Unlocking the Power of Diverse Data Sources
Recently, our VP of Innovations, Dr. Frank J Manion, took part in a survey conducted by Intelligent Medical Objects (IMO) about how data scientists deal with the monotony of data cleaning and preparation. Dr. Manion emphasized that the success of clinical natural language processing (NLP) models largely depends on having access to accurate, well-annotated, and reliable input data.
“Our NLP tools allow us to build models from such high quality, “gold standard” datasets very rapidly, but we often must spend time to address issues of data standardization and harmonization so that they contain equivalent semantics," says Dr. Manion.
According to Dr. Manion, it's not enough to format or standardize the terminology used in data. It's also crucial to use data hygiene practices and tools to ensure consistent language and structure in the data. As more and more data is being integrated from various sources, there is a growing need for tools and frameworks that can provide better semantic harmonization between these diverse sources.
Download the IMO insight brief to learn how data science leaders handle data quality issues and reclaim their time.
