Project description:We present a 71-year-old male, who had had a heart transplantation 24 years prior, who came to our clinic with a low-grade fever and a new II/VI holosystolic murmur. Echocardiography showed a large mass in the right atrium with attachment near the junction of the right atrium and superior vena cava. The patient was taken to the operating room for resection of the mass. Microscopic evaluation was consistent with thrombus. Differential diagnosis of cardiac masses after cardiac transplant includes tumour, thrombus, and vegetation. Final diagnosis can be challenging; multimodality imaging and biopsy or resection often are required for final diagnosis.
Project description:Many of our decisions take place under uncertainty. To successfully navigate the environment, individuals need to estimate the degree of uncertainty and adapt their behaviors accordingly by learning from experiences. However, uncertainty is a broad construct and distinct types of uncertainty may differentially influence our learning. We provide a semi-systematic review to illustrate cognitive and neurobiological processes involved in learning under two types of uncertainty: learning in environments with stochastic outcomes, and with volatile outcomes. We specifically reviewed studies (N = 26 studies) that included an adolescent population, because adolescence is a period in life characterized by heightened exploration and learning, as well as heightened uncertainty due to experiencing many new, often social, environments. Until now, reviews have not comprehensively compared learning under distinct types of uncertainties in this age range. Our main findings show that although the overall developmental patterns were mixed, most studies indicate that learning from stochastic outcomes, as indicated by increased accuracy in performance, improved with age. We also found that adolescents tended to have an advantage compared with adults and children when learning from volatile outcomes. We discuss potential mechanisms explaining these age-related differences and conclude by outlining future research directions.
Project description:The aim of this study was to empirically investigate differences in role expectations, among the stakeholders involved, about the devolved personnel management role of front-line managers (FLMs). In particular, we researched the role expectation differences between FLMs, their middle managers, and Human Resource (HR) practitioners. In total, nineteen semi-structured interviews have been conducted involving eleven FLMs, eight middle managers, and two HR practitioners working at the same Dutch hospital. Most discovered role expectation differences were related to how FLMs should execute their HR tasks (i.e., process ambiguity). FLMs were often uncertain if their role enactment met those of their middle managers and/or HR practitioners, herewith indicating role stress. Our findings underline the importance of paying attention to role expectations' differences in aligning components of the HRM-performance relationship. Future research could include the role expectations of other important stakeholders, such as: subordinates and top management. The outcomes of this empirical work are translated into four interventions to diminish FLMs' role stress.
Project description:Terrorism is a major problem worldwide, causing thousands of fatalities and billions of dollars in damage every year. To address this threat, we propose a novel feature representation method and evaluate machine learning models that learn from localized news data in order to predict whether a terrorist attack will occur on a given calendar date and in a given state. The best model (a Random Forest aided by a novel variable-length moving average method) achieved area under the receiver operating characteristic (AUROC) of ≥ 0.667 (statistically significant w.r.t. random guessing with p ≤ .0001) on four of the five states that were impacted most by terrorism between 2015 and 2018. These results demonstrate that treating terrorism as a set of independent events, rather than as a continuous process, is a fruitful approach-especially when historical events are sparse and dissimilar-and that large-scale news data contains information that is useful for terrorism prediction. Our analysis also suggests that predictive models should be localized (i.e., state models should be independently designed, trained, and evaluated) and that the characteristics of individual attacks (e.g., responsible group or weapon type) were not correlated with prediction success. These contributions provide a foundation for the use of machine learning in efforts against terrorism in the United States and beyond.
Project description:To the best of our knowledge, we report here for the first time a case of exploding head syndrome (EHS) that caused repeating panic attacks. A 62-year-old woman experienced a sudden sensation of a loud noise just before going to sleep. The frequency of these episodes rapidly increased to multiple times per night, and she soon began to fear sleep, which led to the occurrence of nighttime panic attacks. She was diagnosed with EHS at our sleep clinic, and clonazepam was prescribed accompanied by reassurance about the benign nature of this syndrome. The intensity of the loud noise gradually reduced, and her fear of sleep and panic attacks disappeared at around the same time. In this report, we argue the importance of gaining further knowledge about EHS, including that about complicating psychiatric symptoms and that about its treatment.
Project description:Human infants show systematic responses to events that violate their expectations. Can they also revise these expectations based on others' expressions of surprise? Here we ask whether infants (N = 156, mean = 15.2 months, range: 12.0-18.0 months) can use an experimenter's expression of surprise to revise their own expectations about statistically probable vs. improbable events. An experimenter sampled a ball from a box of red and white balls and briefly displayed either a surprised or an unsurprised expression at the outcome before revealing it to the infant. Following an unsurprised expression, the results were consistent with prior work; infants looked longer at a statistically improbable outcome than a probable outcome. Following a surprised expression, however, this standard pattern disappeared or was even reversed. These results suggest that even before infants can observe the unexpected events themselves, they can use others' surprise to expect the unexpected. Starting early in life, human learners can leverage social information that signals others' prediction error to update their own predictions.
Project description:COVID-19 poses a unique set of challenges to the healthcare system due to its rapid spread, intensive resource utilization, and relatively high morbidity and mortality. Healthcare workers are at especially high risk of exposure given the viruses spread through close contact. Reported cardiac complications of COVID-19 include myocarditis, acute coronary syndrome, cardiomyopathy, pericardial effusion, arrhythmia, and shock. Thus, echocardiography is integral in the timely diagnosis and clinical management of COVID-19 patients. Rush University Medical Center has been at the forefront of the COVID-19 response in Illinois with high numbers of cases reported in Chicago and surrounding areas. The echocardiography laboratory at Rush University Medical Center (RUMC) proactively took numerous steps to balance the imaging needs of a busy, nearly 700-bed academic medical center while maintaining safety.
Project description:Stress is an increasingly prevalent mental health condition that can have serious effects on human health. The development of stress prediction tools would greatly benefit public health by allowing policy initiatives and early stress-reducing interventions. The advent of mobile health technologies including smartphones and smartwatches has made it possible to collect objective, real-time, and continuous health data. We sought to pilot the collection of heart rate variability data from the Apple Watch electrocardiograph (ECG) sensor and apply machine learning techniques to develop a stress prediction tool. Random Forest (RF) and Support Vector Machines (SVM) were used to model stress based on ECG measurements and stress questionnaire data collected from 33 study participants. Data were stratified into socio-demographic classes to further explore our prediction model. Overall, the RF model performed slightly better than SVM, with results having an accuracy within the low end of state-of-the-art. Our models showed specificity in their capacity to assess "no stress" states but were less successful at capturing "stress" states. Overall, the results presented here suggest that, with further development and refinement, Apple Watch ECG sensor data could be used to develop a stress prediction tool. A wearable device capable of continuous, real-time stress monitoring would enable individuals to respond early to changes in their mental health. Furthermore, large-scale data collection from such devices would inform public health initiatives and policies.
Project description:The inherited and acquired long QT is a risk marker for potential serious cardiac arrhythmias and sudden cardiac death. Smartwatches are becoming more popular and are increasingly used for monitoring human health. The present study aimed to assess the feasibility and reliability of evaluating the QT interval in lead I, lead II, and V2 lead using a commercially available Apple Watch. One hundred nineteen patients admitted to our Cardiology Division were studied. I, II, and V2 leads were obtained after recording a standard 12-lead ECG. Lead I was recorded with the smartwatch on the left wrist and the right index finger on the crown. Lead II was obtained with the smartwatch on the left lower abdomen and the right index finger on the crown. The V2 lead was recorded with the smartwatch in the fourth intercostal space left parasternal with the right index finger on the crown. There was agreement among the QT intervals of I, II, and V2 leads and the QT mean using the smartwatch and the standard ECG with Spearman's correlations of 0.886; 0.881; 0.793; and 0.914 (p < 0.001), respectively. The reliability of the QTc measurements between standard and smartwatch ECG was also demonstrated with a Bland-Altman analysis using different formulas. These data show that a smartwatch can feasibly and reliably assess QT interval. These results could have an important clinical impact when frequent QT interval monitoring is required.