Knowledge Discovery for Decision Support in Law

The Split Up project applies knowledge discovery techniques (KDD) to legal domains. Theories of jurisprudence underpin a classification scheme that is used to identify tasks suited to KDD. Theoretical perspectives also guide the selection of cases appropriate for a KDD exercise. Further, jurisprudence underpins strategies for dealing with contradictory data. Argumentation theory is instrumental for representing domain expertise so that the KDD process can be constrained. Specifically, a variant of the argumentation structure proposed by Toulmin is used to decompose tasks into independent sub-tasks in the data transformation phase. This enables a complex KDD exercise to be decomposed into numerous simpler exercises that each require less data and have fewer instances of missing values. The use of the structure also facilitates the development of information systems that integrate multiple reasoning mechanisms such as first order logic, neural networks or fuzzy inferences and provides a convenient structure for the generation of explanations. The viability of this approach was tested with the development of a system that predicts property split outcomes in cases litigated in the Family Court of Australia. The system has been evaluated using a mix of strategies that derive from a framework proposed by Reich

Towards the Application of Association Rules for Defeasible Rules Discovery

In this paper we investigate the feasibility of Knowledge Discovery from Database (KDD) in order to facilitate the discovery of defeasible rules that represent the ratio decidendi underpinning legal decision making. Moreover we will argue in favour of Defeasible Logic as the appropriate formal system in which the extracted principles should be encoded

Criteria to measure social media value in health care settings : narrative literature review

Background: With the growing use of social media in health care settings, there is a need to measure outcomes resulting from its use to ensure continuous performance improvement. Despite the need for measurement, a unified approach for measuring the value of social media used in health care remains elusive. Objective: This study aimed to elucidate how the value of social media in health care settings can be ascertained and to taxonomically identify steps and techniques in social media measurement from a review of relevant literature. Methods: A total of 65 relevant articles drawn from 341 articles on the subject of measuring social media in health care settings were qualitatively analyzed and synthesized. The articles were selected from the literature from diverse disciplines including business, information systems, medical informatics, and medicine. Results: The review of the literature showed different levels and focus of analysis when measuring the value of social media in health care settings. It equally showed that there are various metrics for measurement, levels of measurement, approaches to measurement, and scales of measurement. Each may be relevant, depending on the use case of social media in health care. Conclusions: A comprehensive yardstick is required to simplify the measurement of outcomes resulting from the use of social media in health care. At the moment, there is neither a consensus on what indicators to measure nor on how to measure them. We hope that this review is used as a starting point to create a comprehensive measurement criterion for social media used in health care. © 2019 Chukwuma Ukoha, Andrew Stranieri

A study plan for investigating Smart brush for better oral hygiene in frail elderly

Oral health in Australia’s older population is of great concern and studies show that two-thirds of residents in aged care facilities have significant oral problems. Cognitive, and functional alterations that accumulate while ageing leads to increasing care dependency which then impacts on the ability to maintain good oral health. This paper presents ideas for a pilot investigation into the effectiveness of smart brush technology for improving oral health among the elderly. The proposed pilot study will follow a design that incorporates a Critical Realist methodological perspective known as the Context- Initiative-Mechanism-Outcome approach with a theoretical perspective, the theory of interactive Media effects (TIME). This paper presents a proposition suggesting smart brush as a means for improving oral health among the elderly through identification of context (frail elderly), initiative (smart brush), mechanism (interaction with the smart brush affordances), and outcome (improved oral health). Both qualitative (interviews) and quantitative data (plaque score, brushing duration/coverage) will be collected and analyzed for testing the proposition

Missing health data pattern matching technique for continuous remote patient monitoring

Remote patient monitoring (RPM) has been gaining popularity recently. However, health data acquisition is a significant challenge associated with patient monitoring. In continuous RPM, health data acquisition may miss health data during transmission. Missing data compromises the quality and reliability of patient risk assessment. Several studies suggested techniques for analyzing missing data; however, many are unsuitable for RPM. These techniques neglect the variability of missing data and provide biased results with imputation. Therefore, a holistic approach must consider the correlation and variability of the various vitals and avoid biased imputation. This paper proposes a coherent computation pattern-matching technique to identify and predict missing data patterns. The performance of the proposed approach is evaluated using data collected from a field trial. Results show that the technique can effectively identify and predict missing patterns. © 2023, The Author(s)

Blockchain leveraged task migration in body area sensor networks

Blockchain technologies emerging for healthcare support secure health data sharing with greater interoperability among different heterogeneous systems. However, the collection and storage of data generated from Body Area Sensor Net-works(BASN) for migration to high processing power computing services requires an efficient BASN architecture. We present a decentralized BASN architecture that involves devices at three levels; 1) Body Area Sensor Network-medical sensors typically on or in patient's body transmitting data to a Smartphone, 2) Fog/Edge, and 3) Cloud. We propose that a Patient Agent(PA) replicated on the Smartphone, Fog and Cloud servers processes medical data and execute a task offloading algorithm by leveraging a Blockchain. Performance analysis is conducted to demonstrate the feasibility of the proposed Blockchain leveraged, distributed Patient Agent controlled BASN. © 2019 IEEE.E

A lightweight blockchain based framework for underwater ioT

The Internet of Things (IoT) has facilitated services without human intervention for a wide range of applications, including underwater monitoring, where sensors are located at various depths, and data must be transmitted to surface base stations for storage and processing. Ensuring that data transmitted across hierarchical sensor networks are kept secure and private without high computational cost remains a challenge. In this paper, we propose a multilevel sensor monitoring architecture. Our proposal includes a layer-based architecture consisting of Fog and Cloud elements to process and store and process the Internet of Underwater Things (IoUT) data securely with customized Blockchain technology. The secure routing of IoUT data through the hierarchical topology ensures the legitimacy of data sources. A security and performance analysis was performed to show that the architecture can collect data from IoUT devices in the monitoring region efficiently and securely. © 2020 by the authors. Licensee MDPI, Basel, Switzerland

Blockchain leveraged decentralized IoT eHealth framework

Blockchain technologies recently emerging for eHealth, can facilitate a secure, decentral- ized and patient-driven, record management system. However, Blockchain technologies cannot accommodate the storage of data generated from IoT devices in remote patient management (RPM) settings as this application requires a fast consensus mechanism, care- ful management of keys and enhanced protocols for privacy. In this paper, we propose a Blockchain leveraged decentralized eHealth architecture which comprises three layers: (1) The Sensing layer –Body Area Sensor Networks include medical sensors typically on or in a patient body transmitting data to a smartphone. (2) The NEAR processing layer –Edge Networks consist of devices at one hop from data sensing IoT devices. (3) The FAR pro- cessing layer –Core Networks comprise Cloud or other high computing servers). A Patient Agent (PA) software replicated on the three layers processes medical data to ensure reli- able, secure and private communication. The PA executes a lightweight Blockchain consen- sus mechanism and utilizes a Blockchain leveraged task-offloading algorithm to ensure pa- tient’s privacy while outsourcing tasks. Performance analysis of the decentralized eHealth architecture has been conducted to demonstrate the feasibility of the system in the pro- cessing and storage of RPM data