing behaviors differed significantly between racial groups in 2014, 2016, and 2018 (p < 0.001 for all years). Compared to White women, Asians (OR: 0.169, 95% CI: 0.149–0.191), Native Hawaiians/other Pacific Islanders (OR: 0.339, 95% CI: 0.249–0.462), American Indians or Alaskan Natives (OR: 0.664, 95% CI: 0.532–0.829), Hispanics (OR: 0.726, 95% CI: 0.670–0.786), and other non-Hispanic races (OR: 0.439, 95% CI: 0.323–0.598) were significantly less likely to receive Pap test. Racial disparities in cervical cancer screening with Pap tests exist for Asians, Native Hawaiians/other Pacific Islanders, American Indians or Alaskan Natives, Hispanics, and other non-Hispanics.

In this study, we leveraged machine learning (ML) approach to develop and validate new assessment tools for predicting stroke and bleeding among patients with atrial fibrillation (AFib) and cancer. We conducted a retrospective cohort study including patients who were newly diagnosed with AFib with a record of cancer from the 2012–2018 Surveillance, Epidemiology, and End Results (SEER)-Medicare database. The ML algorithms were developed and validated separately for each outcome by fitting elastic net, random forest (RF), extreme gradient boosting (XGBoost), support vector machine (SVM), and neural network models with tenfold cross-validation (train:test = 7:3). We obtained area under the curve (AUC), sensitivity, specificity, and F2 score as performance metrics. Model calibration was assessed using Brier score. In sensitivity analysis, we resampled data using Synthetic Minority Oversampling Technique (SMOTE). Among 18,388 patients with AFib and cancer, 523 (2.84%) had ischemic stroke and 221 (1.20%) had major bleeding within one year after AFib diagnosis. In prediction of ischemic stroke, RF significantly outperformed other ML models [AUC (0.916, 95% CI 0.887–0.945), sensitivity 0.868, specificity 0.801, F2 score 0.375, Brier score = 0.035]. However, the performance of ML algorithms in prediction of major bleeding was low with highest AUC achieved by RF (0.623, 95% CI 0.554–0.692). RF models performed better than CHA₂DS₂-VASc and HAS-BLED scores. SMOTE did not improve the performance of the ML algorithms. Our study demonstrated a promising application of ML in stroke prediction among patients with AFib and cancer. This tool may be leveraged in assisting clinicians to identify patients at high risk of stroke and optimize treatment decisions.

Background: Given its narrow therapeutic range, digoxin’s pharmacokinetic parameters in infants are difficult to predict due to variation in birth weight and gestational age, especially for critically ill newborns. There is limited evidence to support the safety and dosage requirements of digoxin, let alone to predict its concentrations in infants. This study aimed to compare the concentrations of digoxin predicted by traditional regression modeling and artificial neural network (ANN) modeling for newborn infants given digoxin for clinically significant patent ductus arteriosus (PDA).Methods: A retrospective chart review was conducted to obtain data on digoxin use for clinically significant PDA in a neonatal intensive care unit. Newborn infants who were given digoxin and had digoxin concentration(s) within the acceptable range were identified as subjects in the training model and validation datasets, accordingly. Their demographics, disease, and medication information, which were potentially associated with heart failure, were used for model training and analysis of digoxin concentration prediction. The models were generated using backward standard multivariable linear regressions (MLRs) and a standard backpropagation algorithm of ANN, respectively. The common goodness-of-fit estimates, receiver operating characteristic curves, and classification of sensitivity and specificity of the toxic concentrations in the validation dataset obtained from MLR or ANN models were compared to identify the final better predictive model.Results: Given the weakness of correlations between actual observed digoxin concentrations and pre-specified variables in newborn infants, the performance of all ANN models was better than that of MLR models for digoxin concentration prediction. In particular, the nine-parameter ANN model has better forecasting accuracy and differentiation ability for toxic concentrations.Conclusion: The nine-parameter ANN model is the best alternative than the other models to predict serum digoxin concentrations whenever therapeutic drug monitoring is not available. Further cross-validations using diverse samples from different hospitals for newborn infants are needed.