The teaching process of auscultation is complex in itself, and difficult to operate since it requires a wide spectrum of patients with the most diverse cardiopulmonary pathologies, readily available during teaching and assessment hours, for an ever-growing number of medical students. In this paper we will focus on how virtual patient technologies can promote the evolution of the current teaching methodologies, promoting better learning. The chosen methodology was a) a review of available medical simulation technologies for auscultation teaching; b) a case study illustrating how a virtual patient simulation technology has been successfully used to teach and certify auscultation skills. Results show the positive impact and high acceptability of virtual patient simulation technologies in the teaching of auscultation to medical students.Biomedical Engineering as an undergraduate degree in Latin America is not new. However, most programs have the objective to produce professionals dedicated to the management and maintenance of health care technology. We believe that there is an important area of opportunity in the education of engineers who are competent in the design and development of medical devices. Among the 100 programs in the region there could be a few which could stand out as providers of such professionals. This work proposes a curricular structure to fulfill these aims.In order to teach different modeling techniques we demonstrate equation-based, block-schema based, compartment and component-based modeling using acausal and object-oriented modeling language – Modelica. Hands-on implementation using all these techniques and comparing them towards same system (in our case glucose-insulin regulation) we teach pros and cons of each technique. Equation-based or block-schema based may be rapidly implemented from literature. However, compartment based or component-based models brings better understanding of modeled reality. When students have such experience, they tend to assess published papers more critically and do more complex system analysis.In this project, a fully functional incubator with precise control with respect to temperature, humidity, and airflow was developed and assessed. In parallel with the development of the incubator, a heuristic simulation was created to test and tune the Mamdani fuzzy logic controller. The controller was then applied to the incubator prototype.To bridge the gap between the biological sciences (typically female-dominated) and engineering (typically male-dominated), biomedical engineering (BME) activities could potentially be used as a vehicle to alter female students’ perception of engineering as a whole. Female’s pursuit of STEM (Science, Technology, Engineering and Math) degrees is typically confined to the biological sciences and females earn a high proportion of degrees in nursing, psychology and the social sciences, yet male presence persists in physical sciences and engineering. Female’s participation in engineering remains much lower than men at all degree levels. Here, research questions included do female high school students 1) perceive engineering as relevant? 2) have an interest & aptitude towards exploring engineering in college and as a career? 3) have anxiety in terms of engineering? 4) have engineering “role-confidence”? Participants, a randomly selected pool of 28 high school students (almost exclusively female from schools throughout the DC Metro area) took part in a week-long, all-day workshop where they were exposed to female engineering mentors, peers, and activities tied to BME & Engineering. Pre and post surveys, adapted from standard STEM surveys, were administered to the pool of participants. Increases in confidence and interest in engineering and decreased anxiety were observed following female high school students’ participation in hands-on activities in BME.Cardiovascular diseases (CVDs) remain responsible for millions of deaths annually. Myocardial infarction (MI) is the most prevalent condition among CVDs. Although datadriven approaches have been applied to predict CVDs from ECG signals, comparatively little work has been done on the use of multiple-lead ECG traces and their efficient integration to diagnose CVDs. In this paper, we propose an end-to-end trainable and joint spectral-longitudinal model to predict heart attack using data-level fusion of multiple ECG leads. Selleckchem Bemcentinib The spectral stage transforms the time-series waveforms to stacked spectrograms and encodes the frequency-time characteristics, whilst the longitudinal model helps to utilise the temporal dependency that exists in these waveforms using recurrent networks. We validate the proposed approach using a public MI dataset. Our results show that the proposed spectrallongitudinal model achieves the highest performance compared to the baseline methods.Accurately monitoring and modeling smoking behavior in real life settings is critical for designing and delivering appropriate smoking-cessation interventions through mHealth applications. In this paper, we inspect smoking patterns based on data collected from 52 volunteers during a 4-week period of their everyday lives. These data are acquired by an automatic data acquisition system comprising an electric lighter, two wearable sensors and one mobile phone, which together can automatically track smoking events, collect concurrent context and physiology, and trigger pop-up questionnaires. We visualize temporal patterns of smoking at the level of the week, day and time of the day. Statistical analysis on all subjects has demonstrated significant differences at the levels evaluated. Distinct emotions during smoking at individual level are also found. Quantified smoking patterns can upgrade our understanding of individual behaviors and contribute to optimizing intervention plans.Dyskinesias are abnormal involuntary movements that patients with mid-stage and advanced Parkinson’s disease (PD) may suffer from. These troublesome motor impairments are reduced by adjusting the dose or frequency of medication levodopa. However, to make a successful adjustment, the treating physician needs information about the severity rating of dyskinesia as patients experience in their natural living environment. In this work, we used movement data collected from the upper and lower extremities of PD patients along with a deep model based on Long Short-Term Memory to estimate the severity of dyskinesia. We trained and validated our model on a dataset of 14 PD subjects with dyskinesia. The subjects performed a variety of daily living activities while their dyskinesia severity was rated by a neurologist. The estimated dyskinesia severity ratings from our developed model highly correlated with the neurologist-rated dyskinesia scores (r=0.86 (p less then 0.001) and 1.77 MAE (6%)) indicating the potential of the developed the approach in providing the information required for effective medication adjustments for dyskinesia management.