461 research outputs found
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Rock Springs_Nocomis leptocephalus_Garcia
No full textBiodiversity information of headwater fish community in Rock Springs Park, Gwinnett County GA and DNA sequence information for single sample of Nocomis leptocephalus (Bluehead Chub)
Student Engagement and Success Newsletter, May-June 2022
No full textMay-June 2022 issue of Student Engagement and Success Newsletter, a newsletter published by SES at Georgia Gwinnett Colleg
Little Mulberry_Lepomis auritus_Regnier
No full textBiodiversity information of headwater fish community in Little Mulberry Park, Gwinnett County GA and DNA sequence information for single sample of Lepomis auritus (Redbreast sunfish)
Little Mulberry_Semotilus atromaculatus_Morse
No full textBiodiversity information of headwater fish community in Little Mulberry Park, Gwinnett County GA and DNA sequence information for single sample of Semotilus atromaculatus (Creek Chub)
Globe, September 2022, blog posts
No full textSeptember, 2022 blog posts from The Globe GGC (theglobeggc.net), published by Georgia Gwinnett College. Note that these posts are unduplicated in published issues.September, 2022 blog posts from The Globe GGC (theglobeggc.net), published by Georgia Gwinnett College. Contents: "Softball Carries Week While Baseball Breaks Even" (September 11, 2022), "The Student Body Elects Anthony Thomas as SGA’s New President" (September 23, 2022), "Men's Soccer Went Undefeated" (September 23, 2022
Socioeconomic and livelihood impacts within Bangkok’s expanding metropolitan region
Get PDFRecent national development plans in Thailand have incorporated concepts of sustainability, livelihood rights, and human dignity. Yet, development and urban expansion have unfolded in unexpected ways, complicating the socioeconomic and ecological integrity of peri-urban and rural spaces. This paper explores the ways in which urban expansion and state development within rural peripheries reshape political economies and, in so doing, the nature of vulnerability and precarity. Using ethnographic data collected among agrarian households in Samut Prakan province and among domestic migrant laborers in the Bangkok Metropolitan Region (BMR), the research considers the socioeconomic and ecological effects of peripheral areas’ tighter integration into expanding urban geographies. In effect, to what degree does urban development unfolding in the BMR improve people’s lives and, simultaneously, rework the dynamics of vulnerability and precarity experienced among those laboring in marginal spaces of the economy? A broad array of ethnographic and Landsat data demonstrates that families and individuals must renegotiate livelihood strategies to mitigate the sociopolitical, economic, and environmental outcomes of development. The findings demonstrate how agrarian families manage the structural and stochastic shocks and pressures of development in urbanizing landscapes and what this means for the future of smallholders situated within changing national economies.Ethnographic research was supported through grants provided by Santa Clara University, Missouri State University, and Georgia Gwinnett College, including grant numbers JFDL0001, TTRY0019, and DPROV074
Predictive Model for ICU Readmission Based on Discharge Summaries Using Machine Learning and Natural Language Processing
Get PDFAMA Style
Orangi-Fard N, Akhbardeh A, Sagreiya H. Predictive Model for ICU Readmission Based on Discharge Summaries Using Machine Learning and Natural Language Processing. Informatics. 2022; 9(1):10. https://doi.org/10.3390/informatics9010010
Chicago/Turabian Style
Orangi-Fard, Negar, Alireza Akhbardeh, and Hersh Sagreiya. 2022. "Predictive Model for ICU Readmission Based on Discharge Summaries Using Machine Learning and Natural Language Processing" Informatics 9, no. 1: 10. https://doi.org/10.3390/informatics9010010AMA Style
Orangi-Fard N, Akhbardeh A, Sagreiya H. Predictive Model for ICU Readmission Based on Discharge Summaries Using Machine Learning and Natural Language Processing. Informatics. 2022; 9(1):10. https://doi.org/10.3390/informatics9010010
Chicago/Turabian Style
Orangi-Fard, Negar, Alireza Akhbardeh, and Hersh Sagreiya. 2022. "Predictive Model for ICU Readmission Based on Discharge Summaries Using Machine Learning and Natural Language Processing" Informatics 9, no. 1: 10. https://doi.org/10.3390/informatics9010010Predicting ICU readmission risk will help physicians make decisions regarding discharge.
We used discharge summaries to predict ICU 30-day readmission risk using text mining and machine
learning (ML) with data from the Medical Information Mart for Intensive Care III (MIMIC-III).We
used Natural Language Processing (NLP) and the Bag-of-Words approach on discharge summaries
to build a Document-Term-Matrix with 3000 features. We compared the performance of support
vector machines with the radial basis function kernel (SVM-RBF), adaptive boosting (AdaBoost),
quadratic discriminant analysis (QDA), least absolute shrinkage and selection operator (LASSO),
and Ridge Regression. A total of 4000 patients were used for model training and 6000 were used
for validation. Using the bag-of-words determined by NLP, the area under the receiver operating
characteristic (AUROC) curve was 0.71, 0.68, 0.65, 0.69, and 0.65 correspondingly for SVM-RBF,
AdaBoost, QDA, LASSO, and Ridge Regression. We then used the SVM-RBF model for feature
selection by incrementally adding features to the model from 1 to 3000 bag-of-words. Through this
exhaustive search approach, only 825 features (words) were dominant. Using those selected features,
we trained and validated all ML models. The AUROC curve was 0.74, 0.69, 0.67, 0.70, and 0.71
respectively for SVM-RBF, AdaBoost, QDA, LASSO, and Ridge Regression. Overall, this technique
could predict ICU readmission relatively well
