Hermansen Dickens (wrenchjam5)

To translate recent advances in medical device interoperability research into clinical practice, standards are being developed that specify precise requirements towards the network representation of particular medical devices connecting through ISO/IEEE 11073 SDC. The present contribution supplements this protocol standard with specific models for endoscopic camera systems, light sources, insufflators, and pumps. Through industry consensus, these new standards provide modular means to describe the devices' capabilities and modes of interaction in a service-oriented medical device communication architecture. SJ6986 This enables seamless data exchange and the potential for new assistive systems to support the caregiver.Critical care units internationally contain medical devices that generate Big Data in the form of high speed physiological data streams. Great opportunities exist for systemic and reliable approaches for the analysis of high speed physiological data for clinical decision support. This paper presents the instantiation of a Big Data analytics based Health Analytics as-a-Service model. The availability results of the deployment of two instances of Artemis Cloud to support two neonatal ICUs (NICUs) in Ontario Canada are presented.Cerebellar Ataxia is a neurological disorder without an approved treatment. Patients will have impaired and uncoordinated motor functionality making them unable to complete their day-to-day activities. Ataxia clinics are established around the world to facilitate research and rehabilitate patients. However, the patients are generally evaluated by human - observation. Therefore, machine learning based data analysis is popular on motion captured via sensors. There are many neurological tests designed to analyse the motor impairments in different domains (such as upper limb, lower limb, gait, balance and speech). Clinicians follow scoring protocols to record the severity of patients for each domain test. This paper delivers a clinical assessment platform combining 12 neurological tests in 5 domains. It captures motion (from BioKin sensors), haptic and audio data (from the tablet or laptop screen). A data analysis system is hosted in a remote server which evaluates data to produce a severity score via different models built for each neurological test. The assessment platform clients and server communicate via a cloud buffer system. The scores input by the clinicians and predicted by the machine learning models are logged in the cloud database. This enables clinicians and doctors to view and compare the history of patient diagnosis. The server system is structured for automated score model upgrades via prompted approval. Thus, the most viable scoring model could be accommodated for each test based on longitudinal studies.Bipolar Disorder is a common mental illness affecting millions of people worldwide. It is most commonly presented as periods of depressive lows and manic highs, both of which can be extremely uncomfortable and distressing for the individual affected. Existing bipolar patient monitoring relies on subjective self-reports, which are inaccurate and biased. Moreover, many symptoms are not easily recognized or are ignored by the patient, resulting in a loss of information and misleading reports. To achieve reliable daily monitoring of dysfunctional behaviors, we propose a system mDB that uses a mobile phone to monitor a variety of symptomatic activities, in the hopes of improving care and quality of life for these individuals.In this work, a novel open-source dataset for noninvasive fetal electrocardiography research is presented. It is composed of 60 high-quality electrophysiological recordings acquired between the 21st and the 27th weeks of gestation. For each acquisition, whose average duration is 30.5 s, 24 unipolar abdominal leads and three bipolar thoracic leads were included, along with a maternal respiration signal collected by a thoracic resistive belt. The chosen electrode