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Data Analytics Framework

Using our standard Data Analysis Framework our clinical database software can be further developed for advanced Data-Analytics allowing us to obtain, integrate and analyse complex data sets from multiple data sources.


We can design tour software applications to perform quite advance data analysis and presents this information as complex studies. This allows examination of complex data interactions within biological systems and to model and discover emergent properties within genetic, tissue and patient data.


Data Analysis
Our Data Analysis service covers both qualitative and quantitative approaches to data analysis. Data collection is a precursor to data analysis, and data analysis is closely linked to data visualization and data modeling with the goal of highlighting useful information, suggesting conclusions, and supporting decision making. In statistical applications, some people divide data analysis into descriptive statistics, exploratory data analysis (EDA), and confirmatory data analysis (CDA). EDA focuses on discovering new features in the data and CDA on confirming or falsifying existing hypotheses. Predictive analytics focuses on application of statistical or structural models for predictive forecasting or classification.


Data Analytics Framework
Cohesion Medical's Data Analytics Framework service employs a body of methods that help to describe facts, detect patterns, develop explanations, and test hypotheses relating to you clinical data. Fundamentally, at its simplest level, data analysis produces numerical results - it finds the average number that describes a typical value and it finds differences among numbers. At its complex level, advanced data analysis allows us to investigate how these values originate in the first place in the real world. The Framework operates at following levels:

  • Basic Mode - performs easy data calculations and presents this information in the Studies section.Here, total values in speecific data-tables are presented in isolation.
  • Advanced Mode - the above situation is advanced by using data-analytics approaches across multiple and expansive data fields.
  • Research Mode - the data collated above as numerical results can be drilled down to see specific entries and compare them against other data-sets.



The Studies section allows users to realise the significant value of this patient data when collated and analysed. Data-Analytics models allows the system to examine data relating to Social Research, Epidemology, Postcode mapping, Demographics, Social  Status, Biometrics and Nutrition Studies


Studies of Disease and Conditions highlights values for the age at onset and descriptions of diagnosis and comorbidty. Currently, we record all the diagnosis details specified but this could be developed much more to allow deeper analysis of the disease, its progression and phases over time. Analysis of these pattern changes will allow "Event Markers" to be identified.


Genetic Studies collates information on ethnic origins and family inheritance factors. It is possible to migrate genetic data into the database in order that it be analysed with other patient data. This would allow genetic decoding to be related to environmental factors and event markers, or disease triggers. Algorithms for analysing these genetic factors can provide significant insight into the cause of psoriasis.