Healthcare data analytics ranges from basic statistical summaries of data and inferences to advanced predictive models. These models help to gain insight into disease progression and they support of the process of selecting the right treatment for individual patients. Statistical methods used include:
- Regression analysis
- Quantitative analysis
- Unsupervised / supervised learning
- Pattern matching
- Descriptive / inferential statistics
- Structured data analysis
- Risk stratification
- Identification of abnormal patterns
- Statistical analysis of large historical data
- Processing large time-series data
One factor that these methods have in common is that they reveal patterns in data, and from these patterns, inferences can be made to support decisions.
Data analysts spend the bulk of their time on getting, cleaning and preparing data, which is the process of preparing and selecting variables for data sets that will be used by analytical tools. Each data analysis project has its own requirements for variables (features), and during the analysis, more knowledge is gained about variables which prove to be useful, and, about the additional variables that are required. Given a repository of healthcare data, the ability to quickly prepare data sets with selected and constructed features will enable data analysts to test hypotheses quickly, thus increasing productivity.
To aid in dataset creation for healthcare research, MGRID has developed Aperture. Aperture removes the need to manually craft SQL or scripts to select, merge and manipulate data data, which speeds up the time of data set creation from weeks to hours. Please refer to the Aperture product page for more information.