Mining unstructured data to support medical research has been prevalent for many years. This requires analysis of Big Data sets and often includes manual chart review to identify patients and extract specific attributes. Chart review is extremely costly and time consuming, and it is hard to achieve a good coverage of variations in language and format.
How can we reduce the manual effort in mining unstructured patient data?
Linguamatics Health, powered by I2E, allows large-scale data exploration and rapid extraction of attributes that significantly reduce manual curation.
- Quickly find documents containing clinical attributes of interest in Big Data, such as biomarker values, diagnoses, or adverse events
- Easily construct queries that extract clinical attributes in Big Data to provide training material for machine-learning algorithms
- Identify patients for clinical and observational studies based on clinical attributes in unstructured text
- Mine scientific literature for insights into genetic mutation and disease association
To find out more:
Read our application note on the power of text mining for precision medicine research.