A Learning Health System for Radiation Oncology

The proposed research aims to address the challenges faced by clinical data science researchers in radiation oncology accessing, integrating, and analyzing heterogeneous data from various sources. The research presents a scalable intelligent infrastructure, called the Health Information Gateway and Exchange (HINGE), which captures and structures data from multiple sources into a knowledge base with semantically interlinked entities. This infrastructure enables researchers to mine novel associations and gather relevant knowledge for personalized clinical outcomes. The dissertation discusses the design framework and implementation of HINGE, which abstracts structured data from treatment planning systems, treatment management systems, and electronic health records. It utilizes disease-specific smart templates for capturing clinical information in a discrete manner. HINGE performs data extraction, aggregation, and quality and outcome assessment functions automatically, connecting seamlessly with local IT/medical infrastructure. Furthermore, the research presents a knowledge graph-based approach to map radiotherapy data to an ontology-based data repository using FAIR (Findable, Accessible, Interoperable, Reusable) concepts. This approach ensures that the data is easily discoverable and accessible for clinical decision support systems. The dissertation explores the ETL (Extract, Transform, Load) process, data model frameworks, ontologies, and provides a real-world clinical use case for this data mapping. To improve the efficiency of retrieving information from large clinical datasets, a search engine based on ontology-based keyword searching and synonym-based term matching tool was developed. The hierarchical nature of ontologies is leveraged to retrieve patient records based on parent and children classes. Additionally, patient similarity analysis is conducted using vector embedding models (Word2Vec, Doc2Vec, GloVe, and FastText) to identify similar patients based on text corpus creation methods. Results from the analysis using these models are presented. The implementation of a learning health system for predicting radiation pneumonitis following stereotactic body radiotherapy is also discussed. 3D convolutional neural networks (CNNs) are utilized with radiographic and dosimetric datasets to predict the likelihood of radiation pneumonitis. DenseNet-121 and ResNet-50 models are employed for this study, along with integrated gradient techniques to identify salient regions within the input 3D image dataset. The predictive performance of the 3D CNN models is evaluated based on clinical outcomes. Overall, the proposed Learning Health System provides a comprehensive solution for capturing, integrating, and analyzing heterogeneous data in a knowledge base. It offers researchers the ability to extract valuable insights and associations from diverse sources, ultimately leading to improved clinical outcomes. This work can serve as a model for implementing LHS in other medical specialties, advancing personalized and data-driven medicine

The Impact of e-Democracy in Political Stability of Nigeria

The history of the Nigerian electoral process has been hitherto characterized by violence stemming from disputes in election outcomes. For instance, violence erupted across some states in Northern Nigeria when results indicated that a candidate who was popular in that part of the country was losing the election leading to avoidable loss of lives. Beside, this dispute in election outcome lingers for a long time in litigation at the electoral tribunals which distracts effective governance. However, the increasing penetrating use of ICTs in Nigeria is evident in the electoral processes with consequent shift in the behavior of actors in the democratic processes, thus changing the ways Nigerians react to election outcomes. This paper examines the trend in the use ICT in the Nigerian political system and its impact on the stability of the polity. It assesses the role of ICT in recent electoral processes and compares its impact on the outcome of the process in lieu of previous experiences in the Nigeria. Furthermore, the paper also examines the challenges and risks of implementing e-Democracy in Nigeria and its relationship to the economy in the light of the socio-economic situation of the country. The paper adopted qualitative approach in data gathering and analysis. From the findings, the paper observed that e-democracy is largely dependent on the level of ICT adoption, which is still at its lowest ebb in the country. It recognizes the challenges in the provision of ICT infrastructure and argues that appropriate low-cost infrastructure applicable to the Nigerian condition can be made available to implement e-democracy and thus arouse the interest of the populace in governance, increase the number of voters, and enhance transparency, probity and accountability, and participation in governance as well as help stabilize the nascent democrac

Becoming Artifacts: Medieval Seals, Passports and the Future of Digital Identity

What does a digital identity token have to do with medieval seals? Is the history of passports of any use for enabling the discovery of Internet users\u27 identity when crossing virtual domain boundaries during their digital browsing and transactions? The agility of the Internet architecture and its simplicity of use have been the engines of its growth and success with the users worldwide. As it turns out, there lies also its crux. In effect, Internet industry participants have argued that the critical problem business is faced with on the Internet is the absence of an identity layer from the core protocols of its logical infrastructure. As a result, the cyberspace parallels a global territory without any identification mechanism that is reliable, consistent and interoperable across domains. This dissertation is an investigation of the steps being taken by Internet stakeholders in order to resolve its identity problems, through the lenses of historical instances where similar challenges were tackled by social actors. Social science research addressing the Internet identity issues is barely nascent. Research on identification systems in general is either characterized by a paucity of historical perspective, or scantily references digital technology and online identification processes. This research is designed to bridge that gap. The general question at its core is: How do social actors, events or processes enable the historical emergence of authoritative identity credentials for the public at large? This work is guided by that line of inquiry through three broad historical case studies: first, the medieval experience with seals used as identity tokens in the signing of deeds that resulted in transfers of rights, particularly estate rights; second, comes the modern, national state with its claim to the right to know all individuals on its territory through credentials such as the passport or the national identity card; and finally, viewed from the United States, the case of ongoing efforts to build an online digital identity infrastructure. Following a process-tracing approach to historical case study, this inquiry presents enlightening connections between the three identity frameworks while further characterizing each. We understand how the medieval doctrines of the Trinity and the Eucharist developed by schoolmen within the Church accommodated seals as markers of identity, and we understand how the modern state seized on the term `nationality\u27 - which emerged as late as in the 19th century - to make it into a legal fiction that was critical for its identification project. Furthermore, this investigation brings analytical insights which enable us to locate the dynamics driving the emergence of those identity systems. An ordering of the contributing factors in sequential categories is proposed in a sociohistorical approach to explain the causal mechanisms at work across these large phenomena. Finally this research also proposes historically informed projections of scenarios as possible pathways to the realization of authoritative digital identity. But that is the beginning of yet another story of identity

On Random Sampling for Compliance Monitoring in Opportunistic Spectrum Access Networks

In the expanding spectrum marketplace, there has been a long term evolution towards more market€“oriented mechanisms, such as Opportunistic Spectrum Access (OSA), enabled through Cognitive Radio (CR) technology. However, the potential of CR technologies to revolutionize wireless communications, also introduces challenges based upon the potentially non€“deterministic CR behaviour in the Electrospace. While establishing and enforcing compliance to spectrum etiquette rules are essential to realization of successful OSA networks in the future, there has only been recent increased research activity into enforcement. This dissertation presents novel work on the spectrum monitoring aspect, which is crucial to effective enforcement of OSA. An overview of the challenges faced by current compliance monitoring methods is first presented. A framework is then proposed for the use of random spectral sampling techniques to reduce data collection complexity in wideband sensing scenarios. This approach is recommended as an alternative to Compressed Sensing (CS) techniques for wideband spectral occupancy estimation, which may be difficult to utilize in many practical congested scenarios where compliance monitoring is required. Next, a low€“cost computational approach to online randomized temporal sensing deployment is presented for characterization of temporal spectrum occupancy in cognitive radio scenarios. The random sensing approach is demonstrated and its performance is compared to CS€“based approach for occupancy estimation. A novel frame€“based sampling inversion technique is then presented for cases when it is necessary to track the temporal behaviour of individual CRs or CR networks. Parameters from randomly sampled Physical Layer Convergence Protocol (PLCP) data frames are used to reconstruct occupancy statistics, taking account of missed frames due to sampling design, sensor limitations and frame errors. Finally, investigations into the use of distributed and mobile spectrum sensing to collect spatial diversity to improve the above techniques are presented, for several common monitoring tasks in spectrum enforcement. Specifically, focus is upon techniques for achieving consensus in dynamic topologies such as in mobile sensing scenarios