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

Assessment of urban flood susceptibility using semi-supervised machine learning model

2019 ◽  
Vol 659 ◽  
pp. 940-949 ◽  
Author(s):  
Gang Zhao ◽  
Bo Pang ◽  
Zongxue Xu ◽  
Dingzhi Peng ◽  
Liyang Xu
Keyword(s):  
Machine Learning ◽  
Learning Model ◽  
Supervised Machine Learning ◽  
Urban Flood ◽  
Flood Susceptibility ◽  
Machine Learning Model

scholarly journals GP.6 Improving Triaging of EEG Referrals for Rule out Infantile Spasms (ITERIS)

2021 ◽  
Vol 48 (s3) ◽  
pp. S13-S13
Author(s):  
D Djordjevic ◽  
J Tracey ◽  
M Alqahtani ◽  
J Boyd ◽  
C Go
Keyword(s):  
Machine Learning ◽  
Predictive Factors ◽  
Predictive Accuracy ◽  
Learning Model ◽  
Infantile Spasms ◽  
Low Risk ◽  
Machine Learning Techniques ◽  
Risk Category ◽  
Point System ◽  
Machine Learning Model

Background: Infantile spasms (IS) is a devastating pediatric seizure disorder for which EEG referrals are prioritized at the Hospital for Sick Children, representing a resource challenge. The goal of this study was to improve the triaging system for these referrals. Methods: Part 1: descriptive analysis was performed retrospectively on EEG referrals. Part 2: prospective questionnaires were used to determine relative risk of various predictive factors. Part 3: electronic referral form was amended to include 5 positive predictive factors. A triage point system was tested by assigning EEGs as high risk (3 days), standard risk (1 week), or low risk (2 weeks). A machine learning model was developed. Results: Most EEG referrals were from community pediatricians with a low yield of IS diagnoses. Using the 5 predictive factors, the proposed triage system accurately diagnosed all IS within 3 days. No abnormal EEGs were missed in the low-risk category. The machine learning model had over 90% predictive accuracy and will be prospectively tested. Conclusions: Improving EEG triaging for IS may be possible to prioritize higher risk patients. Machine Learning techniques can potentially be applied to help with predictions. We hope that our findings will ultimately improve resource utilization and patient care.

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.

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

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.

scholarly journals Short communication: Insect detection using a machine learning model

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Yash Munnalal Gupta ◽  
SOMJIT HOMCHAN
Keyword(s):  
Machine Learning ◽  
Short Communication ◽  
Learning Model ◽  
Acheta Domesticus ◽  
Supervised Machine Learning ◽  
Correct Identification ◽  
Species Classification ◽  
Still Images ◽  
Machine Learning Model ◽  
Biological Studies

Abstract. Homchan S, Gupta YM. 2020. Short communication: Insect detection using a machine learning model. Nusantara Bioscience 13: 69-73. The key step in characterizing any organisms and their gender highly relies on correct identification of specimens. Here we aim to classify insect and their sex by supervised machine learning (ML) model. In the present preliminary study, we used a newly developed graphical user interface (GUI) based platform to create a machine learning model for classifying two economically important cricket species. This study aims to develop ML model for Acheta domesticus and Gryllus bimaculatus species classification and sexing. An experimental investigation was conducted to use Google teachable machine GTM for preliminary cricket species detection and sexing using pre-processed 2646 still images. An alternative method for image processing is used to extract still images from high-resolution video for optimum accuracy. Out of the 2646 images, 2247 were used for training ML model and 399 were used for testing the trained model. The prediction accuracy of trained model had 100 % accuracy to identify both species and their sex. The developed trained model can be integrated into the mobile application for cricket species classification and sexing. The present study may guide professionals in the field of life science to develop ML models based on image classification, and serve as an example for researchers and taxonomists to employ machine learning for species classification and sexing in the preliminary analysis. Apart from our main goals, the paper also intends to provide the possibility of ML models in biological studies and to conduct the preliminary assessment of biodiversity.

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