Mining big data for key insights into healthcare, life sciences and social media at the Linguamatics San Francisco SeminarLinguamatics
Natural Language Processing (NLP) and text analytics experts from pharmaceutical and biotech companies, healthcare providers and payers gathered together to discuss the latest industry trends and to hear the product news and case studies from Linguamatics on August 26th.
The keynote presentation from Dr Gabriel Escobar was the highlight of the event, covering a rehospitalization prediction project that the Kaiser Permanente Department of Research have been working on in collaboration with Linguamatics.
The predictive model has been developed using a cohort of approximately 400,000 patients and combine scores from structured clinical data with factors derived from unstructured data using I2E.
Key factors that could affect a patient’s likelihood of rehospitalization are trapped in text; these include ambulatory status, social support network and functional status. I2E queries enabled KP to extract these factors and use them to indicate the accuracy of the structured data’s predictive score.
Leading the use of I2E in healthcare, Linguamatics exemplified how cancer centers are working together to develop queries for pathology reports, mining medical literature and predicting pneumonia from radiology reports. They also demonstrated a prototype application to match patients to clinical trials and a cohort selection tool using semantic tagging of patient narratives in the Apache Solr search engine.