A University of Washington School of Public Health researcher has adapted a text-mining tool to identify new patterns in the electronic health records (EHR) of sepsis patients. The methodology could lead to more precise treatment of patients with this life-threatening response to infection.
Dr. Alison Fohner, assistant professor of epidemiology, led a project at Kaiser Permanente Northern California Division of Research, using a machine learning process to group electronic health record (EHR) terms that commonly show up together within the same patients’ health records in early hospitalization. Findings were published July 17 in the Journal of the American Medical Informatics Association.
“The possibilities in this space are pretty huge,” Dr. Fohner says. “Text-mining methods were originally designed to uncover meaning in free-form writing like blog posts, emails and social science research, but the analytical capabilities of text mining are immensely useful for other types of data as well.” Data from an integrated health-care system like Kaiser Permanente are well-suited for adapting these outside methods to novel applications, she says, because they are extremely detailed and comprehensive.
The results of the study confirmed the extreme diversity in sepsis presentation. Patterns in patients’ EHRs can be used to more objectively evaluate patient outcomes and hospital performances and to better understand patient responses to treatment in past and future clinical trials.Friday Letter Submission, Publish on August 30