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Chapter 11 presents classification with support vector machines – details of the algorithms for linear and nonlinear SVMs. Discussed are also kernel functions, hyperparameters, variable importance measures, and cost-sensitive SVMs.
Chapter 10 covers the random forests algorithm for classification. Presented are also the impurity metrics applicable to splitting nodes in classification trees (Gini, entropy, and misclassification impurity), as well as permutation-based and impurity-based variable importance measures.
This chapter interrogates corpus data to analyze the three alternations subject to study in this book one by one using a battery of state-of-the-art analysis techniques in addition to customary descriptive statistics, Conditional Random Forest (CRF) modeling and mixed-effects logistic regression analysis. The goal of the chapter is to uncover qualitative generalizations: for example, we see that while effect directions of constraints on variation are generally stable across varieties of English, effects strengths can be significantly different.
We investigated clinical and demographic variables to better understand their relationship to hospital length of stay for patients involuntarily committed to California state psychiatric hospitals under the state’s incompetent to stand trial (IST) statutes. Additionally, we determined the most important variables in the model that influenced patient length of stay.
Methods
We retrospectively studied all patients admitted as IST to California state psychiatric hospitals during the period January 1, 2010 through June 30, 2018 (N = 20 041). Primary diagnosis, total number of violent acts while hospitalized, age at admission, treating hospital, level of functioning at admission, ethnicity, sex, and having had a previous state hospital admission were evaluated using a parametric survival model.
Results
The analysis showed that the most important variables related to length of stay were (1) diagnosis, (2) number of violent acts while hospitalized, and (3) age of admission. Specifically, longer length of stay was associated with (1) having a diagnosis of schizophrenia or neurocognitive disorder, (2) one or more violent acts, and (3) older age at admission. The other variables studied were also statistically significant, but not as influential in the model.
Conclusions
We found significant relations between length of stay and the variables studied, with the most important variables being (1) diagnosis, (2) number of physically violent acts, and (3) age at admission. These findings emphasize the need for treatments to target cognitive issues in the seriously mentally ill as well as treatment of violence and early identification of violence risk factors.
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