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Physical Sciences and Mathematics Commons™
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Full-Text Articles in Physical Sciences and Mathematics
Diagnosis-Specific Readmission Risk Prediction Using Electronic Health Data: A Retrospective Cohort Study, Courtney Hebert, Chaitanya Shivade, Randi Foraker, Jared Wasserman, Caryn Roth, Hagop Mekhjan, Stanley Lemeshow, Peter Embi
Diagnosis-Specific Readmission Risk Prediction Using Electronic Health Data: A Retrospective Cohort Study, Courtney Hebert, Chaitanya Shivade, Randi Foraker, Jared Wasserman, Caryn Roth, Hagop Mekhjan, Stanley Lemeshow, Peter Embi
Dartmouth Scholarship
Background: Readmissions after hospital discharge are a common occurrence and are costly for both hospitals and patients. Previous attempts to create universal risk prediction models for readmission have not met with success. In this study we leveraged a comprehensive electronic health record to create readmission-risk models that were institution- and patient- specific in an attempt to improve our ability to predict readmission. Methods: This is a retrospective cohort study performed at a large midwestern tertiary care medical center. All patients with a primary discharge diagnosis of congestive heart failure, acute myocardial infarction or pneumonia over a two-year time period were …
Multiple Subject Barycentric Discriminant Analysis (Musubada): How To Assign Scans To Categories Without Using Spatial Normalization, Hervé Abdi, Lynne J. Williams, Andrew C. Connolly, M. Ida Gobbini
Multiple Subject Barycentric Discriminant Analysis (Musubada): How To Assign Scans To Categories Without Using Spatial Normalization, Hervé Abdi, Lynne J. Williams, Andrew C. Connolly, M. Ida Gobbini
Dartmouth Scholarship
We present a new discriminant analysis (DA) method called Multiple Subject Barycentric Discriminant Analysis (MUSUBADA) suited for analyzing fMRI data because it handles datasets with multiple participants that each provides different number of variables (i.e., voxels) that are themselves grouped into regions of interest (ROIs). Like DA, MUSUBADA (1) assigns observations to predefined categories, (2) gives factorial maps displaying observations and categories, and (3) optimally assigns observations to categories. MUSUBADA handles cases with more variables than observations and can project portions of the data table (e.g., subtables, which can represent participants or ROIs) on the factorial maps. Therefore MUSUBADA can …
Dna Methylation Arrays As Surrogate Measures Of Cell Mixture Distribution, Eugene Houseman, William P. Accomando, Devin C. Koestler, Brock C. Christensen, Carmen J. Marsit
Dna Methylation Arrays As Surrogate Measures Of Cell Mixture Distribution, Eugene Houseman, William P. Accomando, Devin C. Koestler, Brock C. Christensen, Carmen J. Marsit
Dartmouth Scholarship
There has been a long-standing need in biomedical research for a method that quantifies the normally mixed composition of leukocytes beyond what is possible by simple histological or flow cytometric assessments. The latter is restricted by the labile nature of protein epitopes, requirements for cell processing, and timely cell analysis. In a diverse array of diseases and following numerous immune-toxic exposures, leukocyte composition will critically inform the underlying immuno-biology to most chronic medical conditions. Emerging research demonstrates that DNA methylation is responsible for cellular differentiation, and when measured in whole peripheral blood, serves to distinguish cancer cases from controls.