scholarly journals Characterization of indeterminate breast lesions on B-mode ultrasound using automated machine learning models

2020 ◽  
Vol 7 (05) ◽  
Author(s):  
Shuo Wang ◽  
Sihua Niu ◽  
Enze Qu ◽  
Flemming Forsberg ◽  
Annina Wilkes ◽  
...  
Keyword(s):  
Machine Learning ◽  
Breast Lesions ◽  
Learning Models ◽  
Automated Machine Learning ◽  
Machine Learning Models

scholarly journals Fast characterization of biomass and waste by infrared spectra and machine learning models

2020 ◽  
Vol 387 ◽  
pp. 121723 ◽  
Author(s):  
Junyu Tao ◽  
Rui Liang ◽  
Jian Li ◽  
Beibei Yan ◽  
Guanyi Chen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Infrared Spectra ◽  
Learning Models ◽  
Machine Learning Models

Characterization of Iranian Grapevine Cultivars Using Machine Learning Models

2019 ◽  
Vol 90 (3) ◽  
pp. 615-621 ◽  
Author(s):  
Bahman Panahi ◽  
Seyyed Abolghasem Mohammadi ◽  
Hamed Doulati-Baneh
Keyword(s):  
Machine Learning ◽  
Learning Models ◽  
Machine Learning Models

A web-based automated machine learning platform to analyze liquid biopsy data

Lab on a Chip ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 2166-2174
Author(s):  
Hanfei Shen ◽  
Tony Liu ◽  
Jesse Cui ◽  
Piyush Borole ◽  
Ari Benjamin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Liquid Biopsy ◽  
Learning Models ◽  
Learning Tool ◽  
Web Based ◽  
Learning Platform ◽  
Automated Machine Learning ◽  
Machine Learning Tool ◽  
Machine Learning Models

We have developed a web-based, self-improving and overfitting-resistant automated machine learning tool tailored specifically for liquid biopsy data, where machine learning models can be built without the user's input.

A Novel Methodology for the Characterization of Cutting Conditions in Turning Processes Using Machine Learning Models and Acoustic Emission Signals

2021 ◽  
pp. 556-566
Author(s):  
Telmo Fernández de Barrena ◽  
Juan Luis Ferrando ◽  
Ander García ◽  
Pedro Jose Arrazola ◽  
Jose Manuel Abete ◽  
...  
Keyword(s):  
Machine Learning ◽  
Acoustic Emission ◽  
Cutting Conditions ◽  
Learning Models ◽  
Machine Learning Models

scholarly journals Using Automated Machine Learning to Predict the Mortality of Patients With COVID-19: Prediction Model Development Study

10.2196/23458 ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. e23458
Author(s):  
Kenji Ikemura ◽  
Eran Bellin ◽  
Yukako Yagi ◽  
Henny Billett ◽  
Mahmoud Saada ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Ensemble Model ◽  
Learning Models ◽  
High Performing ◽  
Automated Machine Learning ◽  
Machine Learning Models

Background During a pandemic, it is important for clinicians to stratify patients and decide who receives limited medical resources. Machine learning models have been proposed to accurately predict COVID-19 disease severity. Previous studies have typically tested only one machine learning algorithm and limited performance evaluation to area under the curve analysis. To obtain the best results possible, it may be important to test different machine learning algorithms to find the best prediction model. Objective In this study, we aimed to use automated machine learning (autoML) to train various machine learning algorithms. We selected the model that best predicted patients’ chances of surviving a SARS-CoV-2 infection. In addition, we identified which variables (ie, vital signs, biomarkers, comorbidities, etc) were the most influential in generating an accurate model. Methods Data were retrospectively collected from all patients who tested positive for COVID-19 at our institution between March 1 and July 3, 2020. We collected 48 variables from each patient within 36 hours before or after the index time (ie, real-time polymerase chain reaction positivity). Patients were followed for 30 days or until death. Patients’ data were used to build 20 machine learning models with various algorithms via autoML. The performance of machine learning models was measured by analyzing the area under the precision-recall curve (AUPCR). Subsequently, we established model interpretability via Shapley additive explanation and partial dependence plots to identify and rank variables that drove model predictions. Afterward, we conducted dimensionality reduction to extract the 10 most influential variables. AutoML models were retrained by only using these 10 variables, and the output models were evaluated against the model that used 48 variables. Results Data from 4313 patients were used to develop the models. The best model that was generated by using autoML and 48 variables was the stacked ensemble model (AUPRC=0.807). The two best independent models were the gradient boost machine and extreme gradient boost models, which had an AUPRC of 0.803 and 0.793, respectively. The deep learning model (AUPRC=0.73) was substantially inferior to the other models. The 10 most influential variables for generating high-performing models were systolic and diastolic blood pressure, age, pulse oximetry level, blood urea nitrogen level, lactate dehydrogenase level, D-dimer level, troponin level, respiratory rate, and Charlson comorbidity score. After the autoML models were retrained with these 10 variables, the stacked ensemble model still had the best performance (AUPRC=0.791). Conclusions We used autoML to develop high-performing models that predicted the survival of patients with COVID-19. In addition, we identified important variables that correlated with mortality. This is proof of concept that autoML is an efficient, effective, and informative method for generating machine learning–based clinical decision support tools.

Improving XGBoost with Imagination Sampling

2020 ◽  
Vol 2 (1) ◽  
pp. 3-6
Author(s):  
Eric Holloway
Keyword(s):  
Machine Learning ◽  
General System ◽  
Learning Models ◽  
Starting Point ◽  
Machine Learning Models

Imagination Sampling is the usage of a person as an oracle for generating or improving machine learning models. Previous work demonstrated a general system for using Imagination Sampling for obtaining multibox models. Here, the possibility of importing such models as the starting point for further automatic enhancement is explored.

Epigenetic Target Prediction with Accurate Machine Learning Models

2021 ◽  
Author(s):  
Norberto Sánchez-Cruz ◽  
Jose L. Medina-Franco
Keyword(s):  
Machine Learning ◽  
Small Molecules ◽  
Predictive Models ◽  
Large Scale ◽  
Target Prediction ◽  
Quantitative Measure ◽  
Learning Models ◽  
Discovery Research ◽  
Drug Discovery Research ◽  
Machine Learning Models

<p>Epigenetic targets are a significant focus for drug discovery research, as demonstrated by the eight approved epigenetic drugs for treatment of cancer and the increasing availability of chemogenomic data related to epigenetics. This data represents a large amount of structure-activity relationships that has not been exploited thus far for the development of predictive models to support medicinal chemistry efforts. Herein, we report the first large-scale study of 26318 compounds with a quantitative measure of biological activity for 55 protein targets with epigenetic activity. Through a systematic comparison of machine learning models trained on molecular fingerprints of different design, we built predictive models with high accuracy for the epigenetic target profiling of small molecules. The models were thoroughly validated showing mean precisions up to 0.952 for the epigenetic target prediction task. Our results indicate that the herein reported models have considerable potential to identify small molecules with epigenetic activity. Therefore, our results were implemented as freely accessible and easy-to-use web application.</p>

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