London SynEx Demonstrator Site: Impact Assessment Report

The key ingredients of the SynEx-UCL software components are: 1. A comprehensive and federated electronic healthcare record that can be used to reference or to store all of the necessary healthcare information acquired from a diverse range of clinical databases and patient-held devices. 2. A directory service component to provide a core persons demographic database to search for and authenticate staff users of the system and to anchor patient identification and connection to their federated healthcare record. 3. A clinical record schema management tool (Object Dictionary Client) that enables clinicians or engineers to define and export the data sets mapping to individual feeder systems. 4. An expansible set of clinical management algorithms that provide prompts to the patient or clinician to assist in the management of patient care. CHIME has built up over a decade of experience within Europe on the requirements and information models that are needed to underpin comprehensive multiprofessional electronic healthcare records. The resulting architecture models have influenced new European standards in this area, and CHIME has designed and built prototype EHCR components based on these models. The demonstrator systems described here utilise a directory service and object-oriented engineering approach, and support the secure, mobile and distributed access to federated healthcare records via web-based services. The design and implementation of these software components has been founded on a thorough analysis of the clinical, technical and ethico-legal requirements for comprehensive EHCR systems, published through previous project deliverables and in future planned papers. The clinical demonstrator site described in this report has provided the solid basis from which to establish "proof of concept" verification of the design approach, and a valuable opportunity to install, test and evaluate the results of the component engineering undertaken during the EC funded project. Inevitably, a number of practical implementation and deployment obstacles have been overcome through this journey, each of those having contributed to the time taken to deliver the components but also to the richness of the end products. UCL is fortunate that the Whittington Hospital, and the department of cardiovascular medicine in particular, is committed to a long-term vision built around this work. That vision, outlined within this report, is shared by the Camden and Islington Health Authority and by many other purchaser and provider organisations in the area, and by a number of industrial parties. They are collectively determined to support the Demonstrator Site as an ongoing project well beyond the life of the EC SynEx Project. This report, although a final report as far as the EC project is concerned, is really a description of the first phase in establishing a centre of healthcare excellence. New EC Fifth Framework project funding has already been approved to enable new and innovative technology solutions to be added to the work already established in north London

Human-Machine Collaborative Optimization via Apprenticeship Scheduling

Coordinating agents to complete a set of tasks with intercoupled temporal and resource constraints is computationally challenging, yet human domain experts can solve these difficult scheduling problems using paradigms learned through years of apprenticeship. A process for manually codifying this domain knowledge within a computational framework is necessary to scale beyond the ``single-expert, single-trainee" apprenticeship model. However, human domain experts often have difficulty describing their decision-making processes, causing the codification of this knowledge to become laborious. We propose a new approach for capturing domain-expert heuristics through a pairwise ranking formulation. Our approach is model-free and does not require enumerating or iterating through a large state space. We empirically demonstrate that this approach accurately learns multifaceted heuristics on a synthetic data set incorporating job-shop scheduling and vehicle routing problems, as well as on two real-world data sets consisting of demonstrations of experts solving a weapon-to-target assignment problem and a hospital resource allocation problem. We also demonstrate that policies learned from human scheduling demonstration via apprenticeship learning can substantially improve the efficiency of a branch-and-bound search for an optimal schedule. We employ this human-machine collaborative optimization technique on a variant of the weapon-to-target assignment problem. We demonstrate that this technique generates solutions substantially superior to those produced by human domain experts at a rate up to 9.5 times faster than an optimization approach and can be applied to optimally solve problems twice as complex as those solved by a human demonstrator.Comment: Portions of this paper were published in the Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI) in 2016 and in the Proceedings of Robotics: Science and Systems (RSS) in 2016. The paper consists of 50 pages with 11 figures and 4 table

Digital wood craft

In 1995, Robin Evans points out in his book The Projective Cast how the development of techniques changed architecture and the space inhabited in times of Gothic and early Renaissance. We see a parallel phenomenon today, where the interplay of technology and tool gives shape to new design (Kolarevic 2005). Yet in opposition to the interwoven fields of design and craft of the late Gothic, todayis building sector is enormously diversified, and a growing complexity in the building process and number of used materials can be observed. This gives an opposite point of departure into a more integrated field of design and innovation in architectural design and building industry

mARC: Memory by Association and Reinforcement of Contexts

This paper introduces the memory by Association and Reinforcement of Contexts (mARC). mARC is a novel data modeling technology rooted in the second quantization formulation of quantum mechanics. It is an all-purpose incremental and unsupervised data storage and retrieval system which can be applied to all types of signal or data, structured or unstructured, textual or not. mARC can be applied to a wide range of information clas-sification and retrieval problems like e-Discovery or contextual navigation. It can also for-mulated in the artificial life framework a.k.a Conway "Game Of Life" Theory. In contrast to Conway approach, the objects evolve in a massively multidimensional space. In order to start evaluating the potential of mARC we have built a mARC-based Internet search en-gine demonstrator with contextual functionality. We compare the behavior of the mARC demonstrator with Google search both in terms of performance and relevance. In the study we find that the mARC search engine demonstrator outperforms Google search by an order of magnitude in response time while providing more relevant results for some classes of queries

Old School Catalog 1906-07, Chicago College of Dental Surgery

https://scholar.valpo.edu/oldschoolcatalogs/1030/thumbnail.jp

Old School Catalog 1913-14, Chicago College of Dental Surgery

https://scholar.valpo.edu/oldschoolcatalogs/1117/thumbnail.jp

Old School Catalog 1916-17, Chicago College of Dental Surgery

https://scholar.valpo.edu/oldschoolcatalogs/1107/thumbnail.jp