A Semi-Supervised Machine Learning Model for Risk Analysis of Urinary Tract Infections in People with Dementia

2020 ◽  
Author(s):  
Honglin Li ◽  
Shirin Enshaeifar ◽  
Severin Skillman ◽  
Andreas Markides ◽  
Mark Kenny ◽  
...  
Keyword(s):  
Machine Learning ◽  
Urinary Tract ◽  
Risk Analysis ◽  
Urinary Tract Infections ◽  
Learning Model ◽  
Supervised Machine Learning ◽  
People With Dementia ◽  
Machine Learning Model ◽  
Tract Infections

scholarly journals 2107. Decision Trees vs. Neural Networks for Supervised Machine Learning-Based Prediction of Healthcare-Associated Urinary Tract Infections

2018 ◽  
Vol 5 (suppl_1) ◽  
pp. S618-S618
Author(s):  
Philip Zachariah ◽  
Elioth Mirsha Sanabria Buenaventura ◽  
Jianfang Liu ◽  
Bevin Cohen ◽  
David Yao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Urinary Tract ◽  
Decision Trees ◽  
Urinary Tract Infections ◽  
Supervised Machine Learning ◽  
Tract Infections ◽  
Healthcare Associated

scholarly journals Machine learning models predicting multidrug resistant urinary tract infections using “DsaaS”

2020 ◽  
Vol 21 (S10) ◽  
Author(s):  
Alessio Mancini ◽  
Leonardo Vito ◽  
Elisa Marcelli ◽  
Marco Piangerelli ◽  
Renato De Leone ◽  
...  
Keyword(s):  
Machine Learning ◽  
Urinary Tract ◽  
Urinary Tract Infections ◽  
Multidrug Resistant ◽  
Learning Models ◽  
Tract Infections ◽  
Machine Learning Models

scholarly journals Translationese and Post-editese: How comparable is comparable quality?

2018 ◽  
Vol 16 ◽  
Author(s):  
Joke Daems ◽  
Orphée De Clercq ◽  
Lieve Macken
Keyword(s):  
Machine Learning ◽  
Learning Model ◽  
Supervised Machine Learning ◽  
Original Text ◽  
High Quality ◽  
Machine Learning Model ◽  
Comparable Quality

Whereas post-edited texts have been shown to be either of comparable quality to human translations or better, one study shows that people still seem to prefer human-translated texts. The idea of texts being inherently different despite being of high quality is not new. Translated texts, for example, are also different from original texts, a phenomenon referred to as ‘Translationese’. Research into Translationese has shown that, whereas humans cannot distinguish between translated and original text, computers have been trained to detect Translationese successfully. It remains to be seen whether the same can be done for what we call Post-editese. We first establish whether humans are capable of distinguishing post-edited texts from human translations, and then establish whether it is possible to build a supervised machine-learning model that can distinguish between translated and post-edited text.

scholarly journals Predicting Sentiment Polarity of Microblogs using an LSTM – CNN Deep Learning Model

2019 ◽  
Vol 8 (6) ◽  
pp. 4368-4373
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Prediction Accuracy ◽  
Short Term Memory ◽  
Learning Model ◽  
Supervised Machine Learning ◽  
Machine Learning Model ◽  
Proposed Model ◽  
Network Approaches

In this paper we propose a novel supervised machine learning model to predict the polarity of sentiments expressed in microblogs. The proposed model has a stacked neural network structure consisting of Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN) layers. In order to capture the long-term dependencies of sentiments in the text ordering of a microblog, the proposed model employs an LSTM layer. The encodings produced by the LSTM layer are then fed to a CNN layer, which generates localized patterns of higher accuracy. These patterns are capable of capturing both local and global long-term dependences in the text of the microblogs. It was observed that the proposed model performs better and gives improved prediction accuracy when compared to semantic, machine learning and deep neural network approaches such as SVM, CNN, LSTM, CNN-LSTM, etc. This paper utilizes the benchmark Stanford Large Movie Review dataset to show the significance of the new approach. The prediction accuracy of the proposed approach is comparable to other state-of-art approaches.

c-Cbl expression as a novel predictive marker of survival in patients with metastatic colorectal cancer.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. e15090-e15090
Author(s):  
Shin Yin Lee ◽  
Vijaya B. Kolachalama ◽  
Umit Tapan ◽  
Janice Weinberg ◽  
Jean M. Francis ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Machine Learning ◽  
Medical Center ◽  
Area Under The Curve ◽  
Predictive Marker ◽  
Learning Model ◽  
Negative Regulator ◽  
Supervised Machine Learning ◽  
Molecular Features ◽  
Machine Learning Model

e15090 Background: Aberrant hyperactive Wnt/ß-catenin signaling is critical in colorectal cancer (CRC) tumorigenesis. Casitas B-lineage Lymphoma (c-Cbl) is a negative regulator of Wnt signaling, and functions as a tumor suppressor. The objective of this study was to evaluate c-Cbl expression as a predictive marker of survival in patients with metastatic CRC (mCRC). Methods: Patients with mCRC treated at Boston University Medical Center between 2004 and 2014 were analyzed. c-Cbl and nuclear ß-catenin expression was quantified in explanted biopsies using a customized color-based image segmentation pipeline. Quantification was normalized to the total tumor area in an image, and deemed ‘low’ or ‘high’ according to the mean normalized values of the cohort. A supervised machine-learning model based on bootstrap aggregating was constructed with c-Cbl expression as the input feature and 3-year survival as output. Results: Of the 72 subjects with mCRC, 52.78% had high and 47.22% had low c-Cbl expression. Patients with high c-Cbl had significantly better median overall survival than those with low c-Cbl expression (3.7 years vs. 1.8 years; p = 0.0026), and experienced superior 3-year survival (47.37% vs 20.59%; p = 0.017). Intriguingly, nuclear ß-catenin expression did not correlate with survival. No significant differences were detected between high and low c-Cbl groups in baseline characteristics (demographics, comorbidities), tumor-related parameters (primary tumor location, number of metastasis, molecular features) or therapy received (surgery, chemotherapy regimen). A 5-fold cross-validated machine-learning model associated with 3-year survival demonstrated an area under the curve of 0.729, supporting c-Cbl expression as a predictor of mCRC survival. Conclusions: Our results show that c-Cbl expression is associated with and predicts mCRC survival. Demonstration of these findings despite the small cohort size underscores the power of quantitative histology and machine-learning application. While further work is needed to validate c-Cbl as a novel biomarker of mCRC survival, this study supports c-Cbl as a regulator of Wnt/ß-catenin signaling and a suppressor of other oncogenes in CRC tumorigenesis.

Supervised machine learning model to predict oncotype DX risk category in patients over age 50

2021 ◽  
Author(s):  
Kate R. Pawloski ◽  
Mithat Gonen ◽  
Hannah Y. Wen ◽  
Audree B. Tadros ◽  
Donna Thompson ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Model ◽  
Oncotype Dx ◽  
Supervised Machine Learning ◽  
Risk Category ◽  
Machine Learning Model
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