Toward Dynamically Adaptive Simulation: Multimodal Classification of User Expertise Using Wearable Devices.

Simulation-based training has been proven to be a highly effective pedagogical strategy. However, misalignment between the participant's level of expertise and the difficulty of the simulation has been shown to have significant negative impact on learning outcomes. To ensure that learning outcomes are achieved, we propose a novel framework for adaptive simulation with the goal of identifying the level of expertise of the learner, and dynamically modulating the simulation complexity to match the learner's capability. To facilitate the development of this framework, we investigate the classification of expertise using biological signals monitored through wearable sensors. Trauma simulations were developed in which electrocardiogram (ECG) and galvanic skin response (GSR) signals of both novice and expert trauma responders were collected. These signals were then utilized to classify the responders' expertise, successive to feature extraction and selection, using a number of machine learning methods. The results show the feasibility of utilizing these bio-signals for multimodal expertise classification to be used in adaptive simulation applications.

Unlocking the Potential of Rucaparib: A Case Series on Its Impact in Metastatic Breast Cancer With Mutations.

Triple-negative breast cancer poses distinct challenges because it lacks hormone receptors and does not have human epidermal growth factor receptor 2 (HER2) amplification. Mutations in BRCA1/2 genes are associated with homologous recombination deficiency tumors, rendering them susceptible to poly (ADP-ribose) polymerase (PARP) inhibitors. Notably, germline BRCA1/2 mutations are linked to distinct clinical features, including an increased risk of triple-negative breast cancer (TNBC) and a younger age of onset. PARP inhibitors such as olaparib and talazoparib have demonstrated efficacy in patients with BRCA mutations, leading to FDA approvals for ovarian and breast cancers. However, there remains limited data on PARP inhibitor response rates in patients with somatic BRCA mutations. This case series demonstrates the use of rucaparib in metastatic breast cancer patients harboring both germline and somatic BRCA1/2 mutations, discussing the advancing landscape of targeted therapies in breast cancer management. In the first case, despite undergoing anthracycline-based chemotherapy followed by hormonal therapy, disease progression ensued. However, transitioning to rucaparib yielded a remarkable complete response lasting over two years, highlighting its efficacy in this clinical setting. Similarly, in the second case, rucaparib demonstrated effectiveness as a maintenance therapy subsequent to achieving a near-complete response to taxane and platinum-based treatment. These findings emphasize the promising role of rucaparib in managing metastatic breast cancer in patients with BRCA1/2 mutations, further contributing to the expanding armamentarium of targeted therapies in breast cancer care.

On Fixed Accuracy Confidence Interval in Multivariate Normal Distribution with Order 1 Autoregressive Covariance Structure.

In this paper, stein-type two-stage sampling procedure is carried out for fixed accuracy confidence interval estimation of the common variance ( σ 2 ) parameter corresponding to multivariate normal distribution with autoregressive covariance structure of order 1. Related asymptotics are obtained and simulation results are presented.

Detection of maternal and fetal stress from the electrocardiogram with self-supervised representation learning.

In the pregnant mother and her fetus, chronic prenatal stress results in entrainment of the fetal heartbeat by the maternal heartbeat, quantified by the fetal stress index (FSI). Deep learning (DL) is capable of pattern detection in complex medical data with high accuracy in noisy real-life environments, but little is known about DL's utility in non-invasive biometric monitoring during pregnancy. A recently established self-supervised learning (SSL) approach to DL provides emotional recognition from electrocardiogram (ECG). We hypothesized that SSL will identify chronically stressed mother-fetus dyads from the raw maternal abdominal electrocardiograms (aECG), containing fetal and maternal ECG. Chronically stressed mothers and controls matched at enrolment at 32 weeks of gestation were studied. We validated the chronic stress exposure by psychological inventory, maternal hair cortisol and FSI. We tested two variants of SSL architecture, one trained on the generic ECG features for emotional recognition obtained from public datasets and another transfer-learned on a subset of our data. Our DL models accurately detect the chronic stress exposure group (AUROC = 0.982 ± 0.002), the individual psychological stress score (R2 = 0.943 ± 0.009) and FSI at 34 weeks of gestation (R2 = 0.946 ± 0.013), as well as the maternal hair cortisol at birth reflecting chronic stress exposure (0.931 ± 0.006). The best performance was achieved with the DL model trained on the public dataset and using maternal ECG alone. The present DL approach provides a novel source of physiological insights into complex multi-modal relationships between different regulatory systems exposed to chronic stress. The final DL model can be deployed in low-cost regular ECG biosensors as a simple, ubiquitous early stress detection and monitoring tool during pregnancy. This discovery should enable early behavioral interventions.

The future of simulation-based medical education: Adaptive simulation utilizing a deep multitask neural network.

BACKGROUND: In resuscitation medicine, effectively managing cognitive load in high-stakes environments has important implications for education and expertise development. There exists the potential to tailor educational experiences to an individual's cognitive processes via real-time physiologic measurement of cognitive load in simulation environments. OBJECTIVE: The goal of this research was to test a novel simulation platform that utilized artificial intelligence to deliver a medical simulation that was adaptable to a participant's measured cognitive load. METHODS: The research was conducted in 2019. Two board-certified emergency physicians and two medical students participated in a 10-minute pilot trial of a novel simulation platform. The system utilized artificial intelligence algorithms to measure cognitive load in real time via electrocardiography and galvanic skin response. In turn, modulation of simulation difficulty, determined by a participant's cognitive load, was facilitated through symptom severity changes of an augmented reality (AR) patient. A postsimulation survey assessed the participants' experience. RESULTS: Participants completed a simulation that successfully measured cognitive load in real time through physiological signals. The simulation difficulty was adapted to the participant's cognitive load, which was reflected in changes in the AR patient's symptoms. Participants found the novel adaptive simulation platform to be valuable in supporting their learning. CONCLUSION: Our research team created a simulation platform that adapts to a participant's cognitive load in real-time. The ability to customize a medical simulation to a participant's cognitive state has potential implications for the development of expertise in resuscitation medicine.