Although significant progress has been made in the biomedical domain toward the development of such systems for text analysis, fine-tuning is usually necessary to achieve acceptable performance for specific use cases. enlarged) identified in each report to be used for search indexing. vestibular aqueduct) and attributes (e.g. The system would generate labels that correspond to entities (e.g. Ideally, we would like to utilize a fully automated knowledge extraction system for which it would be necessary only to supply radiology reports. Therefore, to facilitate the effective use of anatomic information contained in radiology reports for audiology research, we adopted a machine learning procedure. As shown in the work presented here, these approaches lack sensitivity (recall) for this data set, and thus fail to identify most of the reports that contain an abnormality. Two straightforward methods to be considered are keyword searches and International Classification of Diseases (ICD9) based searches. Because the reports are unlabeled, it is difficult for researchers to select reports that contain abnormalities in a specific region, e.g. The Audiological and Genetic Database (AudGenDB), a public, de-identified observational research database derived from EHR data sources, contains over 16,000 de-identified, unlabeled radiologist reports. In audiologic and otologic research, the ability to use anatomic information described in radiology is essential to understand the causes of hearing loss for research subjects and to develop new treatment modalities. These methods have been applied to automate EHR text analysis in a variety of studies including phenotype extraction, adverse drug-event identification, and domain-specific radiology report classification. Natural language processing (NLP) and machine learning (ML) methods present an alternative to manual text review. Such manual review may be time consuming and expensive, particularly for large data sets. In the absence of automated processing, this requires trained data abstractors to manually review the text sources and identify discrete values of interest. Prior to research utilization, EHR text data, such as physician notes and radiology reports typically must be converted to discrete values, e.g. Kullo has authored 248 scientific papers, and he is the co-Principal Investigator of the Mayo electronic Medical Records and GEnomics (eMERGE) grant.Electronic health records (EHRs) contain significant amounts of unstructured text that pose a challenge to their secondary use as a research data source. He directs the Early Atherosclerosis and Familial Hypercholesterolemia Clinics at Mayo Clinic Rochester, these evaluate and treat patients with premature vascular disease and genetic dyslipidemias.ĭr. Kullo led the MI-GENES study - the first genomic medicine clinical trial to demonstrate that incorporating genetic risk information can alter a health outcome. His research program is funded by the NIH since 2003 and includes both genomic medicine discovery and implementation projects.Kullo's research is focused on the genetics of atherosclerosis and lipid disorders. He completed residency training in Internal Medicine, including the Clinician Investigator training pathway and subsequently a Fellowship in Cardiovascular Diseases at Mayo Clinic. Iftikhar Kullo is a Professor of Medicine at Mayo Clinic Rochester, MN.
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