Knowledge-based Biomedical Data Science 2019

Knowledge-based biomedical data science (KBDS) involves the design and implementation of computer systems that act as if they knew about biomedicine. Such systems depend on formally represented knowledge in computer systems, often in the form of knowledge graphs. Here we survey the progress in the last year in systems that use formally represented knowledge to address data science problems in both clinical and biological domains, as well as on approaches for creating knowledge graphs. Major themes include the relationships between knowledge graphs and machine learning, the use of natural language processing, and the expansion of knowledge-based approaches to novel domains, such as Chinese Traditional Medicine and biodiversity.Comment: Manuscript 43 pages with 3 tables; Supplemental material 43 pages with 3 table

The ability model of emotional intelligence: Principles and updates

This article presents seven principles that have guided our thinking about emotional intelligence, some of them new. We have reformulated our original ability model here guided by these principles, clarified earlier statements of the model that were unclear, and revised portions of it in response to current research. In this revision, we also positioned emotional intelligence amidst other hot intelligences including personal and social intelligences, and examined the implications of the changes to the model. We discuss the present and future of the concept of emotional intelligence as a mental ability

Improving the Relevance of Cyber Incident Notification for Mission Assurance

Military organizations have embedded Information and Communication Technology (ICT) into their core mission processes as a means to increase operational efficiency, improve decision making quality, and shorten the kill chain. This dependence can place the mission at risk when the loss, corruption, or degradation of the confidentiality, integrity, and/or availability of a critical information resource occurs. Since the accuracy, conciseness, and timeliness of the information used in decision making processes dramatically impacts the quality of command decisions, and hence, the operational mission outcome; the recognition, quantification, and documentation of critical mission-information resource dependencies is essential for the organization to gain a true appreciation of its operational risk. This research identifies existing decision support systems and evaluates their capabilities as a means for capturing, maintaining and communicating mission-to-information resource dependency information in a timely and relevant manner to assure mission operations. This thesis answers the following research question: Which decision support technology is the best candidate for use in a cyber incident notification system to overcome limitations identified in the existing United States Air Force cyber incident notification process

Development and evaluation of an intelligent handheld insulin dose advisor for patients with Type-1 diabetes

Diabetes mellitus is an increasingly common, chronic, incurable disease requiring careful monitoring and treatment so as to minimise the risk of serious long-term complications. It has been suggested that computers used by healthcare professionals and/or patients themselves may playa useful role in the diabetes care process. Seven key systems (AIDA, ADICOL, DIABETES, DIAS, IIumaLink, T-IDDM, POIRO) in the area of diabetes decision support, and their underlying techniques and approaches are summarised and compared. The development of the Patient-Oriented Insulin Regimen Optimiser (POIRO) for insulindependent (Type-I) diabetes, and its hybrid statistical and rule-based expert system is then taken forward. The re-implementation and updating of the system for the Palm OS family of modern Personal Digital Assistants (PDAs) is described. The evaluation of this new version in a seven week, randomised, open, cross-over clinical pilot study involving eight patients on short-acting plus long-acting insulin basalbolus regimens showed it to be easy-to-operate, reliable, not time consuming and well liked by patients. Following this, the characteristics and use of all currently available insulin formulations, and the corresponding insulin regimens are summarised. Algorithms to provide dose advice and decision support for patients taking the new rapid-acting, intermediate-acting and premixed insulin formulations are then developed. The user interface is improved and extended, amongst others through the development and use of a model describing individual user's meal time habits. Implementation-related issues encountered are discussed, and further work and future directions are identified and outlined. Motivated by the complex and safety-critical nature of systems such as POIRO, we also report on the use of the B abstract machine notation for the formal specification of the original POIRO system, and focusing on projects and published case studies. review the use of formal methods in the development of medical computer systems

Neurosymbolic AI for Reasoning on Graph Structures: A Survey

Neurosymbolic AI is an increasingly active area of research which aims to combine symbolic reasoning methods with deep learning to generate models with both high predictive performance and some degree of human-level comprehensibility. As knowledge graphs are becoming a popular way to represent heterogeneous and multi-relational data, methods for reasoning on graph structures have attempted to follow this neurosymbolic paradigm. Traditionally, such approaches have utilized either rule-based inference or generated representative numerical embeddings from which patterns could be extracted. However, several recent studies have attempted to bridge this dichotomy in ways that facilitate interpretability, maintain performance, and integrate expert knowledge. Within this article, we survey a breadth of methods that perform neurosymbolic reasoning tasks on graph structures. To better compare the various methods, we propose a novel taxonomy by which we can classify them. Specifically, we propose three major categories: (1) logically-informed embedding approaches, (2) embedding approaches with logical constraints, and (3) rule-learning approaches. Alongside the taxonomy, we provide a tabular overview of the approaches and links to their source code, if available, for more direct comparison. Finally, we discuss the applications on which these methods were primarily used and propose several prospective directions toward which this new field of research could evolve.Comment: 21 pages, 8 figures, 1 table, currently under review. Corresponding GitHub page here: https://github.com/NeSymGraph

Efficient Decision Support Systems

This series is directed to diverse managerial professionals who are leading the transformation of individual domains by using expert information and domain knowledge to drive decision support systems (DSSs). The series offers a broad range of subjects addressed in specific areas such as health care, business management, banking, agriculture, environmental improvement, natural resource and spatial management, aviation administration, and hybrid applications of information technology aimed to interdisciplinary issues. This book series is composed of three volumes: Volume 1 consists of general concepts and methodology of DSSs; Volume 2 consists of applications of DSSs in the biomedical domain; Volume 3 consists of hybrid applications of DSSs in multidisciplinary domains. The book is shaped decision support strategies in the new infrastructure that assists the readers in full use of the creative technology to manipulate input data and to transform information into useful decisions for decision makers