Frontal EEG asymmetry has been investigated as a physiological metric of approach motivation, with higher left frontal activity (LFA) suggested to reflect approach motivation. However, correlations between LFA and traditional metrics of approach motivation (e.g., scores from the behavioral inhibition system/behavioral approach system [BIS/BAS] survey) are inconsistent. It is also not clear how LFA correlates to approach motivation on an observable, behavioral level. Here, we tested correlations between BIS/BAS scores, LFA, and performance in the Effort Expenditure for Rewards Task (EEfRT). In our sample (n = 49), BIS/BAS results did not correlate to LFA values (resting or task states), and were also unrelated to EEfRT performance variables. We found evidence of significant and distinct correlations between LFA and EEfRT performance. Resting-state LFA positively correlated to effort expenditure on lower utility trials, where reward size and/or probability were suboptimal. Task-onset LFA captured in the first 5 min of the task was related to overall behavioral performance in the EEfRT. High task-onset LFA correlated to high trial completion rates, high-effort trial selection percentages, and overall monetary earnings. One interpretation of these initial findings is that resting-state LFA reflects approach tendencies to expend effort, but that this extends to suboptimal situations, whereas task-state LFA better reflects effortful approach toward high-utility goals. Given the relatively small sample size and the risk of Type I/II errors, we present the study as exploratory and the results as preliminary. However, the findings highlight interesting initial links between LFA and EEfRT performance. The need for larger replication studies is discussed.

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Endoscopic specialists performing gastroscopy, which relies on the naked eye, may benefit from a computer-aided diagnosis (CADx) system that employs deep learning. This report proposes utilizing a CADx system to classify normal and abnormal gastric cancer, gastritis, and gastric ulcer. The CADx system was trained using a deep learning algorithm known as a convolutional neural network (CNN). Specifically, Xception, which includes depth-wise separable convolution, was employed as the CNN. Image augmentation was applied to improve the disadvantages of medical data, which are difficult to collect. A class activation map (CAM), an algorithm that visualizes the classified region of interest in a CNN, was used to cut and paste the image area into another image. The CAM-identified lesion location in an abnormal image was augmented by pasting it into a normal image. The normal image was divided into nine equal parts and pasted where the variance difference from the lesion was minimal. Consequently, the number of abnormal images increased by 360,905. Xception was used to train the augmented dataset. A confusion matrix was used to evaluate the performance of the gastroscopy CADx system. The performance criteria were specificity, sensitivity, F1 score, harmonic average of precision, sensitivity (recall), and AUC. The F1 score of the CADx system trained with the original dataset was 0.792 and AUC was 0.885. The dataset augmentation approach using CAM presented in this report is shown to be an effective augmentation algorithm, with performance improved to 0.835, 0.903 in terms of F1 score and AUC respectively.

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Background Although there is a growing interest in prediction models based on electronic medical records (EMRs) to identify patients at risk of adverse cardiac events following invasive coronary treatment, robust models fully utilizing EMR data are limited. Objective We aimed to develop and validate machine learning (ML) models by using diverse fields of EMR to predict the risk of 30-day adverse cardiac events after percutaneous intervention or bypass surgery. Methods EMR data of 5,184,565 records of 16,793 patients at a quaternary hospital between 2006 and 2016 were categorized into static basic (eg, demographics), dynamic time-series (eg, laboratory values), and cardiac-specific data (eg, coronary angiography). The data were randomly split into training, tuning, and testing sets in a ratio of 3:1:1. Each model was evaluated with 5-fold cross-validation and with an external EMR-based cohort at a tertiary hospital. Logistic regression (LR), random forest (RF), gradient boosting machine (GBM), and feedforward neural network (FNN) algorithms were applied. The primary outcome was 30-day mortality following invasive treatment. Results GBM showed the best performance with area under the receiver operating characteristic curve (AUROC) of 0.99; RF had a similar AUROC of 0.98. AUROCs of FNN and LR were 0.96 and 0.93, respectively. GBM had the highest area under the precision-recall curve (AUPRC) of 0.80, and the AUPRCs of RF, LR, and FNN were 0.73, 0.68, and 0.63, respectively. All models showed low Brier scores of <0.1 as well as highly fitted calibration plots, indicating a good fit of the ML-based models. On external validation, the GBM model demonstrated maximal performance with an AUROC of 0.90, while FNN had an AUROC of 0.85. The AUROCs of LR and RF were slightly lower at 0.80 and 0.79, respectively. The AUPRCs of GBM, LR, and FNN were similar at 0.47, 0.43, and 0.41, respectively, while that of RF was lower at 0.33. Among the categories in the GBM model, time-series dynamic data demonstrated a high AUROC of >0.95, contributing majorly to the excellent results. Conclusions Exploiting the diverse fields of the EMR data set, the ML-based 30-day adverse cardiac event prediction models demonstrated outstanding results, and the applied framework could be generalized for various health care prediction models.