Using openEHR Archetypes for Automated Extraction of Numerical Information from Clinical Narratives

Up to 80% of medical information is documented by unstructured data such as clinical reports written in natural language. Such data is called unstructured because the information it contains cannot be retrieved automatically as straightforward as from structured data. However, we assume that the use of this flexible kind of documentation will remain a substantial part of a patient’s medical record, so that clinical information systems have to deal appropriately with this type of information description. On the other hand, there are efforts to achieve semantic interoperability between clinical application systems through information modelling concepts like HL7 FHIR or openEHR. Considering this, we propose an approach to transform unstructured documented information into openEHR archetypes. Furthermore, we aim to support the field of clinical text mining by recognizing and publishing the connections between openEHR archetypes and heterogeneous phrasings. We have evaluated our method by extracting the values to three openEHR archetypes from unstructured documents in English and German language

Changes to the energy policy landscape and potential impacts on Scotland's consumers: distributional impact modelling

This study looks at the consumer impacts of future changes in the energy landscape. It builds on work mapping out these changes and reviewing the literature on consumer impacts – both positive and negative. We have modelled the distributional consumer impacts – or who will be impacted, where, and by how much – across a subset of key policies. Impacts are presented by consumer segmentation type, employing a bespoke segmentation model developed for the Scottish Government for this purpose

Energy futures of representative Swiss communities under the influence of urban development, building retrofit, and climate change

Reducing energy demand in buildings is an integral part of many climate change mitigation strategies. Yet, the prospected development of communities is often overlooked when estimating future energy demand. Here, we investigate the future energy demand in representative Swiss communities, considering climate change projections, building retrofit and urban development. Following a scenario-based approach we model urban, suburban and rural community archetypes under changing boundary conditions and different time scales using the City Energy Analyst an open-source computational framework. The results demonstrate that the future energy demand of Swiss communities is highly dependant on their development trajectories regarding population growth, occupant density and building use-types. For the urban archetype, the most significant result is the increase of annual space cooling which by 2060 could be comparable to space heating. For the sub-urban, increases in energy demand due to urban development were observed despite retrofit measures, whereas the rural archetype displays high space heating demand across all scenarios. Consequently, predictions for future energy demand at the community scale without considering urban development trajectories are likely to be incomplete. The results demonstrate the relevance of increasing the modelling scale from national to community scale to support decision making on different levels of governance

Modelling District Heating in a Renewable Electricity System

With the decarbonisation of electricity generation, large scale heat pumps are becoming increasingly viable for district heating combined with thermal energy storage, district heating can provide flexibility to the electricity grid by decoupling demand from supply. This thesis examines how district heating with heat pumps and thermal energy storage can integrate with and provide a benefit to an electricity system with predominantly renewable generation. The scope of work comprises three interlinked models underpinned by the same set of meteorology data, fundamentally coupling supply and demand. First, heat load data are surveyed, and an hourly demand profile is simulated. Disaggregation of district heating loads from the national demand is accomplished via segmentation of the building stock to model heat demand at high spatiotemporal resolution. Second, a novel method of pricing hourly electricity in a zero carbon, capital-intensive renewable system with electricity storage is developed and applied to a dispatch simulation to generate hourly electricity prices. Third, a dynamic model of district heating is constructed to simulate the meeting of heat loads with different design configurations using electricity as the energy source. Model predictive control is applied with varying forecast horizons so as to minimise the cost of electricity to meet the heat demand given a time series of hourly prices and consequently optimising the capacity of thermal energy storage. It was found that a thermal energy storage capacity equivalent to 1.3% of annual demand is sufficient to minimise operating costs. Finally, the potential impact of district heating on balancing the electricity system is analysed and an equivalence between thermal and electric storage is examined. While this is highly dependent on annual conditions, it can be as much as 3.5 units of thermal storage for every unit of electrical grid storage on the system. This could potentially reduce the investment in grid storage by £36 billion, underlining the significant financial benefits of thermal storage to the whole system. The research highlights the important potential of district heating to the UK’s energy system strategy

Assessing the Domestic Energy Use and Thermal Comfort of Occupants in a Post-war Social Housing Development Estate in Famagusta, Northern Cyprus

Efforts to retrofit post-war residential buildings have gained increasing momentum recently, especially after the European Union called for a zero carbon–emission target by 2050. This study presents a novel methodological framework for determining the most effective energy policy for implementing the EPBD mandates and improving the energy efficiency of existing post-war social housing stock in the South-eastern Mediterranean climate of Cyprus. The study examines how energy policy and regulation is carried out in this area through analysis of nationally representative archetype buildings in the coastal city of Famagusta where the weather is subtropical (Csa) and partly semi-arid (Bsa). The developed empirical framework integrates the socio-technical-systems (STS) approach and provides data about households through field interviews to better understand the relations between sociodemographic characteristics, energy use and thermal comfort. The in-vivo experiences of householders’ thermal-sensation votes is assessed to predict individual aspects of adaptive thermal comfort and its relevance to overheating. Data is collected from in-situ measurements, including recordings of household indoor-air temperatures integrated with thermal-imaging surveys and heatflux measurements of building fabric elements, along with concurrent on-site monitoring of environmental conditions and a review of household energy bills to accurately determine actual energy use. The results reveal that in a non-retrofitted building, cooling and heating comprise the greatest proportion (73%) of total energy consumption. Applications for six passive cooling design strategies are then analysed, and after the life-cycle cost assessment of each is considered, off-site modular building applications are developed. After building optimisation, it is found that approximately an 81% savings related to cooling consumption can be achieved, which suggests that design, ventilation, and servicing strategies, combined with passive shading systems, can improve the energy efficiency and indoor-air quality of residential buildings

Understanding, modelling and predicting transport mobility in urban environments

In the last three decades the global population has been growing at an essentially constant rate, at around 1.5 per cent per year, to about 6.026 billion in 2000 when it was estimated that 47% of that population live in an urban environment. Further, a United Nations' projection indicates that 60% of the total global population may be living in an urban settlement by the year 2025. This increasing urbanisation brings with it increased employment, that delivers affluence, which then continues the cycle of migration and movement to these growing metropolitan areas in both developed and developing countries. As cities increase in population and expand their urban area, there is a consequential expansion of urban transportation and accompanying service infrastructure. People travel daily, irrespective of their vast differences in culture, economic conditions and means of transportation. This daily mobility is sought for its own sake as well as to bridge the spatial distance that separates their homes from the work place, to accomplish their household's domestic needs and to undertake social journeys, such as visiting friends and taking holidays. As the world's urban population undertakes its daily mobility by a variety of transportation modes, an individual's mobility behaviour and mode-choice is governed by a complex matrix of physical and human, social and management indicators, measures and/or drivers. A literature review describes the current understanding of this complex matrix and concludes by identifying and defining a set of fundamental underlying measures that drive private motorised, public transport and non-motorised (walking and bicycling) mobility at national, city and household levels. As practical instruments, transportation models play an important role in providing decision-makers with analytical tools to help them understand their city's transportation and the different future scenarios it may face. While not necessarily producing foolproof information or predictions, models are still the best methods available to test the likely implications of alternative transportation policy decisions in a rapidly changing urban environment. Urban transport models are generally based on the notion that traffic can be modelled in aggregate measures through statistical data and predictive modelling techniques. In this research, dimensional analysis is used to derive sketch-plan models for private motorised, public transport and non-motorised mobility for any urban environment based on four-decades of detailed land-use and travel pattern data from a large international sample of cities. These models are developed on the basis of a set of fundamental underlying measures that are deemed to drive private motorised, public transport and non-motorised (walking and bicycling) mobility at the city level. Importantly, the models also embody three key attributes. They are: * easy to use, minimising user requirements and data inputs * policy-sensitive, capable of assessing a sufficient range of policy options * reliable and robust over time, so that the results can be consistently believed. The capacity of the sketch-plan models to predict personal mobility in an urban environment is statistically validated against an independent land-use and travel pattern data set for 83 cities located on five continents. Despite their simplicity and maintaining a consistent functional form over a time-series of four-decades and across all geographic and cultural regions, the private motorised mobility model can consistently explain up to 92% of the variance in private motorised urban mobility. The results for the public transport mobility model are less reliable and consistent, in particular when developing cities are part of the model. Results for developed or wealthier cities are much better. Reasons for these results and their inadequacies are discussed. The non-motorised modes mobility model is the least successful part of the modelling work. This can be attributed to a combination of inadequate data and, very likely, the more micro-level determinants of usage of these modes. The private motorised urban mobility sketch-plan model equation developed in this thesis is able to predict present and future trends of automobile use in individual cities to a high degree of statistical reliability. The model equation offers urban transport planners a focused direction on the fundamental measures that have the potential to control and deliver automobile restraint policies and strategies. A series of case studies shows that this model has wide applications in understanding past trends in private motorised mobility and in developing urban environmental strategy and policy through its ability to calculate and assess current and future motor vehicle emissions inventories in cities. The thesis makes suggestions for future work in this area of metropolitan level transport modelling, in particular, how to improve the public and non-motorised transport models so that total urban transport mobility can be better understood and modelled