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Data analytics to select markers and cut-off values for clinical scoring

- Stranieri, Andrew, Yatsko, Andrew, Venkatraman, Sitalakshmi, Jelinek, Herbert

  • Authors: Stranieri, Andrew , Yatsko, Andrew , Venkatraman, Sitalakshmi , Jelinek, Herbert
  • Date: 2018
  • Type: Text , Conference proceedings
  • Relation: ACSW '18: Proceedings of the Australasian Computer Science Week Multiconference; Brisbane; 29th January -2nd February 2018 p. 1-6
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  • Description: Scoring systems such as the Glasgow-Coma scale used to assess consciousness AusDrisk to assess the risk of diabetes, are prevalent in clinical practice. Scoring systems typically include relevant variables with ordinal values where each value is assigned a weight. Weights for selected values are summed and compared to thresholds for health care professionals to rapidly generate a score. Scoring systems are prevalent in clinical practice because they are easy and quick to use. However, most scoring systems comprise many variables and require some time to calculate an final score. Further, expensive population-wide studies are required to validate a scoring system. In this article, we present a new approach for the generation of a scoring system. The approach uses a search procedure invoking iterative decision tree induction to identify a suite of scoring rules, each of which requires values on only two variables. Twelve scoring rules were discovered using the approach, from an Australian screening program for the assessment of Type 2 Diabetes risk. However, classifications from the 12 rules can conflict. In this paper we argue that a simple rule preference relation is sufficient for the resolution of rule conflicts.

The design of a smartbrush oral health installation for aged care centres in Australia

- Grzegorz Broda, Lukasz, Oseni, Taiwo, Stranieri, Andrew, Marino, Rodrigo, Robinson, Jodie, Yates, Mark

  • Authors: Grzegorz Broda, Lukasz , Oseni, Taiwo , Stranieri, Andrew , Marino, Rodrigo , Robinson, Jodie , Yates, Mark
  • Date: 2021
  • Type: Text , Conference paper
  • Relation: 5th International Conference on Medical and Health Informatics, ICMHI 2021 p. 176-180
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  • Description: The oral health of residents in aged care centres in Australia is poor, contributing to infections, hospital admissions, and increased suffering. Although the use of electric toothbrushes has been deployed in many centres, smartbrushes that record and transmit information about brushing patterns and duration are not routinely deployed. Yet, the use of smartbrushes for aged care residents promises better oral care. Thus, a study aimed at investigating the appropriateness and suitability of a smartbrush for aged care residents is currently underway. Due to the peculiarity of the aged care setting, the incorporation of smartbrushes into residents' care does require careful planning and design considerations. This paper describes an initial design process undertaken through the use of an actor to understand the important elements to be incorporated whilst installing a smartbrush for use in aged care settings. The design covers configuration settings of the brush and app, including ergonomic factors related to brush and smartphone placement. A design science approach led to an installation re-design and a revised protocol for the planned study, the ultimate aim being to design installations to enhance perceived usefulness, ease of use, and attitudes towards the incorporation of smartbrushes for improving oral health care for aged care residents. © 2021 ACM.

An Agile group aware process beyond CRISP-DM: A hospital data mining case study

- Sharma, Vishakha, Stranieri, Andrew, Ugon, Julien, Martin, Laura

  • Authors: Sharma, Vishakha , Stranieri, Andrew , Ugon, Julien , Martin, Laura
  • Date: 2017
  • Type: Text , Conference proceedings
  • Relation: ICCDA '17: Proceedings of the International Conference on Computer and Data Analysis May 2017 p. 109-113
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  • Description: The CRISP-DM methodology is commonly used in data analytics exercises within an organisation to provide system and structure to data mining processes. However, in providing a rigorous framework, CRISP-DM overlooks two facets of data analytics in organisational contexts; data mining exercises are far more agile and subject to change than presumed in CRISP-DM and central decisions regarding the interpretation of patterns discovered and the direction of analytics exercises are typically not made by individuals but by committees or groups within an organisation. The current study provides a case study of data mining in a hospital setting and suggests how the agile nature of an analytics exercise and the group reasoning inherent in key decisions can be accommodated within a CRISP-DM methodology.

Dynamically recommending repositories for health data : a machine learning model

- Uddin, Md Ashraf, Stranieri, Andrew, Gondal, Iqbal, Balasubramanian, Venki

  • Authors: Uddin, Md Ashraf , Stranieri, Andrew , Gondal, Iqbal , Balasubramanian, Venki
  • Date: 2020
  • Type: Text , Conference proceedings
  • Relation: 2020 Australasian Computer Science Week Multiconference, ACSW 2020
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  • Description: Recently, a wide range of digital health record repositories has emerged. These include Electronic Health record managed by the government, Electronic Medical Record (EMR) managed by healthcare providers, Personal Health Record (PHR) managed directly by the patient and new Blockchain-based systems mainly managed by technologies. Health record repositories differ from one another on the level of security, privacy, and quality of services (QoS) they provide. Health data stored in these repositories also varies from patient to patient in sensitivity, and significance depending on medical, personal preference, and other factors. Decisions regarding which digital record repository is most appropriate for the storage of each data item at every point in time are complex and nuanced. The challenges are exacerbated with health data continuously streamed from wearable sensors. In this paper, we propose a recommendation model for health data storage that can accommodate patient preferences and make storage decisions rapidly, in real-time, even with streamed data. The model maps health data to be stored in the repositories. The mapping between health data features and characteristics of each repository is learned using a machine learning-based classifier mediated through clinical rules. Evaluation results demonstrate the model's feasibility. © 2020 ACM.
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