The need for cybersecurity self-evaluation in healthcare.

The Australian healthcare sector is a complex mix of government departments, associations, providers, professionals, and consumers. Cybersecurity attacks, which have recently increased, challenge the sector in many ways; however, the best approaches for the sector to manage the threat are unclear. This study will report on a semi-structured focus group conducted with five representatives from the Australian healthcare and computer security sectors. An analysis of this focus group transcript yielded four themes: 1) the challenge of securing the Australian healthcare landscape; 2) the financial challenges of cybersecurity in healthcare; 3) balancing privacy and transparency; 4) education and regulation. The results indicate the need for sector-specific tools to empower the healthcare sector to mitigate cybersecurity threats, most notably using a self-evaluation tool so stakeholders can proactively prepare for incidents. Despite the vast amount of research into cybersecurity, little has been conducted on proactive cybersecurity approaches where security weaknesses are identified weaknesses before they occur.

Rapid health data repository allocation using predictive machine learning.

Health-related data is stored in a number of repositories that are managed and controlled by different entities. For instance, Electronic Health Records are usually administered by governments. Electronic Medical Records are typically controlled by health care providers, whereas Personal Health Records are managed directly by patients. Recently, Blockchain-based health record systems largely regulated by technology have emerged as another type of repository. Repositories for storing health data differ from one another based on cost, level of security and quality of performance. Not only has the type of repositories increased in recent years, but the quantum of health data to be stored has increased. For instance, the advent of wearable sensors that capture physiological signs has resulted in an exponential growth in digital health data. The increase in the types of repository and amount of data has driven a need for intelligent processes to select appropriate repositories as data is collected. However, the storage allocation decision is complex and nuanced. The challenges are exacerbated when health data are continuously streamed, as is the case with wearable sensors. Although patients are not always solely responsible for determining which repository should be used, they typically have some input into this decision. Patients can be expected to have idiosyncratic preferences regarding storage decisions depending on their unique contexts. In this paper, we propose a predictive model for the storage of health data that can meet patient needs and make storage decisions rapidly, in real-time, even with data streaming from wearable sensors. The model is built with a machine learning classifier that learns the mapping between characteristics of health data and features of storage repositories from a training set generated synthetically from correlations evident from small samples of experts. Results from the evaluation demonstrate the viability of the machine learning technique used.

A Patient Agent to Manage Blockchains for Remote Patient Monitoring.

Continuous monitoring of patient's physiological signs has the potential to augment traditional medical practice, particularly in developing countries that have a shortage of healthcare professionals. However, continuously streamed data presents additional security, storage and retrieval challenges and further inhibits initiatives to integrate data to form electronic health record systems. Blockchain technologies enable data to be stored securely and inexpensively without recourse to a trusted authority. Blockchain technologies also promise to provide architectures for electronic health records that do not require huge government expenditure that challenge developing nations. However, Blockchain deployment, particularly with streamed data challenges existing Blockchain algorithms that take too long to place data in a block, and have no mechanism to determine whether every data point in every stream should be stored in such a secure way. This article presents an architecture that involves a Patient Agent, coordinating the insertion of continuous data streams into Blockchains to form an electronic health record.

A Cultural Competence Organizational Review for Community Health Services: Insights From a Participatory Approach.

Cultural competence is an important aspect of health service access and delivery in health promotion and community health. Although a number of frameworks and tools are available to assist health service organizations improve their services to diverse communities, there are few published studies describing organizational cultural competence assessments and the extent to which these tools facilitate cultural competence. This article addresses this gap by describing the development of a cultural competence assessment, intervention, and evaluation tool called the Cultural Competence Organizational Review (CORe) and its implementation in three community sector organizations. Baseline and follow-up staff surveys and document audits were conducted at each participating organization. Process data and organizational documentation were used to evaluate and monitor the experience of CORe within the organizations. Results at follow-up indicated an overall positive trend in organizational cultural competence at each organization in terms of both policy and practice. Organizations that are able to embed actions to improve organizational cultural competence within broader organizational plans increase the likelihood of sustainable changes to policies, procedures, and practice within the organization. The benefits and lessons learned from the implementation of CORe are discussed.

Teeth Tales: a community-based child oral health promotion trial with migrant families in Australia.

OBJECTIVES: The Teeth Tales trial aimed to establish a model for child oral health promotion for culturally diverse communities in Australia. DESIGN: An exploratory trial implementing a community-based child oral health promotion intervention for Australian families from migrant backgrounds. Mixed method, longitudinal evaluation. SETTING: The intervention was based in Moreland, a culturally diverse locality in Melbourne, Australia. PARTICIPANTS: Families with 1-4-year-old children, self-identified as being from Iraqi, Lebanese or Pakistani backgrounds residing in Melbourne. Participants residing close to the intervention site were allocated to intervention. INTERVENTION: The intervention was conducted over 5 months and comprised community oral health education sessions led by peer educators and follow-up health messages. OUTCOME MEASURES: This paper reports on the intervention impacts, process evaluation and descriptive analysis of health, knowledge and behavioural changes 18 months after baseline data collection. RESULTS: Significant differences in the Debris Index (OR=0.44 (0.22 to 0.88)) and the Modified Gingival Index (OR=0.34 (0.19 to 0.61)) indicated increased tooth brushing and/or improved toothbrushing technique in the intervention group. An increased proportion of intervention parents, compared to those in the comparison group reported that they had been shown how to brush their child's teeth (OR=2.65 (1.49 to 4.69)). Process evaluation results highlighted the problems with recruitment and retention of the study sample (275 complete case families). The child dental screening encouraged involvement in the study, as did linking attendance with other community/cultural activities. CONCLUSIONS: The Teeth Tales intervention was promising in terms of improving oral hygiene and parent knowledge of tooth brushing technique. Adaptations to delivery of the intervention are required to increase uptake and likely impact. A future cluster randomised controlled trial would provide strongest evidence of effectiveness if appropriate to the community, cultural and economic context. TRIAL REGISTRATION NUMBER: Australian New Zealand Clinical Trials Registry (ACTRN12611000532909).

An exploratory trial implementing a community-based child oral health promotion intervention for Australian families from refugee and migrant backgrounds: a protocol paper for Teeth Tales.

INTRODUCTION: Inequalities are evident in early childhood caries rates with the socially disadvantaged experiencing greater burden of disease. This study builds on formative qualitative research, conducted in the Moreland/Hume local government areas of Melbourne, Victoria 2006-2009, in response to community concerns for oral health of children from refugee and migrant backgrounds. Development of the community-based intervention described here extends the partnership approach to cogeneration of contemporary evidence with continued and meaningful involvement of investigators, community, cultural and government partners. This trial aims to establish a model for child oral health promotion for culturally diverse communities in Australia. METHODS AND ANALYSIS: This is an exploratory trial implementing a community-based child oral health promotion intervention for Australian families from refugee and migrant backgrounds. Families from an Iraqi, Lebanese or Pakistani background with children aged 1-4 years, residing in metropolitan Melbourne, were invited to participate in the trial by peer educators from their respective communities using snowball and purposive sampling techniques. Target sample size was 600. Moreland, a culturally diverse, inner-urban metropolitan area of Melbourne, was chosen as the intervention site. The intervention comprised peer educator led community oral health education sessions and reorienting of dental health and family services through cultural Competency Organisational Review (CORe). ETHICS AND DISSEMINATION: Ethics approval for this trial was granted by the University of Melbourne Human Research Ethics Committee and the Department of Education and Early Childhood Development Research Committee. Study progress and output will be disseminated via periodic newsletters, peer-reviewed research papers, reports, community seminars and at National and International conferences. TRIAL REGISTRATION NUMBER: Australian New Zealand Clinical Trials Registry (ACTRN12611000532909).

How to improve postgenomic knowledge discovery using imputation.

While microarrays make it feasible to rapidly investigate many complex biological problems, their multistep fabrication has the proclivity for error at every stage. The standard tactic has been to either ignore or regard erroneous gene readings as missing values, though this assumption can exert a major influence upon postgenomic knowledge discovery methods like gene selection and gene regulatory network (GRN) reconstruction. This has been the catalyst for a raft of new flexible imputation algorithms including local least square impute and the recent heuristic collateral missing value imputation, which exploit the biological transactional behaviour of functionally correlated genes to afford accurate missing value estimation. This paper examines the influence of missing value imputation techniques upon postgenomic knowledge inference methods with results for various algorithms consistently corroborating that instead of ignoring missing values, recycling microarray data by flexible and robust imputation can provide substantial performance benefits for subsequent downstream procedures.

Ameliorative missing value imputation for robust biological knowledge inference.

Gene expression data is widely used in various post genomic analyses. The data is often probed using microarrays due to their ability to simultaneously measure the expressions of thousands of genes. The expression data, however, contains significant numbers of missing values, which can impact on subsequent biological analysis. To minimize the impact of these missing values, several imputation algorithms including Collateral Missing Value Estimation (CMVE), Bayesian Principal Component Analysis (BPCA), Least Square Impute (LSImpute), Local Least Square Impute (LLSImpute), and K-Nearest Neighbour (KNN) have been proposed. These algorithms, however, exploit either only the global or local correlation structure of the data, which normally can lead to higher estimation errors. This paper presents an Ameliorative Missing Value Imputation (AMVI) technique which has ability to exploit global/local and positive/negative correlations in a given dataset by automatic selection of the optimal number of predictor genes k using a wrapper non-parametric method based on Monte Carlo simulations. The AMVI technique has CMVE strategy at its core because CMVE has demonstrated improved performance compared to both low variance methods like BPCA, LLSImpute, and high variance methods such as KNN and ZeroImpute, as CMVE exploits positive/negative correlations. The performance of AMVI is compared with CMVE, BPCA, LLSImpute, and KNN by randomly removing between 1% and 15% missing values in eight different ovarian, breast cancer and yeast datasets. Together with the standard NRMS error metric, the True Positive (TP) rate of the significant genes selection, biological significance of the selected genes and the statistical significance test results are presented to investigate the impact of missing values on subsequent biological analysis. The enhanced performance of AMVI was demonstrated by its lower NRMS error, improved TP rate, bio significance of the selected genes and statistical significance test results, when compared with the aforementioned imputation methods across all the datasets. The results show that AMVI adapted to the latent correlation structure of the data and proved to be an effective and robust approach compared with the trial and error methodology for selecting k. The results confirmed that AMVI can be successfully applied to accurately impute missing values prior to any microarray data analysis.

Fusion of FNA-cytology and gene-expression data using Dempster-Shafer Theory of evidence to predict breast cancer tumors.

Decision-in decision-out fusion architecture can be used to fuse the outputs of multiple classifiers from different diagnostic sources. In this paper, Dempster-Shafer Theory (DST) has been used to fuse classification results of breast cancer data from two different sources: gene-expression patterns in peripheral blood cells and Fine-Needle Aspirate Cytology (FNAc) data. Classification of individual sources is done by Support Vector Machine (SVM) with linear, polynomial and Radial Base Function (RBF) kernels. Out put belief of classifiers of both data sources are combined to arrive at one final decision. Dynamic uncertainty assessment is based on class differentiation of the breast cancer. Experimental results have shown that the new proposed breast cancer data fusion methodology have outperformed single classification models.

Collateral missing value imputation: a new robust missing value estimation algorithm for microarray data.

MOTIVATION: Microarray data are used in a range of application areas in biology, although often it contains considerable numbers of missing values. These missing values can significantly affect subsequent statistical analysis and machine learning algorithms so there is a strong motivation to estimate these values as accurately as possible before using these algorithms. While many imputation algorithms have been proposed, more robust techniques need to be developed so that further analysis of biological data can be accurately undertaken. In this paper, an innovative missing value imputation algorithm called collateral missing value estimation (CMVE) is presented which uses multiple covariance-based imputation matrices for the final prediction of missing values. The matrices are computed and optimized using least square regression and linear programming methods. RESULTS: The new CMVE algorithm has been compared with existing estimation techniques including Bayesian principal component analysis imputation (BPCA), least square impute (LSImpute) and K-nearest neighbour (KNN). All these methods were rigorously tested to estimate missing values in three separate non-time series (ovarian cancer based) and one time series (yeast sporulation) dataset. Each method was quantitatively analyzed using the normalized root mean square (NRMS) error measure, covering a wide range of randomly introduced missing value probabilities from 0.01 to 0.2. Experiments were also undertaken on the yeast dataset, which comprised 1.7% actual missing values, to test the hypothesis that CMVE performed better not only for randomly occurring but also for a real distribution of missing values. The results confirmed that CMVE consistently demonstrated superior and robust estimation capability of missing values compared with other methods for both series types of data, for the same order of computational complexity. A concise theoretical framework has also been formulated to validate the improved performance of the CMVE algorithm. AVAILABILITY: The CMVE software is available upon request from the authors.