Dr. Hassanpour’s team will build and validate information-extraction and machine learning methods that use EHR text information to detect statistically significant connections between medical records, genetic mutations and targeted therapy responses.
The four-year, $1.5 million grant, awarded by the National Cancer Institute, will allow the research team to assess relationships between clinical and pathologic findings, patient genetic profiles and drug resistance. By combining this data, the researchers hope to create improved and personalized treatment strategies for non-small cell lung cancer patients.
“We expect our machine learning methods to identify NSCLC patients with clinically-actionable mutations based on tumor pathology reports and EMR data, and to provide an accurate, fast and inexpensive pre-selection method that can be utilized before performing time-intensive and expensive DNA sequencing to find the same mutations,” said Dr. Hassanpour, according to a news release.
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