The clinical course of multiple sclerosis (MS) is highly variable, and research data collection is costly and time consuming. We evaluated natural language processing techniques applied to electronic medical records (EMR) to identify MS patients and the key clinical traits of their disease course.We used four algorithms based on ICD-9 codes, text keywords, and medications […]
Tag Archives: Data Mining
Automated extraction of clinical traits of multiple sclerosis in electronic medical records.
Davis MF, Sriram S, Bush WS, Denny JC, Haines JL,. The clinical course of multiple sclerosis (MS) is highly variable, and research data collection is costly and time consuming. We evaluated natural language processing techniques applied to electronic medical records (EMR) to identify MS patients and the key clinical traits of their disease course.We used […]
Putting pleiotropy and selection into context defines a new paradigm for interpreting genetic data.
Natural selection shapes many human genes, including some related to complex diseases. Understanding how selection affects genes, especially pleiotropic ones, may be important in evaluating disease associations and the role played by environmental variation. This may be of particular interest for genes with antagonistic roles that cause divergent patterns of selection. The lectin-like low-density lipoprotein […]
Putting pleiotropy and selection into context defines a new paradigm for interpreting genetic data.
Predazzi IM, Rokas A, Deinard A, Schnetz-Boutaud N, Williams ND, Bush WS, Tacconelli A, Friedrich K, Fazio S, Novelli G, Haines JL, Sirugo G, Williams SM,. Natural selection shapes many human genes, including some related to complex diseases. Understanding how selection affects genes, especially pleiotropic ones, may be important in evaluating disease associations and the […]
Mind the gap: resources required to receive, process and interpret research-returned whole genome data.
Most genotype-phenotype studies have historically lacked population diversity, impacting the generalizability of findings and thereby limiting the ability to equitably implement precision medicine. This well-documented problem has generated much interest in the ascertainment of new cohorts with an emphasis on multiple dimensions of diversity, including race/ethnicity, gender, age, socioeconomic status, disability, and geography. The most […]
Mind the gap: resources required to receive, process and interpret research-returned whole genome data.
Most genotype-phenotype studies have historically lacked population diversity, impacting the generalizability of findings and thereby limiting the ability to equitably implement precision medicine. This well-documented problem has generated much interest in the ascertainment of new cohorts with an emphasis on multiple dimensions of diversity, including race/ethnicity, gender, age, socioeconomic status, disability, and geography. The most […]
Mind the gap: resources required to receive, process and interpret research-returned whole genome data.
Most genotype-phenotype studies have historically lacked population diversity, impacting the generalizability of findings and thereby limiting the ability to equitably implement precision medicine. This well-documented problem has generated much interest in the ascertainment of new cohorts with an emphasis on multiple dimensions of diversity, including race/ethnicity, gender, age, socioeconomic status, disability, and geography. The most […]