Simulated Casualties and Medics for Emergency Training

The MediSim system extends virtual environment technology to allow medical personnel to interact with and train on simulated casualties. The casualty model employs a three-dimensional animated human body that displays appropriate physical and behavioral responses to injury and/or treatment. Medical corpsmen behaviors were developed to allow the actions of simulated medical personnel to conform to both military practice and medical protocols during patient assessment and stabilization. A trainee may initiate medic actions through a mouse and menu interface; a VR interface has also been created by Stansfield\u27s research group at Sandia National Labs

A survey of computational models for blast induced human injuries for security and defence applications

Explosions and blast waves can cause human injuries due to both the interaction of the blast waves with the human tissues and the generated and propelled fragments that strike the body. Several models for the prediction of these injurious effects have been reviewed in this report. They have been selected from those most widely established and more suitable for implementation in finite element codes for risk assessment in large-scale numerical simulations. The models examined have been presented according to the established three categories of primary, secondary and tertiary injury mechanisms. They are probabilistic and their probit (or logistic) functions are presented and explained. Appropriate pressure-impulse and mass-velocity diagrams are drawn for blast waves and fragment injuries, respectively. Comparisons of injury criteria predictions are made, and some merits or shortcomings of the models are indicated. A workable model of munition fragmentation is also included. Thus the report provides the means for an efficient assessment of human injury risk in case of explosion events, which can effectively contribute to refining methods of protection in security and defence.JRC.E.4-Safety and Security of Building

An analysis of civil aviation industry safety needs for the introduction of liquid hydrogen propulsion technology

Over the next few decades air travel is predicted to grow, with international agencies, manufacturers and governments predicting a considerable increase in aviation use. However, based on current fuel type, International Civil Aviation Organization (ICAO) project emissions from aviation are estimated to be seven to ten times higher in 2050 than in 1990. These conflicting needs are problematic and have led to the EU Flightpath 2050 targeting dramatic emissions reductions for the sector (75% CO2, 90% NOX by 2050). One proposed solution, decreasing carbon emissions without stunting the increase in air travel, is hydrogen propulsion; a technology with clear environmental benefits. However, enabling the safe application of this fuel to aviation systems and industrial infrastructure would be a significant challenge. High-profile catastrophic incidents involving hydrogen, and the flammable and cryogenic nature of liquid hydrogen (LH2) have led to its reputation as a more dangerous substance than existing or alternative fuels. But, where they are used (in industry, transport, energy), with sufficient protocols, hydrogen can have a similar level of safety to other fuels. A knowledge of hazards, risks and the management of these becomes key to the integration of any new technology. Using assessments, and a gap analysis approach, this paper examines the civil aviation industry requirements, from a safety perspective, for the introduction of LH2 fuel use. Specific proposed technology assessments are used to analyze incident likelihood, consequence impact, and ease of remediation for hazards in LH2 systems, and a gap analysis approach is utilized to identify if existing data is sufficient for reliable technology safety assessment. Outstanding industry needs are exposed by both examining challenges that have been identified in transport and industrial areas, and by identifying the gaps in current knowledge that are preventing credible assessment, reliable comparison to other fuels and the development of engineering systems. This paper demonstrates that while hydrogen can be a safe and environmentally friendly fuel option, a significant amount of work is required for the implementation of LH2 technology from a mass market perspective

Doctor of Philosophy

dissertationHigh-count microelectrode arrays implanted in peripheral nerves could restore motor function after spinal cord injury or sensory function after limb loss via electrical stimulation. The same device could also help restore volitional control to a prosthesis-using amputee, or sensation to a Spinal cord Injury (SCI) patient, via recordings from the still-viable peripheral nerves. The overall objective of these dissertations studies is to improve the usefulness of intrafascicular electrodes, such as the Utah Slanted Electrode Array (USEA), for neuroprosthetic devices for limb loss or spinal cord injury patients. Previous work in cat sciatic nerve has shown that stimulation through the USEA can remain viable for months after implant. However, stimulation parameters were not stable, and recordings were lost rapidly and were subject to strong contamination by myoelectrical activity from adjacent muscles. Recent research has shown that even when mobility is restored to a patient, either through prosthesis or functional electrical stimulation, difficulties in using the affected limbs arise from the lack of sensory input. In the absence of the usual proprioceptive and cutaneous inputs from the limb, planning and executing motions can be challenging and sometimes lead to the user's abandonment of prostheses. To begin to address this need, I examined the ability of USEAs in cat hindlimb nerves to activate primary sensory fibers by monitoring evoked potentials in somatosensory cortex via skull-screw electrodes. I iv also monitored evoked EMG responses, and determined that it is possible to recruit sensory or motor responses independently of one another. In the second study of this dissertation, I sought to improve the long-term stability of USEAs in the PNS by physically and electrically stabilizing and protecting the array. To demonstrate the efficacy of the stabilization and shielding technique, I examined the recording capabilities of USEA electrodes and their selectivity of muscle activation over the long term in cat sciatic nerve. In addition to long-term viability, clinically useful neuroprosthetic devices will have to be capable of interfacing with complex motor systems such as the human hand. To extend previous results of USEAs in cat hindlimb nerves and to examine selectivity when interfacing with a complex sensorimotor system, I characterized EMG and cortical somatosensory responses to acute USEA stimulation in monkey arm nerves. Then, to demonstrate the functional usefulness of stimulation through the USEA. I used multi-array, multi-electrode stimulation to generate a natural, coordinated grasp

Modeling Decision Making In Trauma Centers From The Standpoint Of Complex Adaptive Systems

This research examines complex clinical decision-making processes in trauma center units of hospitals in terms of the impact of complexity on the medical team involved in the trauma event. The science of complex adaptive systems together with human judgment theories provide important concepts and tools for responding to health care challenges in this century and beyond. Clinical decision-makers in trauma centers are placed in urgent and anxious situations that are increasingly complex, making decision-making and problem-solving processes multifaceted. Under stressful circumstances, physicians must derive their decision-making schemas (―internal models‖ or ―mental models‖) without the benefits of judicious identification, evaluation, and/or application of relevant medical information, and always using fragmentary data. This research developed a model of decision-making processes in trauma events that uses a Bayesian Classifier model jointly with Convolution and Deconvolution operators to study real-time observed trauma data for decision-making processes under stress. The objective was to explore and explain physicians‘ decision-making processes during actual trauma events while under the stress of time constraints and lack of data. The research addresses important operations that describe the behavior of a dynamic system resulting from stress placed on the physician‘s rational decision making processes by the conditions of the environment. Deconvolution, that is, determining the impulse response of the system, is used to understand how physicians clear out extraneous environmental noise in order to have a clearer picture of their mental models and reach a diagnosis or diagnostic course of action

Developing neuroimaging biomarkers of blast-induced traumatic brain injury

In the past two decades, the awareness of the physical and emotional effects and sequalae of traumatic brain injuries (TBI) has grown considerably, especially in the case of soldiers returning from their deployment in Iraq and Afghanistan, after sustaining blast-induced TBI (bTBI). While the understanding of bTBI and how it compares to civilian non-blast TBI is essential for proper prevention, diagnosis and treatment, it is currently limited, especially in human in-vivo studies. Developing neuroimaging biomarkers of bTBI is key in understanding primary blast injury mechanism. I therefore investigated the patterns of white matter and grey matter injuries that are specific to bTBI and aren¶t commonl\ seen in civilians Zho suffered from head trauma using advanced neuroimaging techniques. However, because of significant methodological issues and limitations, I developed and tested a new pipeline capable of running the analysis of white matter abnormalities in soldiers, called subject-specific diffusion segmentation (SSDS). I also used standard methodologies to investigate changes at the level of the grey matter structures, and more particularly the limbic system. Finally, I trained a machine learning algorithm that builds decision trees with the aim of classifying between patients with TBI and controls, and between different TBI mechanisms as an example of what could potentially be applied in the context of bTBI. I found three main neuroimaging biomarkers specific to bTBI. The first one is a microstructural white matter abnormality at the level of the middle cerebellar peduncle, characterized by a decrease of diffusivity measures. The second is also a decrease in diffusivity properties, at the level of the white matter boundary, and the third one is a loss of hippocampal volume, with no association to post-traumatic stress disorder. Finally, I demonstrated that SSDS can be used in tandem with a machine learning algorithm for potential diagnosis of TBI with high accuracy. These findings provide mechanistic insights into bTBI and the effect of primary blast injuries on the human brain. This work also identifies important neuroimaging biomarkers that might facilitate prevention and diagnosis in soldiers who suffered from bTBI.Open Acces

Bayesian Networks for Evidence Based Clinical Decision Support.

PhDEvidence based medicine (EBM) is defined as the use of best available evidence for decision making, and it has been the predominant paradigm in clinical decision making for the last 20 years. EBM requires evidence from multiple sources to be combined, as published results may not be directly applicable to individual patients. For example, randomised controlled trials (RCT) often exclude patients with comorbidities, so a clinician has to combine the results of the RCT with evidence about comorbidities using his clinical knowledge of how disease, treatment and comorbidities interact with each other. Bayesian networks (BN) are well suited for assisting clinicians making evidence-based decisions as they can combine knowledge, data and other sources of evidence. The graphical structure of BN is suitable for representing knowledge about the mechanisms linking diseases, treatments and comorbidities and the strength of relations in this structure can be learned from data and published results. However, there is still a lack of techniques that systematically use knowledge, data and published results together to build BNs. This thesis advances techniques for using knowledge, data and published results to develop and refine BNs for assisting clinical decision-making. In particular, the thesis presents four novel contributions. First, it proposes a method of combining knowledge and data to build BNs that reason in a way that is consistent with knowledge and data by allowing the BN model to include variables that cannot be measured directly. Second, it proposes techniques to build BNs that provide decision support by combining the evidence from meta-analysis of published studies with clinical knowledge and data. Third, it presents an evidence framework that supplements clinical BNs by representing the description and source of medical evidence supporting each element of a BN. Fourth, it proposes a knowledge engineering method for abstracting a BN structure by showing how each abstraction operation changes knowledge encoded in the structure. These novel techniques are illustrated by a clinical case-study in trauma-care. The aim of the case-study is to provide decision support in treatment of mangled extremities by using clinical expertise, data and published evidence about the subject. The case study is done in collaboration with the trauma unit of the Royal London Hospital

Geeniinfo väärtus südame-veresoonkonnahaiguste riski hindamisel

Väitekirja elektrooniline versioon ei sisalda publiktasiooneFakt, et südame-veresoonkonnahaigused on peamiseks suremuse põhjustajaks maailmas, rõhutab vajadust edendada ja täiustada olemasolevaid haiguse ennetus- ja ennustusstrateegiaid. Südame-veresoonkonnahaiguste riski hindamine põhineb tänases kliinilises praktikas klassikalisi fenotüübilisi riskitegureid arvestavatel riski hindamise mudelitel. Kuigi nimetatud strateegia võimaldab kõrge riskiga indiviide suhteliselt hästi tuvastada, jääb pea kolmandiku riski hinnang ebatäpseks ning ravimääramine ebaselgeks. Lisaks eelnevale peegeldub mudelite piiratud kasutus selles, et riskifaktorite loetlemisega hinnatakse tegelikkuses molekulaarsel tasandil juba toimunud muutusi. Seega leevendatakse praeguse strateegia kasutamisel pigem patoloogia progresseerunud kulgu, kui pärsitakse või ennetatakse molekulaarsete mehhanismide häirumist varases staadiumis. Üheks võimalikuks edasiarenduse meetmeks pakutakse haiguse geneetilise informatsiooni arvestamist. Seda eeskätt seetõttu, et südame-veresoonkonnahaiguste geneetiliste seoste uuringutega on täna jõutud hinnanguteni, millel on potentsiaali muuta oluliselt täpsemaks nii tervete indiviidide varast haigusriski hindamist kui ka haigete kliinilist käsitlust. Selle doktoritöö peamiseks eesmärgiks on anda ülevaade tänastest südame-veresoonkonnahaiguste riski hindamise meetmetest ning sellest, kas ja kuidas geneetilise informatsiooni kaasamine igapäeva kliinilistesse otsustesse neid edendada võiks. Lisaks toon näiteid, kuidas kõrge resolutsiooniga genoomi järjestusandmestik võimaldaks tunnusega seotud põhjuslikke geenivariante täpsemini tuvastada ning kuidas populatsiooni-põhise biopanga andmete kasutamine tõhustaks kõrge riskiga indiviidide kliinilist käsitlust.Cardiovascular diseases are the main cause of morbidity and mortality worldwide, underscoring the requisite for improved strategies for disease prevention and risk prediction. The main approach applied in today's clinical practice to identify those at increased cardiovascular risk relies on the utilization of phenotypic risk models that facilitate the estimation of one's disease risk based on traditional risk factors. While this strategy is beneficial for avoiding disease incidence and it does on the whole target individuals at high risk for treatment sufficiently well, a third of individuals, who experience an adverse event, are misclassified into a lower risk category and are therefore advocated treatment ambiguously. Importantly, the current approach lacks in providing accurate estimation for primordial prevention, that is estimating risk before risk factors emerge. To overcome this issue and seek for approaches to enhance risk estimation, attention has now been turned to genetics with the aim of incorporating genetic information into established risk prediction strategies. The scrutiny of the genetic architecture of cardiovascular diseases conducted in recent decades has today resulted in estimates that can be of clinical utility and value. This doctoral thesis aims to give an overview of the status quo of the genomic research on cardiovascular diseases and contemplate on what the advances in molecular technology, computational capacities and large-scale initiatives have enabled, what the progress of these endeavours entail and whether these do bestow incremental value for clinical utility. Furthermore, I will bring examples of how the utilization of high-coverage sequencing data can enhance the search for the genetic underpinnings of cardiovascular disease-associated phenotypes, and how the use of large-scale cohorts and population-based biobanks can enable the anticipated improvement in disease risk estimation, especially when integrated into a national healthcare system.https://www.ester.ee/record=b522706

Do Response Times Matter? The Impact of EMS Response Times on Health Outcomes

The introduction of technology aimed at reducing the response times of emergency medical services has been one of the principal innovations in crisis care over the last several decades. These substantial investments have typically been justified by an assumed link between shorter response times and improved health outcomes. But, current medical research does not actually show a significant relationship between response time and mortality. In this study, I explain the discrepancy between conventional wisdom and current medical research; existing research fails to account for the endogeneity of incident severity and response time. Analyzing detailed call-level information from the state of Utah's Bureau of Emergency Medical Services, I measure the impact of response time on mortality and hospital utilization using the distance of the incident from the nearest EMS agency headquarters as an instrument for response time. I find that response times significantly affect mortality, but not hospital utilization. A cost benefit analysis suggests that the anticipated benefits of a response time reduction exceed the costs and I discuss free-rider problems that might be responsible for the inefficiently high response times I observe.Emergency Medical Services, response time, mortality, cost-benefit analysis, free-rider

Acute inflammation and infection: the effects on recovery following moderate-to-severe traumatic brain injury

Current thinking by Traumatic Brain Injury (TBI) researchers and clinicians has devolved from the idea that TBI is an event with a finite recovery period, and have shifted to considering TBI a chronic disease with long-term implications for health. Therefore, there is great interest in determining acute biological and clinical factors that influence long-term health and function after injury. This interest drives the two central themes of this dissertation, to better understand: 1) the continuum of TBI disability from acute to chronic recovery; 2) the effects of non-neurological factors on recovery from TBI. Notably, the availability of data that spans the TBI disability continuum—from early stages post-injury to death—is sparse. Aim 1 of this dissertation explains a probabilistic marching procedure used to merge two databases, the National Trauma Databank and TBI Model Systems, which creates an infrastructure to examine the long-term effects of relevant acute care variables. In aim 2, the merged dataset is leveraged to assess the negative effects of acute care hospital-acquired pneumonia (HAP) on long-term global disability and health care utilization. HAP is one example of a non-neurological factor that impacts TBI recovery. Aim 3 focuses on two systemic markers of inflammation and hormone dysfunction, tumor necrosis factor-alpha (TNFα) and estradiol (E2), and assesses their inter-relationship acutely after injury, and their temporal relationship to mortality. The public health implications of the work herein provide observational data to better understand the continuum of TBI disability, and major non-neurological contributors to recovery from injury