Machine learning models for predicting unscheduled return visits of patients with abdominal pain at emergency department and validation during COVID-19 pandemic: A retrospective cohort study.

Machine learning (ML) models for predicting 72-hour unscheduled return visits (URVs) for patients with abdominal pain in the emergency department (ED) were developed in a previous study. This study refined the data to adjust previous prediction models and evaluated the model performance in future data validation during the COVID-19 era. We aimed to evaluate the practicality of the ML models and compare the URVs before and during the COVID-19 pandemic. We used electronic health records from Chang Gung Memorial Hospital from 2018 to 2019 as a training dataset, and various machine learning models, including logistic regression (LR), random forest (RF), extreme gradient boosting (XGB), and voting classifier (VC) were developed and subsequently used to validate against the 2020 to 2021 data. The models highlighted several determinants for 72-hour URVs, including patient age, prior ER visits, specific vital signs, and medical interventions. The LR, XGB, and VC models exhibited the same AUC of 0.71 in the testing set, whereas the VC model displayed a higher F1 score (0.21). The XGB model demonstrated the highest specificity (0.99) and precision (0.64) but the lowest sensitivity (0.01). Among these models, the VC model showed the most favorable, balanced, and comprehensive performance. Despite the promising results, the study illuminated challenges in predictive modeling, such as the unforeseen influences of global events, such as the COVID-19 pandemic. These findings not only highlight the significant potential of machine learning in augmenting emergency care but also underline the importance of iterative refinement in response to changing real-world conditions.

A Machine Learning Model for Predicting Unscheduled 72 h Return Visits to the Emergency Department by Patients with Abdominal Pain.

Seventy-two-hour unscheduled return visits (URVs) by emergency department patients are a key clinical index for evaluating the quality of care in emergency departments (EDs). This study aimed to develop a machine learning model to predict 72 h URVs for ED patients with abdominal pain. Electronic health records data were collected from the Chang Gung Research Database (CGRD) for 25,151 ED visits by patients with abdominal pain and a total of 617 features were used for analysis. We used supervised machine learning models, namely logistic regression (LR), support vector machine (SVM), random forest (RF), extreme gradient boosting (XGB), and voting classifier (VC), to predict URVs. The VC model achieved more favorable overall performance than other models (AUROC: 0.74; 95% confidence interval (CI), 0.69-0.76; sensitivity, 0.39; specificity, 0.89; F1 score, 0.25). The reduced VC model achieved comparable performance (AUROC: 0.72; 95% CI, 0.69-0.74) to the full models using all clinical features. The VC model exhibited the most favorable performance in predicting 72 h URVs for patients with abdominal pain, both for all-features and reduced-features models. Application of the VC model in the clinical setting after validation may help physicians to make accurate decisions and decrease URVs.

Specialized medial prefrontal-amygdala coordination in other-regarding decision preference.

Social behaviors recruit multiple cognitive operations that require interactions between cortical and subcortical brain regions. Interareal synchrony may facilitate such interactions between cortical and subcortical neural populations. However, it remains unknown how neurons from different nodes in the social brain network interact during social decision-making. Here we investigated oscillatory neuronal interactions between the basolateral amygdala and the rostral anterior cingulate gyrus of the medial prefrontal cortex while monkeys expressed context-dependent positive or negative other-regarding preference (ORP), whereby decisions affected the reward received by another monkey. Synchronization between the two nodes was enhanced for a positive ORP but suppressed for a negative ORP. These interactions occurred in beta and gamma frequency bands depending on the area contributing the spikes, exhibited a specific directionality of information flow associated with a positive ORP and could be used to decode social decisions. These findings suggest that specialized coordination in the medial prefrontal-amygdala network underlies social-decision preferences.

Tuning dissimilarity explains short distance decline of spontaneous spike correlation in macaque V1.

Fast spike correlation is a signature of neural ensemble activity thought to underlie perception, cognition, and action. To relate spike correlation to tuning and other factors, we focused on spontaneous activity because it is the common 'baseline' across studies that test different stimuli, and because variations in correlation strength are much larger across cell pairs than across stimuli. Is the probability of spike correlation between two neurons a graded function of lateral cortical separation, independent of functional tuning (e.g. orientation preferences)? Although previous studies found a steep decline in fast spike correlation with horizontal cortical distance, we hypothesized that, at short distances, this decline is better explained by a decline in receptive field tuning similarity. Here we measured macaque V1 tuning via parametric stimuli and spike-triggered analysis, and we developed a generalized linear model (GLM) to examine how different combinations of factors predict spontaneous spike correlation. Spike correlation was predicted by multiple factors including color, spatiotemporal receptive field, spatial frequency, phase and orientation but not ocular dominance beyond layer 4. Including these factors in the model mostly eliminated the contribution of cortical distance to fast spike correlation (up to our recording limit of 1.4mm), in terms of both 'correlation probability' (the incidence of pairs that have significant fast spike correlation) and 'correlation strength' (each pair's likelihood of fast spike correlation). We suggest that, at short distances and non-input layers, V1 fast spike correlation is determined more by tuning similarity than by cortical distance or ocular dominance.