Browsing by Author "Dube K"

Now showing 1 - 8 of 8
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    A Framework for Analysing Learning Health Systems: Are we removing the most impactful barriers?
    (Wiley Periodicals, Inc on behalf of the University of Michigan, 21/10/2019) McLachlan S; Dube K; Johnson O; Buchanan D; Potts HWW; Gallagher T; Fenton N
    Introduction: Learning health systems (LHS) are one of the major computing advances in health care. However, no prior research has systematically analysed barriers and facilitators for LHS. This paper presents an investigation into the barriers, benefits, and facilitating factors for LHS in order to create a basis for their successful implementation and adoption. Methods: First, the ITPOSMO‐BBF framework was developed based on the established ITPOSMO (information, technology, processes, objectives, staffing, management, and other factors) framework, extending it for analysing barriers, benefits, and facilitators. Second, the new framework was applied to LHS. Results: We found that LHS shares similar barriers and facilitators with electronic health records (EHR); in particular, most facilitator effort in implementing EHR andLHS goes towards barriers categorised as human factors, even though they were seen to carry fewer benefits. Barriers whose resolution would bring significant benefits in safety, quality, and health outcomes remain. Discussion: LHS envisage constant generation of new clinical knowledge and practice based on the central role of collections of EHR. Once LHS are constructed and operational, they trigger new data streams into the EHR. So LHS and EHR have a symbiotic relationship. The implementation and adoption of EHRs have proved and continues to prove challenging, and there are many lessons for LHS arising from these challenges. Conclusion: Successful adoption of LHS should take account of the framework proposed in this paper, especially with respect to its focus on removing barriers that have the most impact
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    Computational Modeling of Agglutinative Languages: The Challenge for Southern Bantu Languages
    (Arusha Linguistics, 2021-02-24) Kambarami F; McLachlan S; Bozic B; Dube K; Chimhundu H
    In computational linguistics, language models are probabilistic models that predict the likelihood of words occurring within specific sentences. They are key components of many natural language processing systems. Traditional full word models do not work well for agglutinative languages. These are languages that have words built out of distinctly identifiable sub-parts that carry specific meanings and functions and can be combined in different ways to form new words. Sub-word language models have been considered to address this problem and have had success with some agglutinative languages. However the existing models do not appear to address the specific ways in which the sentences and words within the Southern Bantu languages, which are agglutinative, are formed. The adoption of sub-word models for these languages has also been low.
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    Holistic User Context-Aware Recommender Algorithm
    (Hindawi Limited, 29/09/2019) Kavu T; Dube K; Raeth P
    Existing recommender algorithms lack dynamism, human focus, and serendipitous recommendations. The literature indicates that the context of a user influences user decisions, and when incorporated in recommender systems (RSs), novel and serendipitous recommendations can be realized. This article shows that social, cultural, psychological, and economic contexts of a user influence user traits or decisions. The article demonstrates a novel approach of incorporating holistic user context-aware knowledge in an algorithm to solve the highlighted problems. Web content mining and collaborative filtering approaches were used to develop a holistic user context-aware (HUC) algorithm. The algorithm was evaluated on a social network using online experimental evaluations. The algorithm demonstrated dynamism, novelty, and serendipity with an average of 84% novelty and 85% serendipity.
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    Learning health systems: The research community awareness challenge
    (BCS, The Chartered Institute for IT, 2018-03) McLachlan S; Dube K; Buchanan DH; Lean S; Johnson O; Potts H; Gallagher T; Marsh W; Fenton N
    The Learning Health System (LHS) is one in which progress in science, informatics and care culture converges to continuously create new knowledge as a natural by-product of care processes. While LHS were first described over a decade ago, much of the recent published work that should fall within the domain of LHS fails to claim or be identified as such. This observation was confirmed through review of papers published at the recent 2017 IEEE International Conference on Health Informatics (ICHI 2017), where no single LHS solution had been so identified. The authors lacked awareness that their work represented an LHS, or of any discrete classification for their work within the LHS domain. We believe this lack of awareness inhibits continued LHS research and prevents formation of a critical mass of researchers within the domain. Efforts to produce a framework and classification structure to enable confident identification of work with the LHS domain are urgently needed to address this pressing research community challenge.
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    Smart automotive technology adherence to the law: (de)constructing road rules for autonomous system development, verification and safety
    (Oxford University Press, 22/02/2022) McLachlan S; Neil M; Dube K; Bogani R; Fenton N; Schaffer B
    Driving is an intuitive task that requires skill, constant alertness and vigilance for unexpected events. The driving task also requires long concentration spans, focusing on the entire task for prolonged periods, and sophisticated negotiation skills with other road users including wild animals. Modern motor vehicles include an array of smart assistive and autonomous driving systems capable of subsuming some, most, or in limited cases, all of the driving task. Building these smart automotive systems requires software developers with highly technical software engineering skills, and now a lawyer’s in-depth knowledge of traffic legislation as well. This article presents an approach for deconstructing the complicated legalese of traffic law and representing its requirements and flow. Our approach (de)constructs road rules in legal terminology and specifies them in ‘structured English logic’ that is expressed as ‘Boolean logic’ for automation and ‘Lawmaps’ for visualization. We demonstrate an example using these tools leading to the construction and validation of a ‘Bayesian Network model’. We strongly believe these tools to be approachable by programmers and the general public, useful in development of Artificial Intelligence to underpin motor vehicle smart systems, and in validation to ensure these systems are considerate of the law when making decisions.
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    Synthea: An approach, method, and software mechanism for generating synthetic patients and the synthetic electronic health care record
    (Oxford University Press (OUP), 30/08/2017) Walonoski J; Kramer M; Nichols J; Quina A; Moesel; Hall D; Duffett C; Dube K; Gallagher T; McLachlan S
    Objective: Our objective is to create a source of synthetic electronic health records that is readily available; suited to industrial, innovation, research, and educational uses; and free of legal, privacy, security, and intellectual property restrictions. Materials and Methods: We developed Synthea, an open-source software package that simulates the lifespans of synthetic patients, modeling the 10 most frequent reasons for primary care encounters and the 10 chronic conditions with the highest morbidity in the United States. Results: Synthea adheres to a previously developed conceptual framework, scales via open-source deployment on the Internet, and may be extended with additional disease and treatment modules developed by its user community. One million synthetic patient records are now freely available online, encoded in standard formats (eg, Health Level-7 [HL7] Fast Healthcare Interoperability Resources [FHIR] and Consolidated-Clinical Document Architecture), and accessible through an HL7 FHIR application program interface. Discussion: Health care lags other industries in information technology, data exchange, and interoperability. The lack of freely distributable health records has long hindered innovation in health care. Approaches and tools are available to inexpensively generate synthetic health records at scale without accidental disclosure risk, lowering current barriers to entry for promising early-stage developments. By engaging a growing community of users, the synthetic data generated will become increasingly comprehensive, detailed, and realistic over time. Conclusion: Synthetic patients can be simulated with models of disease progression and corresponding standards of care to produce risk-free realistic synthetic health care records at scale.
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    Tempting the Fate of the furious: cyber security and autonomous cars
    (Routledge, 27/05/2022) McLachlan S; Schafer B; Dube K; Kyrimi E; Fenton N
    The United Nations Economic Commission for Europe (UN ECE) has developed new aspects of its WP.29 agreement for harmonising vehicle regulations, focusing on the regulation of vehicle manufacturers’ approaches to ensuring vehicle cyber security by requiring implementation of an approved cyber security management system (CSMS). This paper investigates the background, framework and content of WP.29’s cyber security regulation. We provide an overall description of the processes required to become certified, discuss key gaps, issues and the impacts of implementation on stakeholders, and provide recommendations for manufacturers and the authorities who will oversee the operation. Putting the discussion into a broader theoretical framework on risk certification, we explore to the role of non-academic sources to shape public risk perception and to drive, for better or worse, legislative responses.
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    Towards standardisation of evidence-based clinical care process specifications
    (SAGE Journals, 2020-12) McLachlan S; Kyrimi E; Dube K; Hitman G; Simmonds J; Fenton N
    There is a strong push towards standardisation of treatment approaches, care processes and documentation of clinical practice. However, confusion persists regarding terminology and description of many clinical care process specifications which this research seeks to resolve by developing a taxonomic characterisation of clinical care process specifications. Literature on clinical care process specifications was analysed, creating the starting point for identifying common characteristics and how each is constructed and used in the clinical setting. A taxonomy for clinical care process specifications is presented. The De Bleser approach to limited clinical care process specifications characterisation was extended and each clinical care process specification is successfully characterised in terms of purpose, core elements and relationship to the other clinical care process specification types. A case study on the diagnosis and treatment of Type 2 Diabetes in the United Kingdom was used to evaluate the taxonomy and demonstrate how the characterisation framework applies. Standardising clinical care process specifications ensures that the format and content are consistent with expectations, can be read more quickly and high-quality information can be recorded about the patient. Standardisation also enables computer interpretability, which is important in integrating Learning Health Systems into the modern clinical environment. The approach presented allows terminologies for clinical care process specifications that were widely used interchangeably to be easily distinguished, thus, eliminating the existing confusion.