Researchers from Queen's University have developed a new toolkit that harnesses the power of 'Big Data' for digital health with the aim of driving improvements in patient care and outcomes through data-driven innovation.
Overview of eHDPrep workflow. The ordering of steps reflects logical dependencies. Dashed arrows and boxes signify optional steps. Following import, the semantic characteristics of the data are established, missing values are dealt with, and a series of operations may be performed. “Natural Language Processing” is only required if free-text variables are present. “Merge Variables” is an optional step for user-defined merging operations with functionality to measure information loss.
in making clinical decisions, such as determining which treatment may be more effective to treat a certain type of cancer.
The tool also enables a better understanding of health data by joining information together into higher level concepts that can reveal non-obvious links between different patients—in a process called"semantic enrichment."
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