Personalized medicine is becoming a reality. As clinicians begin to analyze information stored in historic medical records combined with data obtained by new technologies, they are constantly challenged with data sets of increasing size and complexity. The sheer complexity of data sets often obscures biologically meaningful relationships between data fields.
The goal is to provide patients with customized therapies reflecting their individual clinical phenotype to avoid unnecessary over- or under-treatment and minimize the risk of preventable side effects. This requires quantifiable patient stratification and phenotypic classification based on diverse data from various clinical disciplines, which is increasingly augmented with DNA polymorphism data from genome-wide association studies.
While the need for well-developed algorithms and analytical workflows is self-evident, the presentation of results is equally important.
Viscovery solutions provide a powerful visual approach to explore data, reveal relevant dependences and gain insight. Predictive models created by Viscovery increase effectiveness, both in the laboratory and in the clinical setting. For example, large gene expression and DNA sequence data sets can be analyzed to identify “gene signatures” in cancer cells, analyze the molecular mechanisms underlying an adverse drug reaction or reveal the effects of mutations in different pathogenic microbes.
Viscovery solutions have been used successfully in health informatics for:
- Patient evaluation and stratification
- Clinical research
- Gene expression analysis and metabolic profiling
- Medical diagnostics and pathological classification
- Exploration of biological data patterns
- Knowledge discovery in medical data bases
- Patient risk analysis
- Health care system analysis
- Treatment and prescription cost analysis
Viscovery’s flagship project in personalized medicine is a collaboration with the CIRO+ Center of Excellence for Chronic Organ Failure in The Netherlands. The project utilizes Viscovery data mining to analyze data collected from patients suffering from chronic obstructive pulmonary disease (COPD) and to apply the obtained results to improve therapy outcomes.
Among the findings regarding the severity of the disease and patient response to treatment, a major analysis of the comorbidities of COPD has found broad attention. This study, based on the visual clustering of Viscovery, has been published in the prestigious AJRCCM journal. More papers describing results of the analytical Viscovery approach have been published or are in preparation.
Contact us at email@example.com to learn more about how we can support you in health informatics and set up a consultative interview and/or demo.