Using Machine Learning to Predict Patterns of Employment and Day Program Participation

Broda, Michael; Bogenschutz, Matthew; Dinora, Parthenia; Prohn, Seb M.; Lineberry, Sarah; Ross, Erica

doi:10.1352/1944-7558-126.6.477

Cited by 4 publications

(3 citation statements)

References 46 publications

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“…In (7), CI is the sample consistency index, RI is the random consistency index, and n is the index quantity. The value of the random consistency index can be obtained by looking up the table [21].…”

Section: Index Weighting Methods Of Machine Model For Collegementioning

confidence: 99%

“…The most accurate model is the random forest classifier, which predicts the employment results of adults with iodine deficient disorder, with an accuracy of 89% in the test sample and 80% in the persistence sample. These results suggest that potential machine learning tools can examine the results used to support the employment value of iodine deficient disorder patients in evidence-based decision-making [7]. Awujoola et al proposed a modern, accurate, and valuable machine learning classification model.…”

Section: Recent Related Workmentioning

confidence: 99%

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The Use of Machine Learning Model in the Evaluation of College Students’ Employment and Entrepreneurship Level

2022

Wireless Communications and Mobile Computing

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This exploration aims to investigate the employment and entrepreneurship of college students. College students majoring in information and computing sciences in Xinxiang City are taken as the research object. A machine learning model for college students’ employment and entrepreneurship is established. Experiments are conducted using Python language and a machine learning framework. First, based on the employment and entrepreneurship indexes of college students in machine learning, the initial data of college students’ training quality evaluation are obtained from the educational administration system, library management system, college evaluation materials, and questionnaire survey. Then, the combination weighting method is adopted to determine the index weight, quantify the data, and modify the parameters of the data provided by the framework. The Gaussian kernel is selected as the kernel function, and the sample data used by the machine learning model are labeled. Finally, the sample data are employed to train and test the model. After the consistency test, the model reaches the optimal value after 7 iterations, with an error rate of 0.01 and an accuracy rate of 99%. The final error rate of entrepreneurship and innovation model based on machine learning is less than 0.1, which is consistent with the actual situation. The model can meet the requirements of college students’ entrepreneurship and employment evaluation. It proves that it can be applied to the research of college students’ employment and entrepreneurship and has certain theoretical guidance and practical significance for the evaluation of college students’ employment and entrepreneurship level.

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Section: Index Weighting Methods Of Machine Model For Collegementioning

confidence: 99%

Section: Recent Related Workmentioning

confidence: 99%

The Use of Machine Learning Model in the Evaluation of College Students’ Employment and Entrepreneurship Level

2022

Wireless Communications and Mobile Computing

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“…We tested both classification tree and random forest models, finding best fit based on the random forest algorithm. A full accounting of our procedures may be found in Broda et al ( 42 ).…”

Section: Promising Practicesmentioning

confidence: 99%

Promising Practices in the Frontiers of Quality Outcome Measurement for Intellectual and Developmental Disability Services

Bogenschutz

Dinora

Lineberry

et al. 2022

Front. Rehabilit. Sci.

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Medicaid Home and Community-Based Services (HCBS) for people with intellectual and developmental disabilities (IDD) are vital for supporting people with IDD to live well in their communities, but there are not set standards for monitoring quality outcomes related to HCBS. In this paper, we propose promising practices for improving the quality of HCBS outcome measurement, based both in the literature and our own experience conducting an extensive U.S. state-level study. Specifically, we discuss: (1) using merged administrative datasets, (2) developing high-quality psychometrics that attend to ecological issues in measurement, (3) using advanced statistical analyses, and (4) creating immersive, user-friendly translational dissemination products. We conclude by suggesting what we see as important new frontiers for researchers to consider in order to enhance the quality of HCBS outcome measurement for people with IDD in the future.

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Examining Use of Pharmacotherapy for Behavioral Support Among Americans with IDD Using Machine Learning

Bogenschutz,

Broda,

Dinora

et al. 2024

Journal of Mental Health Research in Intellectual Disabilities

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Using Machine Learning to Predict Patterns of Employment and Day Program Participation

Cited by 4 publications

References 46 publications

The Use of Machine Learning Model in the Evaluation of College Students’ Employment and Entrepreneurship Level

The Use of Machine Learning Model in the Evaluation of College Students’ Employment and Entrepreneurship Level

Promising Practices in the Frontiers of Quality Outcome Measurement for Intellectual and Developmental Disability Services

Examining Use of Pharmacotherapy for Behavioral Support Among Americans with IDD Using Machine Learning

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