Conducting Medical Research from Electronic Health Records

Using natural language processing to find necessary samples

To discover links between genes and disease, researchers typically recruit individual patients with and without the disease of interest; have them sign consent forms; take their medical histories; and analyze their blood samples. As well as being time-consuming and expensive, it can be hard to get a large enough sample of patients. But now researchers have shown there might be another way—using electronic medical records to identify patients with the desired phenotypes and then obtaining their anonymized leftover blood samples to test for genetic information.

 

After lengthy training, i2b2’s natural language processing software scans clinical histories, tagging words and phrases that describe smoking history and making a diagnosis (right-hand column). With training, the NLP tools were able to equate “smoking history” with “smokes often,” distinguishing both from “non-smoker.” Clinical experts also reviewed random results and computer scientists refined the search terms to clarify ambiguities like “tob.” Reprinted from S. Murphy, et. al, Instrumenting the health care enterprise for discovery research in the genomic era, Genome Research , 19(9): 1675–1681 (2009).“We showed that we can actually conduct full-blown association studies to find the right patients with the right phenotypes and connect them to the right samples,” says Isaac Kohane, MD, PhD, professor at Harvard Medical School and director of i2b2 (Informatics for Integrating Biology and the Bedside), the National Center for Biomedical Computing that conducted the study published in the September 2009 issue of Genome Research. “It’s soup to nuts work.”

 

With the help of natural language processing (NLP), the i2b2 researchers set out to use a large, available, cheap data pool: the electronic medical record archives for 2.6 million patients at Partners Healthcare System in Massachusetts. Although doctor’s notes are notoriously unstandardized, NLP tools can break them into their smallest components, analyzing parts of speech and how words are joined. The i2b2 team sought to identify pools of patients with rheumatoid arthritis, asthma, secondary illnesses and risk factors for asthma (for example, smoking history). Along the way, clinical experts gauged the accuracy of the process and helped refine search terms. “It takes three to four months of iteration with expert clinicians until we get it just right,” Kohane says. In addition, the researchers developed a system to access anonymously saved leftover blood samples from the identified populations to use for future studies requiring genetic data.

 

And the NLP tools did a pretty good job: Of about 98,000 patients identified as having asthma, 82 percent of the time the experts reviewing the files concurred in that diagnosis; 90 percent of the patients identified with a history of smoking had such a history; and of the 4,618 NLP-identified rheumatoid arthritis sufferers, 92 percent had definite arthritis (according to expert review) while 98 percent probably did. By studying these electronic patients, the researchers successfully reproduced several results from past clinical research. And while the clinical studies had paid an average of $650 to characterize and obtain blood samples from each patient, i2b2 spent $20 to $100.

 

“This paper represents very encouraging results using free open-source software,” says Chunhua Weng, PhD, assistant professor of biomedical informatics at Columbia University. She says the next step is to include information such as how long an individual smoked or when symptoms began in patient descriptions. Kohane agrees, noting that researchers are working to include time-varying data in i2b2’s model.



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