scholarly journals Early Detection of Students at Risk – Predicting Student Dropouts Using Administrative Student Data and Machine Learning Methods

2018 ◽  
Author(s):  
Johannes Berens ◽  
Kerstin Schneider ◽  
Simon Görtz ◽  
Simon Oster ◽  
Julian Burghoff
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
Early Detection ◽  
Students At Risk ◽  
Learning Methods ◽  
Student Data ◽  
Machine Learning Methods

Novel Machine Learning Methods for ERP Analysis: A Validation From Research on Infants at Risk for Autism

2012 ◽  
Vol 37 (3) ◽  
pp. 274-298 ◽  
Author(s):  
Daniel Stahl ◽  
Andrew Pickles ◽  
Mayada Elsabbagh ◽  
Mark H. Johnson ◽  
The BASIS Team
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
Learning Methods ◽  
Machine Learning Methods

Prediction of Maternal Hemorrhage: Using Machine Learning to Identify Patients at Risk (Preprint)

2021 ◽  
Author(s):  
Jill M Westcott ◽  
Francine Hughes ◽  
Wenke Liu ◽  
Mark Grivainis ◽  
Iffath Hoskins ◽  
...  
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
Postpartum Hemorrhage ◽  
Mode Of Delivery ◽  
Receiver Operating Curve ◽  
Stage Model ◽  
Learning Methods ◽  
Second Stage ◽  
Machine Learning Methods ◽  
Patients At Risk

BACKGROUND Postpartum hemorrhage remains one of the largest causes of maternal morbidity and mortality in the United States. OBJECTIVE To utilize machine learning techniques to identify patients at risk for postpartum hemorrhage at obstetric delivery. METHODS Women aged 18 to 55 delivering at a major academic center from July 2013 to October 2018 were included for analysis (n = 30,867). A total of 497 variables were collected from the electronic medical record including demographic information, obstetric, medical, surgical, and family history, vital signs, laboratory results, labor medication exposures, and delivery outcomes. Postpartum hemorrhage was defined as a blood loss of ≥ 1000 mL at the time of delivery, regardless of delivery method, with 2179 positive cases observed (7.06%). Supervised learning with regression-, tree-, and kernel-based machine learning methods was used to create classification models based upon training (n = 21,606) and validation (n = 4,630) cohorts. Models were tuned using feature selection algorithms and domain knowledge. An independent test cohort (n = 4,631) determined final performance by assessing for accuracy, area under the receiver operating curve (AUC), and sensitivity for proper classification of postpartum hemorrhage. Separate models were created using all collected data versus limited to data available prior to the second stage of labor/at the time of decision to proceed with cesarean delivery. Additional models examined patients by mode of delivery. RESULTS Gradient boosted decision trees achieved the best discrimination in the overall model. The model including all data mildly outperformed the second stage model (AUC 0.979, 95% CI 0.971-0.986 vs. AUC 0.955, 95% CI 0.939-0.970). Optimal model accuracy was 98.1% with a sensitivity of 0.763 for positive prediction of postpartum hemorrhage. The second stage model achieved an accuracy of 98.0% with a sensitivity of 0.737. Other selected algorithms returned models that performed with decreased discrimination. Models stratified by mode of delivery achieved good to excellent discrimination, but lacked sensitivity necessary for clinical applicability. CONCLUSIONS Machine learning methods can be used to identify women at risk for postpartum hemorrhage who may benefit from individualized preventative measures. Models limited to data available prior to delivery perform nearly as well as those with more complete datasets, supporting their potential utility in the clinical setting. Further work is necessary to create successful models based upon mode of delivery. An unbiased approach to hemorrhage risk prediction may be superior to human risk assessment and represents an area for future research.

Using Machine Learning to Advance Early Warning Systems: Promise and Pitfalls

2020 ◽  
Vol 122 (14) ◽  
pp. 1-30
Author(s):  
James Soland ◽  
Benjamin Domingue ◽  
David Lang
Keyword(s):  
Machine Learning ◽  
High School ◽  
At Risk ◽  
Early Warning ◽  
Early Warning Systems ◽  
Machine Learning Techniques ◽  
Dropping Out ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Learning Techniques

Background/Context Early warning indicators (EWI) are often used by states and districts to identify students who are not on track to finish high school, and provide supports/interventions to increase the odds the student will graduate. While EWI are diverse in terms of the academic behaviors they capture, research suggests that indicators like course failures, chronic absenteeism, and suspensions can help identify students in need of additional supports. In parallel with the expansion of administrative data that have made early versions of EWI possible, new machine learning methods have been developed. These methods are data-driven and often designed to sift through thousands of variables with the purpose of identifying the best predictors of a given outcome. While applications of machine learning techniques to identify students at-risk of high school dropout have obvious appeal, few studies consider the benefits and limitations of applying those models in an EWI context, especially as they relate to questions of fairness and equity. Focus of Study In this study, we will provide applied examples of how machine learning can be used to support EWI selection. The purpose is to articulate the broad risks and benefits of using machine learning methods to identify students who may be at risk of dropping out. We focus on dropping out given its salience in the EWI literature, but also anticipate generating insights that will be germane to EWI used for a variety of outcomes. Research Design We explore these issues by using several hypothetical examples of how ML techniques might be used to identify EWI. For example, we show results from decision tree algorithms used to identify predictors of dropout that use simulated data. Conclusions/Recommendations Generally, we argue that machine learning techniques have several potential benefits in the EWI context. For example, some related methods can help create clear decision rules for which students are a dropout risk, and their predictive accuracy can be higher than for more traditional, regression-based models. At the same time, these methods often require additional statistical and data management expertise to be used appropriately. Further, the black-box nature of machine learning algorithms could invite their users to interpret results through the lens of preexisting biases about students and educational settings.

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