Surgical assessment of the geriatric oncology patient

BACKGROUND: The aging population in the United States will correlate with an increased number of cancer diagnoses as cancer is primarily a disease of the elderly. Providing this ever-growing group of individuals with quality surgical management, while taking into account the unique needs and desires of this cohort, is a great challenge facing both geriatricians and surgeons going forward. The best approach to ensure that oncogeriatric patients receive the best tailored treatment is through the completion of a pre-surgical geriatric assessment. However, only a minority of oncogeriatric patients is undergoing a comprehensive pre-surgical geriatric assessment despite the majority of geriatricians and surgeons acknowledging its importance in order to properly risk stratify their patients. LITERATURE REVIEW FINDINGS: Multiple theories exist as to why geriatric assessments are not being utilized more frequently, but the most probable answer is that these assessments are very time-consuming, making it virtually impossible for incorporation into a healthcare provider’s busy schedule. Comprehensive literature review regarding geriatric assessments amongst the oncogeriatric population found that the most sensitive and specific domains of the geriatric assessments predicting morbidity and mortality include Frailty Index, Social Support Survey, Mini-Nutritional Assessment, and Geriatric Depression Screening. PROPOSED METHODS: A novel educational intervention will be proposed to teach Physician Assistant and Medical Students about the domains of the geriatric assessment most predictive of post-surgical risk during their surgical clerkship. The curriculum will utilize both simulation- and competency-based education training under the guidance of geriatricians and surgeons. Students will first learn the necessary skills in a controlled classroom environment and then proceed to incorporate these skills during their clerkship with patients on their service. CONCLUSIONS: The goal of the proposed method is to instill the confidence and skills necessary to provide an accurate geriatric assessment for oncogeriatric patients in future clinicians. The field of geriatric oncology is going to grow exponentially in the up-coming years and familiarizing future clinicians with the most predictive domains regarding surgical outcome will improve treatment outcomes for oncogeriatric patients in the immediate and foreseeable future

Efficient Journey Planning and Congestion Prediction Through Deep Learning

The advancements of technology continuously rising over the years has seen many applications that are useful in providing users with sufficient information to make better journey plans on their own. However, commuters still find themselves going through congested routes every day to get to their destinations. This paper attempts to delineate the possibilities of improving urban mobility through big data processing and deep-learning models. Essentially, through a predictive model to predict congestion and its duration, this paper aims to develop and validate a functional journey planning mobile application that can predict traffic conditions, allowing road users to make better informed decisions to their travel plans. This paper proposes a Multi-Layered Perceptron (MLP) deep learning model for congestion prediction and supplements a Linear Regression (LR) model to predict its duration. The proposed MLP-LR model performed reasonably well with an accuracy of 63% in predicting an occurrence of congestion. Some critical discussions on further research opportunities stemming from this study is also presented

Visualization of Vibrant Cities and Regions – Identification, Design and Development of 3D-GIS Applications and Modules

Facing a continuous state of transition and herewith connected financial, societal and ecological challenges such as the climate or demographic change (United Nations 2013), cities try to integrate innovative information and communication technologies in order to optimize administrative processes, legitimize decision making and to involve all relevant local actors into processes of public relevance. In this context 3D-GIS-models offer various not yet exploited potentials for all named levels of interest. This paper presents an overview over existing application fields for 3D-GIS-solutions, further proposing a categorization in order to be able to develop and implement target-oriented solutions. Moreover, this paper presents the project activities of the Fraunhofer IAO, the city of Cologne and the provider of geo-information-services ESRI, designing and developing end-user oriented applications for the 3D-GIS-tool CityEngine. Therefore various city departments such as the agencies for city planning, traffic and environment were involved in an iterative process in order to identify potential application fields and their benefits within the administrative work as well as their andvantages regarding existing solutions and processes. Aditionally, the particpants decided upon a set of focus applications to be developed within the project. Therefore, this document will concentrate on the potential benefits of the identified and cooperatively designed application fields, further outlining the first steps of the deleopment phase of the citizen particpiation application

Co-Evolving Built Environments and Mobile Autonomy for Future Transport and Mobility

What would an autonomous vehicle’s dream city look like? Autonomous navigation and automated mobility are hard problem that are compounded by the unpredictability and dynamics of urban environment. As we re-think and re-design our built environments, we have the unique opportunity to transform them so that automated mobility systems a can perform efficiently and robustly. The main idea behind this project is to tackle the challenges of autonomous navigation and automated mobility by explicitly considering the coupling of the autonomous vehicles with the environments that they operate in

Selecting the Most Feasible Construction Phasing Plans for Urban Highway Rehabilitation Projects

Despite the abundance of research that has aimed to understand the effects of highway work zones, very little definitive information is available concerning the determination of work zone length (WZL). Quantitative studies that holistically model WZL are very rare. To fill this gap, this study identifies critical factors affecting WZL and develops decision support models that determine the optimal WZL in a balanced tradeoff between motorists’ inconvenience due to traffic disruption and their opportunity cost. A high-confidence dataset was created by conducting a series of scheduling and traffic simulations and analyses. The results revealed that traffic loading and work zone duration are critical factors, with traffic loading at approximately 41,000 vehicles-per-day being an important benchmarking point. Based on these findings, a decision support model was developed to determine the most feasible WZL. As the first of its kind, this study will help state transportation agencies devise sounder construction phasing plans by providing a point of reference when establishing WZL in a viable way to minimize traffic disruption during construction