David Carrell, PhD, is an assistant investigator who develops and applies technology for extracting rich information from unstructured clinical text, such as physician progress notes. This work uses state-of-the-art clinical natural language processing (NLP) technologies in single- and multi-site settings.
An example of this work is an NLP system to identify women who have been diagnosed with recurrent breast cancer. Despite being a common and consequential clinical diagnosis, recurrent breast cancer cannot be tracked reliably using standard medical codes found in a person’s chart. Supported by a grant from the National Cancer Institute, he and his colleagues used information from clinician progress notes, radiology reports, and pathology reports to classify women by breast cancer recurrence.
Working with teams of researchers inside and outside Kaiser Permanente Washington Health Research Institute, Dr. Carrell has applied similar precision phenotyping methods to identify evidence of carotid artery stenosis, colon polyps, problem use of prescription opioids, and colonoscopy quality.
Dr. Carrell’s current research projects are applying NLP and machine learning methods to improve medication safety surveillance (through the Food and Drug Administration Sentinel Initiative) and to evaluate the impact on drug use disorder diagnosis and treatment of Kaiser Permanente Washington patients screened for unhealthy cannabis and other drug use. His ongoing work also includes development and application of automated algorithms based on electronic health record data to identify patients with particular health conditions (called “patient phenotypes”) for use in genetic and epidemiological research.
Surveillance methods for adverse events associated with medication exposure, including problem use of prescription opioids
Methods for using structured and unstructured electronic health record data to identify patients with (or without) specific clinical conditions or phenotypes for large scale epidemiological and genomic studies
Identifying recurrent breast cancer using EHR text; Colonoscopy quality metrics
Recurrent breast cancer; Colonoscopy quality; Extracting information from clinical text; Automated de-identification of clinical text; Methods for applying NLP methods in multi-site research
Prevention and treatment
Carrell D. Whither the Revolution? The Toucqueville Review. 1987, 8:39-92
Rosenthal E, Jarvik GP, Crosslin DR, Gordon S, Carrell D, Stanaway IB, Larson EB, Grafton J, Wei-Qi W, Denny JC, Shah A, Ritchie M, Hakonarson H, Rasmussen-Torvik LJ, Connoly JJ, Sturm A, Feng Q, Kullo IJ. Association between triglycerides, known risk SNVs, and conserved rare variation in SLC25A40 in a multi-ancestry cohort. BMC Med Genomics. 2021 Jan 6;14(1):11. doi: 10.1186/s12920-020-00854-2. PubMed
Suri P, Stanaway IB, Zhang Y, Freidin MB, Tsepilov YA, Carrell DS, Williams FMK, Aulchenko YS, Hakonarson H, Namjou B, Crosslin DR, Jarvik GP, Lee MT. Genome-wide association studies of low back pain and lumbar spinal disorders using electronic health record data identify a locus associated with lumbar spinal stenosis. Pain. 2021 Aug 1;162(8):2263-2272. doi: 10.1097/j.pain.0000000000002221. PubMed
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