25 research outputs found
The Iowa Homemaker vol.18, no.4
No full textA Queen of Homemakers by Harriet Beyer, page 2
Dining Midst Drama by Daisy Mary Kimberley, page 3
Scientific Fun by Ruth Stultz, page 4
A Recipe for Life by Helen Greene, page 5
Fashions Are Fancy Free by Polly Towne, page 6
On a European Honeymoon by Gaynold Carroll, page 7
Home Economics for Homemakers by Daisy Mary Kimberley, page 8
Designs for Richer Living by Marie Larson, page 9
What’s New in Home Economics edited by Marjorie Pettinger, page 10
Food for the Masculine Taste by Ida Halpin, page 12
Behind Bright Jackets edited by Winnifred Cannon, page 13
Help Yourself to Manners by Winnifred Cannon, page 14
Personality in Bloom by Edith Wahrenbrock, page 15
Notes for Music Lovers by Jean Metcalf, page 16
Alums in the News by Grace Strohmeier, page 18
Grooming Guide by Ruth Jensen, page 20
Keeping Posted by the editor, page 21</p
The Iowa Homemaker vol.18, no.5
No full textInside Information, page 1
Personalities Behind the Titles by Ethel Overholt, page 2
A B Cs of Health by Ruth Dahlberg, page 3
Tying Up Christmas by Ruth Stultz, page 4
Designing for Living by Myrtle Campbell, page 5
Secrets for Santa by Gaynold Carroll, page 6
Personalize Your Greeting by Marguerite Root, page 7
Can You Bake an Angel Cake? by Winnifred Cannon, page 8
Holiday Stamps of Approval by Helen Greene, page 9
Frozen Foods for Zero Weather by Harriet Beyer, page 10
A Season of Feasting by Jane Stallings, page 11
What’s New in Home Economics edited by Marjorie Pettinger, page 12
Home Economics at Home, page 14
Zipping It Up by Roberta Stock, page 15
Alums in the News by Grace Strohmeier, page 16
Knitting Knacks by Lois Madsen, page 17
Behind Bright Jackets edited by Winnifred Cannon, page 20
Catering to Coeds by Grace Strohmeier, page 20
Personality on Paper by Marian Van Meter, page 23
Keeping Posted by the editor, page 24</p
Rushlight: Volume XXXIII, March 1888, No.2 (typewritten)
No full textWheaton College (Norton, MA) student literary magazine.Les Danaides, A Description (Essay)A Reception (Essay)Tariff!!! (Essay)A Picture from a Rail-way Car (Essay)A Classic Epic (Poem)A Personal Reminiscence (Story)At the Stroke of Twelve (Essay)Items (Seminary Life and Interests)Editoria
A comparison of machine learning methods for risk stratification after acute coronary syndrome
No full textThesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.Cataloged from PDF version of thesis.Includes bibliographical references (pages 45-46).Accurate risk stratification is essential for the proper management of patients after an acute coronary syndrome (ACS). Currently, the most widely accepted metrics for risk stratification are risk scores such as the Thrombolysis in Myocardial Infarction (TIMI) score and Global Registry of Acute Coronary Events (GRACE) score. However, prior work has shown that many patients who are not traditionally defined as high-risk by the TIMI or GRACE scores suffer adverse events such as cardiovascular death. We therefore wish to find a method of risk stratifying patients that has greater discriminatory ability than the existing scoring metrics. We wish to find a model that can assign a risk score using data that is routinely collected for patients during a hospital stay. Using a dataset of over 4200 patients, we developed logistic regression, neural network, and regression tree models to risk stratify patients for one-year cardiovascular death post ACS. The resulting models were highly predictive of risk compared to the TIMI score. Our findings highlight the efficacy of using machine learning models trained on commonly collected clinical data to risk stratify patients.by Stephanie Pavlick.M. Eng
Choosing Clinical Variables for Risk Stratification Post-Acute Coronary Syndrome
No full textMost risk stratification methods use expert opinion to identify a fixed number of clinical variables that have prognostic significance. In this study our goal was to develop improved metrics that utilize a variable number of input parameters. We first used Bootstrap Lasso Regression (BLR) - a Machine Learning method for selecting important variables - to identify a prognostic set of features that identify patients at high risk of death 6-months after presenting with an Acute Coronary Syndrome. Using data derived from the Global Registry of Acute Coronary Events (GRACE) we trained a logistic regression model using these features and evaluated its performance on a development set (N = 43,063) containing patients who have values for all features, and a separate dataset (N = 6,363) that contains patients who have missing feature values. The final model, Ridge Logistic Regression with Variable Inputs (RLRVI), uses imputation to estimate values for missing features. BLR identified 19 features, 8 of which appear in the GRACE score. RLRVI had modest, yet statistically significant, improvement over the standard GRACE score on both datasets. Moreover, for patients who are relatively low-risk (GRACE < /=87), RLRVI had an AUC and Hazard Ratio of 0.754 and 6.27, respectively, vs. 0.688 and 2.46 for GRACE, (p < 0.007). RLRVI has improved discriminatory performance on patients who have values for the 8 GRACE features plus any subset of the 11 non-GRACE features. Our results demonstrate that BLR and data imputation can be used to obtain improved risk stratification metrics, particularly for patients who are classified as low risk using traditional methods
Choosing Clinical Variables for Risk Stratification Post-Acute Coronary Syndrome
No full textMost risk stratification methods use expert opinion to identify a fixed number of clinical variables that have prognostic significance. In this study our goal was to develop improved metrics that utilize a variable number of input parameters. We first used Bootstrap Lasso Regression (BLR) – a Machine Learning method for selecting important variables – to identify a prognostic set of features that identify patients at high risk of death 6-months after presenting with an Acute Coronary Syndrome. Using data derived from the Global Registry of Acute Coronary Events (GRACE) we trained a logistic regression model using these features and evaluated its performance on a development set (N = 43,063) containing patients who have values for all features, and a separate dataset (N = 6,363) that contains patients who have missing feature values. The final model, Ridge Logistic Regression with Variable Inputs (RLRVI), uses imputation to estimate values for missing features. BLR identified 19 features, 8 of which appear in the GRACE score. RLRVI had modest, yet statistically significant, improvement over the standard GRACE score on both datasets. Moreover, for patients who are relatively low-risk (GRACE≤87), RLRVI had an AUC and Hazard Ratio of 0.754 and 6.27, respectively, vs. 0.688 and 2.46 for GRACE, (p < 0.007). RLRVI has improved discriminatory performance on patients who have values for the 8 GRACE features plus any subset of the 11 non-GRACE features. Our results demonstrate that BLR and data imputation can be used to obtain improved risk stratification metrics, particularly for patients who are classified as low risk using traditional methods
Computer-Assisted Mathematics Instruction for Students With Specific Learning Disability: A Review of the Literature
No full textThis review was conducted to evaluate the current body of scholarly research regarding the use of computer-assisted instruction (CAI) to teach mathematics to students with specific learning disability (SLD). For many years, computers are utilized for educational purposes. However, the effectiveness of CAI for teaching mathematics to this specific group of students is unclear. First, a brief review of the diagnosis of SLD, the importance of mathematics instruction for these students, and the use of computers in the classroom is provided. Next, a review of the current body of research is presented. Finally, suggestions for future research are discussed. Since 1981, a total of 25 research studies have been published, focusing exclusively on using CAI for teaching mathematics to students with SLD. This review examines the current body of research for this area. In addition, the author provides recommendations for future research on this important subject for this category of students
Weighting protein ensembles with Bayesian statistics and small-angle X-ray scattering data
No full textThesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 52-54).Intrinsically Disordered Proteins (IDPs) are involved in a number of neurodegenerative disorders such as Parkinson's and Alzheimer's diseases. Their disordered nature allows them to sample many different conformations, so their structures must be represented as ensembles. Typically, structural ensembles for IDPs are constructed by generating a set of conformations that yield ensemble averages that agree with pre-existing experimental data. However, as the number of experimental constraints is usually much smaller than the degrees of freedom in the protein, the ensemble construction process is under-determined, meaning there are many different ensembles that agree with a given set of experimental observables. The Variational Bayesian Weighting program uses Bayesian statistics to fit conformational ensembles, and in doing so also quantifies the uncertainty in the underlying ensemble. The present work sought to introduce new functionality to this program, allowing it to use data obtained from Small-Angle X-ray Scattering.by Molly A. Schmidt.M. Eng
Identifying unreliable predictions in clinical risk models
No full text© 2020, The Author(s). The ability to identify patients who are likely to have an adverse outcome is an essential component of good clinical care. Therefore, predictive risk stratification models play an important role in clinical decision making. Determining whether a given predictive model is suitable for clinical use usually involves evaluating the model’s performance on large patient datasets using standard statistical measures of success (e.g., accuracy, discriminatory ability). However, as these metrics correspond to averages over patients who have a range of different characteristics, it is difficult to discern whether an individual prediction on a given patient should be trusted using these measures alone. In this paper, we introduce a new method for identifying patient subgroups where a predictive model is expected to be poor, thereby highlighting when a given prediction is misleading and should not be trusted. The resulting “unreliability score” can be computed for any clinical risk model and is suitable in the setting of large class imbalance, a situation often encountered in healthcare settings. Using data from more than 40,000 patients in the Global Registry of Acute Coronary Events (GRACE), we demonstrate that patients with high unreliability scores form a subgroup in which the predictive model has both decreased accuracy and decreased discriminatory ability
