Breast cancer data analysis for survivability studies and prediction

© 2017 Elsevier B.V. Background Breast cancer is the most common cancer affecting females worldwide. Breast cancer survivability prediction is challenging and a complex research task. Existing approaches engage statistical methods or supervised machine learning to assess/predict the survival prospects of patients. Objective The main objectives of this paper is to develop a robust data analytical model which can assist in (i) a better understanding of breast cancer survivability in presence of missing data, (ii) providing better insights into factors associated with patient survivability, and (iii) establishing cohorts of patients that share similar properties. Methods Unsupervised data mining methods viz. the self-organising map (SOM) and density-based spatial clustering of applications with noise (DBSCAN) is used to create patient cohort clusters. These clusters, with associated patterns, were used to train multilayer perceptron (MLP) model for improved patient survivability analysis. A large dataset available from SEER program is used in this study to identify patterns associated with the survivability of breast cancer patients. Information gain was computed for the purpose of variable selection. All of these methods are data-driven and require little (if any) input from users or experts. Results SOM consolidated patients into cohorts of patients with similar properties. From this, DBSCAN identified and extracted nine cohorts (clusters). It is found that patients in each of the nine clusters have different survivability time. The separation of patients into clusters improved the overall survival prediction accuracy based on MLP and revealed intricate conditions that affect the accuracy of a prediction. Conclusions A new, entirely data driven approach based on unsupervised learning methods improves understanding and helps identify patterns associated with the survivability of patient. The results of the analysis can be used to segment the historical patient data into clusters or subsets, which share common variable values and survivability. The survivability prediction accuracy of a MLP is improved by using identified patient cohorts as opposed to using raw historical data. Analysis of variable values in each cohort provide better insights into survivability of a particular subgroup of breast cancer patients

Genetic analysis of Myanmar Vigna species in responses to salt stress at the seedling stage

Twelve (12) Vigna genotypes were investigated for the evaluation of their tolerance levels in responses to four concentrations of NaCl (0, 75, 150 and 225 mM) at seedling stage. In the investigation, salt stress inhibited almost all the growth parameters as well as relative water content;  however, the degree of reduction was highly dependent on different genotypes and salinity levels. Generally, the control plants showed higher degree of all measured parameters than those of salt stress plants. Analysis of the heredity parameters based on the 12 investigated genotypes showed different genotypic variance of the salt tolerance index (STI) values. Salinity stress induced two new bands between 45 and 22 kDa, respectively, in salt tolerant genotypes. Furthermore, band intensity of the salt treated genotypes was higher than the control plants. Ward’s clustering technique was clearly divided into two clusters, A and B, according to their levels of salt tolerance. Considering their STI values of growth parameters, two genotypes V7 and V4 were identified as salt tolerant, whereas, V2, V6, V9, V8, V11 and V1 were recognized as salinity susceptible genotypes. These results suggest that, the genetically diverseaccessions resistant to salt stresses within the Vigna genotypes can be of considerable practical value for studying the mechanism of salt tolerance and for the provision of genetic resources for salinity breeding program.Key words: Cluster analysis, heritability, salt tolerance, SDS-PAGE, Vigna

Australia's national health programs: An ontological mapping

Australia has a large number of health program initiatives whose comprehensive assessment will help refine and redefine priorities by highlighting areas of emphasis, under-emphasis, and non-emphasis. The objectives of our research are to: (a) systematically map all the programs onto an ontological framework, and (b) systemically analyse their relative emphases at different levels of granularity. We mapped all the health program initiatives onto an ontology with five dimensions, namely: (a) Policy-scope, (b) Policy-focus, (c) Outcomes, (d) Type of care, and (e) Population served. Each dimension is expanded into a taxonomy of its constituent elements. Each combination of elements from the five dimensions is a possible policy initiative component. There are 30,030 possible components encapsulated in the ontology. It includes, for example: (a) National financial policies on accessibility of preventive care for family, and (b) Local-urban regulatory policies on cost of palliative care for individual-aged. Four of the authors mapped all of Australia's health programs and initiatives on to the ontology. Visualizations of the data are used to highlight the relative emphases in the program initiatives. The dominant emphasis of the program initiatives is: [National] [educational, personnel-physician, information] policies on [accessibility, quality] of [preventive, wellness] care for the [community]. However, although (a) information is emphasized technology is not and (b) accessibility and quality are emphasized cost, satisfaction, and quality are not. The ontology and the results of the mapping can help systematically reassess and redirect the relative emphases of the programs and initiatives from a systemic perspective

Applying a novel combination of techniques to develop a predictive model for diabetes complications

© 2015 Sangi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Among the many related issues of diabetes management, its complications constitute the main part of the heavy burden of this disease. The aim of this paper is to develop a risk advisor model to predict the chances of diabetes complications according to the changes in risk factors. As the starting point, an inclusive list of (k) diabetes complications and (n) their correlated predisposing factors are derived from the existing endocrinology text books. A type of data meta-analysis has been done to extract and combine the numeric value of the relationships between these two. The whole n (risk factors) - k (complications) model was broken down into k different (n-1) relationships and these (n-1) dependencies were broken into n (1-1) models. Applying regression analysis (seven patterns) and artificial neural networks (ANN), we created models to show the (1-1) correspondence between factors and complications. Then all 1-1 models related to an individual complication were integrated using the naïve Bayes theorem. Finally, a Bayesian belief network was developed to show the influence of all risk factors and complications on each other. We assessed the predictive power of the 1-1 models by R2, F-ratio and adjusted R2 equations; sensitivity, specificity and positive predictive value were calculated to evaluate the final model using real patient data. The results suggest that the best fitted regression models outperform the predictive ability of an ANN model, as well as six other regression patterns for all 1-1 models

Architecture of a consent management suite and integration into IHE-based regional health information networks

<p>Abstract</p> <p>Background</p> <p>The University Hospital Heidelberg is implementing a Regional Health Information Network (RHIN) in the Rhine-Neckar-Region in order to establish a shared-care environment, which is based on established Health IT standards and in particular Integrating the Healthcare Enterprise (IHE). Similar to all other Electronic Health Record (EHR) and Personal Health Record (PHR) approaches the chosen Personal Electronic Health Record (PEHR) architecture relies on the patient's consent in order to share documents and medical data with other care delivery organizations, with the additional requirement that the German legislation explicitly demands a patients' opt-in and does not allow opt-out solutions. This creates two issues: firstly the current IHE consent profile does not address this approach properly and secondly none of the employed intra- and inter-institutional information systems, like almost all systems on the market, offers consent management solutions at all. Hence, the objective of our work is to develop and introduce an extensible architecture for creating, managing and querying patient consents in an IHE-based environment.</p> <p>Methods</p> <p>Based on the features offered by the IHE profile Basic Patient Privacy Consent (BPPC) and literature, the functionalities and components to meet the requirements of a centralized opt-in consent management solution compliant with German legislation have been analyzed. Two services have been developed and integrated into the Heidelberg PEHR.</p> <p>Results</p> <p>The standard-based Consent Management Suite consists of two services. The Consent Management Service is able to receive and store consent documents. It can receive queries concerning a dedicated patient consent, process it and return an answer. It represents a centralized policy enforcement point. The Consent Creator Service allows patients to create their consents electronically. Interfaces to a Master Patient Index (MPI) and a provider index allow to dynamically generate XACML-based policies which are stored in a CDA document to be transferred to the first service. Three workflows have to be considered to integrate the suite into the PEHR: recording the consent, publishing documents and viewing documents.</p> <p>Conclusions</p> <p>Our approach solves the consent issue when using IHE profiles for regional health information networks. It is highly interoperable due to the use of international standards and can hence be used in any other region to leverage consent issues and substantially promote the use of IHE for regional health information networks in general.</p

Community and School-Based Health Education for Dengue Control in Rural Cambodia: A Process Evaluation

Dengue fever continues to be a major public health problem in Cambodia, with significant impact on children. Health education is a major means for prevention and control of the National Dengue Control Program (NDCP), and is delivered to communities and in schools. Drawing on data collected in 2003–2004 as part of an ethnographic study conducted in eastern Cambodia, we explore the approaches used in health education and their effectiveness to control dengue. Community health education is provided through health centre outreach activities and campaigns of the NDCP, but is not systematically evaluated, is under-funded and delivered irregularly; school-based education is restricted in terms of time and lacks follow-up in terms of practical activities for prevention and control. As a result, adherence is partial. We suggest the need for sustained routine education for dengue prevention and control, and the need for approaches to ensure the translation of knowledge into practice

Association analyses identify 31 new risk loci for colorectal cancer susceptibility

Colorectal cancer (CRC) is a leading cause of cancer-related death worldwide, and has a strong heritable basis. We report a genome-wide association analysis of 34,627 CRC cases and 71,379 controls of European ancestry that identifies SNPs at 31 new CRC risk loci. We also identify eight independent risk SNPs at the new and previously reported European CRC loci, and a further nine CRC SNPs at loci previously only identified in Asian populations. We use in situ promoter capture Hi-C (CHi-C), gene expression, and in silico annotation methods to identify likely target genes of CRC SNPs. Whilst these new SNP associations implicate target genes that are enriched for known CRC pathways such as Wnt and BMP, they also highlight novel pathways with no prior links to colorectal tumourigenesis. These findings provide further insight into CRC susceptibility and enhance the prospects of applying genetic risk scores to personalised screening and prevention