scholarly journals Machine Learning Models for Genetic Risk Assessment of Infants with Non-syndromic Orofacial Cleft

2018 ◽  
Vol 16 (5) ◽  
pp. 354-364 ◽  
Author(s):  
Shi-Jian Zhang ◽  
Peiqi Meng ◽  
Jieni Zhang ◽  
Peizeng Jia ◽  
Jiuxiang Lin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Assessment ◽  
Genetic Risk ◽  
Orofacial Cleft ◽  
Learning Models ◽  
Genetic Risk Assessment ◽  
Machine Learning Models

scholarly journals Ambient Monitoring of Gait and Machine Learning Models for Dynamic and Short-Term Falls Risk Assessment in People With Dementia

2021 ◽  
Author(s):  
Navid Korhani ◽  
Babak Taati ◽  
Andrea Iaboni ◽  
Andrea Sabo ◽  
Sina Mehdizadeh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Assessment ◽  
Fall Risk ◽  
Learning Models ◽  
Short Term ◽  
Ambient Monitoring ◽  
Clinical Assessments ◽  
People With Dementia ◽  
Time Of Admission ◽  
Machine Learning Models

Data consists of baseline clinical assessments of gait, mobility, and fall risk at the time of admission of 54 adults with dementia. Furthermore, it includes the participants' daily medication intake in three medication categories, and frequent assessments of gait performed via a computer vision-based ambient monitoring system.

scholarly journals Risk Assessment in Energy Infrastructure Installations by Horizontal Directional Drilling Using Machine Learning

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 289
Author(s):  
Maria Krechowicz ◽  
Adam Krechowicz
Keyword(s):  
Machine Learning ◽  
Risk Assessment ◽  
Assessment Process ◽  
Future Research ◽  
Directional Drilling ◽  
Ann Model ◽  
Learning Models ◽  
Horizontal Directional Drilling ◽  
Risk Assessment Process ◽  
Machine Learning Models

Nowadays we can observe a growing demand for installations of new gas pipelines in Europe. A large number of them are installed using trenchless Horizontal Directional Drilling (HDD) technology. The aim of this work was to develop and compare new machine learning models dedicated for risk assessment in HDD projects. The data from 133 HDD projects from eight countries of the world were gathered, profiled, and preprocessed. Three machine learning models, logistic regression, random forests, and Artificial Neural Network (ANN), were developed to predict the overall HDD project outcome (failure free installation or installation likely to fail), and the occurrence of identified unwanted events. The best performance in terms of recall and accuracy was achieved for the developed ANN model, which proved to be efficient, fast and robust in predicting risks in HDD projects. Machine learning applications in the proposed models enabled eliminating the involvement of a group of experts in the risk assessment process and therefore significantly lower the costs associated with the risk assessment process. Future research may be oriented towards developing a comprehensive risk management system, which will enable dynamic risk assessment taking into account various combinations of risk mitigation actions.

Comparing different venous thromboembolism risk assessment machine learning models in Chinese patients

2019 ◽  
Vol 26 (1) ◽  
pp. 26-34 ◽  
Author(s):  
Xin Wang ◽  
Yu‐Qing Yang ◽  
Si‐Hua Liu ◽  
Xin‐Yu Hong ◽  
Xue‐Feng Sun ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Assessment ◽  
Venous Thromboembolism ◽  
Chinese Patients ◽  
Learning Models ◽  
Machine Learning Models

scholarly journals Ambient Monitoring of Gait and Machine Learning Models for Dynamic and Short-Term Falls Risk Assessment in People With Dementia

2021 ◽  
Author(s):  
Navid Korhani ◽  
Babak Taati ◽  
Andrea Iaboni ◽  
Andrea Sabo ◽  
Sina Mehdizadeh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Assessment ◽  
Fall Risk ◽  
Learning Models ◽  
Short Term ◽  
Ambient Monitoring ◽  
Clinical Assessments ◽  
People With Dementia ◽  
Time Of Admission ◽  
Machine Learning Models

Data consists of baseline clinical assessments of gait, mobility, and fall risk at the time of admission of 54 adults with dementia. Furthermore, it includes the participants' daily medication intake in three medication categories, and frequent assessments of gait performed via a computer vision-based ambient monitoring system.

scholarly journals Bayesian and Machine Learning Models for Genomic Prediction of Anterior Cruciate Ligament Rupture in the Canine Model

2020 ◽  
Vol 10 (8) ◽  
pp. 2619-2628
Author(s):  
Lauren A. Baker ◽  
Mehdi Momen ◽  
Kore Chan ◽  
Nathan Bollig ◽  
Fernando Brito Lopes ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Genomic Prediction ◽  
Genetic Risk ◽  
Cruciate Ligament ◽  
Acl Rupture ◽  
Labrador Retriever ◽  
Learning Models ◽  
Anterior Cruciate ◽  
Machine Learning Models

Anterior cruciate ligament (ACL) rupture is a common, debilitating condition that leads to early-onset osteoarthritis and reduced quality of human life. ACL rupture is a complex disease with both genetic and environmental risk factors. Characterizing the genetic basis of ACL rupture would provide the ability to identify individuals that have high genetic risk and allow the opportunity for preventative management. Spontaneous ACL rupture is also common in dogs and shows a similar clinical presentation and progression. Thus, the dog has emerged as an excellent genomic model for human ACL rupture. Genome-wide association studies (GWAS) in the dog have identified a number of candidate genetic variants, but research in genomic prediction has been limited. In this analysis, we explore several Bayesian and machine learning models for genomic prediction of ACL rupture in the Labrador Retriever dog. Our work demonstrates the feasibility of predicting ACL rupture from SNPs in the Labrador Retriever model with and without consideration of non-genetic risk factors. Genomic prediction including non-genetic risk factors approached clinical relevance using multiple linear Bayesian and non-linear models. This analysis represents the first steps toward development of a predictive algorithm for ACL rupture in the Labrador Retriever model. Future work may extend this algorithm to other high-risk breeds of dog. The ability to accurately predict individual dogs at high risk for ACL rupture would identify candidates for clinical trials that would benefit both veterinary and human medicine.

scholarly journals Rupture Risk Assessment for Cerebral Aneurysm Using Interpretable Machine Learning on Multidimensional Data

2020 ◽  
Vol 11 ◽  
Author(s):  
Chubin Ou ◽  
Jiahui Liu ◽  
Yi Qian ◽  
Winston Chong ◽  
Xin Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Assessment ◽  
Aneurysm Rupture ◽  
Multidimensional Data ◽  
Rupture Risk ◽  
Learning Models ◽  
Machine Learning Model ◽  
Score Method ◽  
Machine Learning Models ◽  
Better Than

Background: Assessment of cerebral aneurysm rupture risk is an important task, but it remains challenging. Recent works applying machine learning to rupture risk evaluation presented positive results. Yet they were based on limited aspects of data, and lack of interpretability may limit their use in clinical setting. We aimed to develop interpretable machine learning models on multidimensional data for aneurysm rupture risk assessment.Methods: Three hundred seventy-four aneurysms were included in the study. Demographic, medical history, lifestyle behaviors, lipid profile, and morphologies were collected for each patient. Prediction models were derived using machine learning methods (support vector machine, artificial neural network, and XGBoost) and conventional logistic regression. The derived models were compared with the PHASES score method. The Shapley Additive Explanations (SHAP) analysis was applied to improve the interpretability of the best machine learning model and reveal the reasoning behind the predictions made by the model.Results: The best machine learning model (XGBoost) achieved an area under the receiver operating characteristic curve of 0.882 [95% confidence interval (CI) = 0.838–0.927], significantly better than the logistic regression model (0.779; 95% CI = 0.729–0.829; P = 0.002) and the PHASES score method (0.758; 95% CI = 0.713–0.800; P = 0.001). Location, size ratio, and triglyceride level were the three most important features in predicting rupture. Two typical cases were analyzed to demonstrate the interpretability of the model.Conclusions: This study demonstrated the potential of using machine learning for aneurysm rupture risk assessment. Machine learning models performed better than conventional statistical model and the PHASES score method. The SHAP analysis can improve the interpretability of machine learning models and facilitate their use in a clinical setting.

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>

Sign in / Sign up

Export Citation Format

Share Document