Traumatic brain injury risk assessment with smart technology

2021 ◽  
pp. 154851292110085
Author(s):  
Chiming Huang ◽  
Rosa H Huang ◽  
Bani Yaghoub Majid
Keyword(s):  
Machine Learning ◽  
Injury Risk ◽  
Brain Injuries ◽  
Angular Acceleration ◽  
Simulated Data ◽  
Classification And Regression Tree ◽  
High Tech ◽  
Accurate Identification ◽  
Mean Values ◽  
Concussive Injuries

Mild traumatic brain injuries (mTBIs) continue to burden our warfighters. The high-tech industry has delivered wearable Micro-Electro-Mechanical System (MEMS) head-impact sensors to monitor impact forces. So far, these MEMS sensors have categorically failed to detect mTBIs and are therefore of no clinical utility for diagnosis. Our recent studies have shown that human head kinematics is anisotropic with respect to pitch–roll–yaw degrees of freedom of the head and neck. In the present project, we generated head acceleration datasets on non-injurious impacts and mTBI events based on mean values from the literature. We then augmented the simulated data with pitch–roll–yaw information followed by machine learning with a Classification and Regression Tree analysis. Our results revealed that head angular acceleration in pitch is the best predictor. More than 81.3 % of concussive injuries had head angular accelerations in pitch exceeding 3527 rad/s2. Out of 18.6% of concussive injuries with head angular accelerations in pitch under 3527 rad/s2, 75% of these cases had head angular accelerations in roll exceeding 1679 rad/s2. This study shows that artificial intelligence and machine learning should be able to provide accurate identification of subject-specific concussive thresholds in real time and in the field, thereby moving concussion diagnosis toward precision medicine.

Abstract WP331: Identifying Stroke Subtypes with High Accuracy Using Machine Learning and Icd-9 Claims Data

Stroke ◽  
2017 ◽  
Vol 48 (suppl_1) ◽  
Author(s):  
Charles Esenwa ◽  
Jorge Luna ◽  
Benjamin Kummer ◽  
Hojjat Salmasian ◽  
Hooman Kamel ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Claims Data ◽  
Detection Algorithm ◽  
Classification And Regression Tree ◽  
Published Data ◽  
Learning Models ◽  
Accurate Identification ◽  
Stroke Subtypes ◽  
Machine Learning Models

Introduction: Stroke research using widely available institutional, state-wide and national retrospective data is dependent on accurate identification of stroke subtypes using claims data. Despite the abundance of such data and the advances in clinical informatics, there is limited published data on the application of machine learning models to improve previously reported administrative stroke identification algorithms. Hypothesis: We hypothesized that machine learning models can be applied to claims data coded using the International Classification of Disease, version 9 (ICD-9), to accuracy identify patients with ischemic stroke (IS), intracerebral hemorrhage (ICH), and subarachnoid hemorrhage (SAH), and these models would outperform previously published algorithms in our patient cohort. Methods: We developed a gold standard list of 427 stroke patients continuously admitted to our institution from 1/1/2015 to 9/30/2015 using an internal stroke database and applied 75% of it to train and 25% to test two machine learning models: one using classification and regression tree (CART) and another using regularized logistic regression. There were 2,241 negative controls. We further applied a previously reported stroke detection algorithm, by Tirschwell and Longstreth, to our cohort for comparison. Results: The CART model had a κ of 0.72, 0.82, 0.59; sensitivity of 95%, 99%, 99%; and a specificity of 88%, 78%, 75%; for IS, ICH and SAH respectively. The regularized logistic regression model had a κ of 0.73, 0.80, 0.59; sensitivity of 95%, 99%, 99%, and a specificity of 89%, 78%, 75%; for IS, ICH and SAH respectively. The previously reported algorithm by Tirschwell et al, had a κ of 0.71,0.56, 0.64; sensitivity of 98%, 99%, 99%; and a specificity of 64%, 52%, 50%; for IS, ICH and SAH. Conclusion: Compared with the previously reported ICD 9 based detection algorithm, the machine learning models had a higher κ for diagnosis of IS and ICH, similar sensitivity for all subtypes, and higher specificity for all stroke subtypes in our cohort. Applying machine learning models to identify stroke subtypes from administrative data sets, can lead to highly accurate models of stroke subtype identification for health services researchers.

scholarly journals Is Acceleration a Valid Proxy for Injury Risk in Minimal Damage Traffic Crashes? A Comparative Review of Volunteer, ADL and Real-World Studies

2021 ◽  
Vol 18 (6) ◽  
pp. 2901
Author(s):  
Paul S. Nolet ◽  
Larry Nordhoff ◽  
Vicki L. Kristman ◽  
Arthur C. Croft ◽  
Maurice P. Zeegers ◽  
...  
Keyword(s):  
Real World ◽  
Injury Risk ◽  
Angular Acceleration ◽  
Traffic Crashes ◽  
Rear Impact ◽  
Comparative Review ◽  
Minimal Damage ◽  
Injury Causation ◽  
Crash Tests ◽  
Biomechanical Approach

Injury claims associated with minimal damage rear impact traffic crashes are often defended using a “biomechanical approach,” in which the occupant forces of the crash are compared to the forces of activities of daily living (ADLs), resulting in the conclusion that the risk of injury from the crash is the same as for ADLs. The purpose of the present investigation is to evaluate the scientific validity of the central operating premise of the biomechanical approach to injury causation; that occupant acceleration is a scientifically valid proxy for injury risk. Data were abstracted, pooled, and compared from three categories of published literature: (1) volunteer rear impact crash testing studies, (2) ADL studies, and (3) observational studies of real-world rear impacts. We compared the occupant accelerations of minimal or no damage (i.e., 3 to 11 kph speed change or “delta V”) rear impact crash tests to the accelerations described in 6 of the most commonly reported ADLs in the reviewed studies. As a final step, the injury risk observed in real world crashes was compared to the results of the pooled crash test and ADL analyses, controlling for delta V. The results of the analyses indicated that average peak linear and angular acceleration forces observed at the head during rear impact crash tests were typically at least several times greater than average forces observed during ADLs. In contrast, the injury risk of real-world minimal damage rear impact crashes was estimated to be at least 2000 times greater than for any ADL. The results of our analysis indicate that the principle underlying the biomechanical injury causation approach, that occupant acceleration is a proxy for injury risk, is scientifically invalid. The biomechanical approach to injury causation in minimal damage crashes invariably results in the vast underestimation of the actual risk of such crashes, and should be discontinued as it is a scientifically invalid practice.

scholarly journals Exploratory Analysis of Driving Force of Wildfires in Australia: An Application of Machine Learning within Google Earth Engine

2020 ◽  
Vol 13 (1) ◽  
pp. 10
Author(s):  
Andrea Sulova ◽  
Jamal Jokar Arsanjani
Keyword(s):  
Climate Change ◽  
Machine Learning ◽  
Random Forest ◽  
Google Earth ◽  
Summer Season ◽  
Driving Factors ◽  
Machine Learning Algorithms ◽  
Classification And Regression Tree ◽  
Training Dataset ◽  
Google Earth Engine

Recent studies have suggested that due to climate change, the number of wildfires across the globe have been increasing and continue to grow even more. The recent massive wildfires, which hit Australia during the 2019–2020 summer season, raised questions to what extent the risk of wildfires can be linked to various climate, environmental, topographical, and social factors and how to predict fire occurrences to take preventive measures. Hence, the main objective of this study was to develop an automatized and cloud-based workflow for generating a training dataset of fire events at a continental level using freely available remote sensing data with a reasonable computational expense for injecting into machine learning models. As a result, a data-driven model was set up in Google Earth Engine platform, which is publicly accessible and open for further adjustments. The training dataset was applied to different machine learning algorithms, i.e., Random Forest, Naïve Bayes, and Classification and Regression Tree. The findings show that Random Forest outperformed other algorithms and hence it was used further to explore the driving factors using variable importance analysis. The study indicates the probability of fire occurrences across Australia as well as identifies the potential driving factors of Australian wildfires for the 2019–2020 summer season. The methodical approach and achieved results and drawn conclusions can be of great importance to policymakers, environmentalists, and climate change researchers, among others.

scholarly journals Development of Machine Learning Models for Prediction of Smoking Cessation Outcome

2021 ◽  
Vol 18 (5) ◽  
pp. 2584
Author(s):  
Cheng-Chien Lai ◽  
Wei-Hsin Huang ◽  
Betty Chia-Chen Chang ◽  
Lee-Ching Hwang
Keyword(s):  
Machine Learning ◽  
Smoking Cessation ◽  
Success Rate ◽  
Prediction Models ◽  
Smoking Status ◽  
Medical Center ◽  
Machine Learning Algorithms ◽  
Classification And Regression Tree ◽  
Support Vector ◽  
Smoking Cessation Outcome

Predictors for success in smoking cessation have been studied, but a prediction model capable of providing a success rate for each patient attempting to quit smoking is still lacking. The aim of this study is to develop prediction models using machine learning algorithms to predict the outcome of smoking cessation. Data was acquired from patients underwent smoking cessation program at one medical center in Northern Taiwan. A total of 4875 enrollments fulfilled our inclusion criteria. Models with artificial neural network (ANN), support vector machine (SVM), random forest (RF), logistic regression (LoR), k-nearest neighbor (KNN), classification and regression tree (CART), and naïve Bayes (NB) were trained to predict the final smoking status of the patients in a six-month period. Sensitivity, specificity, accuracy, and area under receiver operating characteristic (ROC) curve (AUC or ROC value) were used to determine the performance of the models. We adopted the ANN model which reached a slightly better performance, with a sensitivity of 0.704, a specificity of 0.567, an accuracy of 0.640, and an ROC value of 0.660 (95% confidence interval (CI): 0.617–0.702) for prediction in smoking cessation outcome. A predictive model for smoking cessation was constructed. The model could aid in providing the predicted success rate for all smokers. It also had the potential to achieve personalized and precision medicine for treatment of smoking cessation.

scholarly journals Digital Smoke Taint Detection in Pinot Grigio Wines Using an E-Nose and Machine Learning Algorithms Following Treatment with Activated Carbon and a Cleaving Enzyme

Fermentation ◽  
2021 ◽  
Vol 7 (3) ◽  
pp. 119
Author(s):  
Vasiliki Summerson ◽  
Claudia Gonzalez Viejo ◽  
Damir D. Torrico ◽  
Alexis Pang ◽  
Sigfredo Fuentes
Keyword(s):  
Machine Learning ◽  
Activated Carbon ◽  
Low Cost ◽  
Aroma Compounds ◽  
Machine Learning Algorithms ◽  
Enzyme Treatment ◽  
Mean Values ◽  
Volatile Aroma ◽  
Carbon Treatment ◽  
Volatile Aroma Compounds

The incidence and intensity of bushfires is increasing due to climate change, resulting in a greater risk of smoke taint development in wine. In this study, smoke-tainted and non-smoke-tainted wines were subjected to treatments using activated carbon with/without the addition of a cleaving enzyme treatment to hydrolyze glycoconjugates. Chemical measurements and volatile aroma compounds were assessed for each treatment, with the two smoke taint amelioration treatments exhibiting lower mean values for volatile aroma compounds exhibiting positive ‘fruit’ aromas. Furthermore, a low-cost electronic nose (e-nose) was used to assess the wines. A machine learning model based on artificial neural networks (ANN) was developed using the e-nose outputs from the unsmoked control wine, unsmoked wine with activated carbon treatment, unsmoked wine with a cleaving enzyme plus activated carbon treatment, and smoke-tainted control wine samples as inputs to classify the wines according to the smoke taint amelioration treatment. The model displayed a high overall accuracy of 98% in classifying the e-nose readings, illustrating it may be a rapid, cost-effective tool for winemakers to assess the effectiveness of smoke taint amelioration treatment by activated carbon with/without the use of a cleaving enzyme. Furthermore, the use of a cleaving enzyme coupled with activated carbon was found to be effective in ameliorating smoke taint in wine and may help delay the resurgence of smoke aromas in wine following the aging and hydrolysis of glycoconjugates.

Software-in-the-Loop Combined Machine Learning for Dynamic Responses Analysis of Floating Offshore Wind Turbines

2021 ◽  
Author(s):  
Peng Chen ◽  
Changhong Hu ◽  
Zhiqiang Hu
Keyword(s):  
Machine Learning ◽  
Experimental Data ◽  
Wind Turbines ◽  
Dynamic Responses ◽  
Offshore Wind ◽  
Learning Technology ◽  
Mean Values ◽  
Offshore Wind Turbines ◽  
Floating Offshore Wind Turbines ◽  
Brute Force Method

Abstract Artificial intelligence (AI) brings a new solution to overcome the challenges of Floating offshore wind turbines (FOWTs) to better predict the dynamic responses with intelligent strategies. A new AI-based software-in-the-loop method, named SADA is introduced in this paper for the prediction of dynamic responses of FOWTs, which is proposed based on an in-house programme DARwind. DARwind is a coupled aero-hydro-servo-elastic in-house program for FOWTs, and a reinforcement learning method with exhaust algorithm and deep deterministic policy gradient (DDPG) are embedded in DARwind as an AI module. Firstly, the methodology is introduced with the selection of Key Disciplinary Parameters (KDPs). Secondly, Brute-force Method and DDPG algorithms are adopted to changes the KDPs’ values according to the feedback of 6DOF motions of Hywind Spar-type platform through comparing the DARwind simulation results and those of basin experimental data. Therefore, many other dynamic responses that cannot be measured in basin experiment can be predicted in good accuracy with SADA method. Finally, the case study of SADA method was conducted and the results demonstrated that the mean values of the platform’s motions can be predicted with higher accuracy. This proposed SADA method takes advantage of numerical-experimental method, basin experimental data and the machine learning technology, which brings a new and promising solution for overcoming the handicap impeding direct use of conventional basin experimental way to analyze FOWT’s dynamic responses during the design phase.

scholarly journals Exploring the Mechanism of Crashes with Autonomous Vehicles Using Machine Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Hengrui Chen ◽  
Hong Chen ◽  
Ruiyu Zhou ◽  
Zhizhen Liu ◽  
Xiaoke Sun
Keyword(s):  
Machine Learning ◽  
Autonomous Vehicles ◽  
Classification And Regression Tree ◽  
Gradient Boosting ◽  
Support Vector ◽  
Crash Severity ◽  
Apriori Algorithm ◽  
Driving Mode ◽  
Extreme Gradient Boosting ◽  
The Impact

The safety issue has become a critical obstacle that cannot be ignored in the marketization of autonomous vehicles (AVs). The objective of this study is to explore the mechanism of AV-involved crashes and analyze the impact of each feature on crash severity. We use the Apriori algorithm to explore the causal relationship between multiple factors to explore the mechanism of crashes. We use various machine learning models, including support vector machine (SVM), classification and regression tree (CART), and eXtreme Gradient Boosting (XGBoost), to analyze the crash severity. Besides, we apply the Shapley Additive Explanations (SHAP) to interpret the importance of each factor. The results indicate that XGBoost obtains the best result (recall = 75%; G-mean = 67.82%). Both XGBoost and Apriori algorithm effectively provided meaningful insights about AV-involved crash characteristics and their relationship. Among all these features, vehicle damage, weather conditions, accident location, and driving mode are the most critical features. We found that most rear-end crashes are conventional vehicles bumping into the rear of AVs. Drivers should be extremely cautious when driving in fog, snow, and insufficient light. Besides, drivers should be careful when driving near intersections, especially in the autonomous driving mode.

scholarly journals Flood Susceptibility Modeling in a Subtropical Humid Low-Relief Alluvial Plain Environment: Application of Novel Ensemble Machine Learning Approach

2021 ◽  
Vol 9 ◽  
Author(s):  
Manish Pandey ◽  
Aman Arora ◽  
Alireza Arabameri ◽  
Romulus Costache ◽  
Naveen Kumar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Ground Subsidence ◽  
Ensemble Model ◽  
Ganga Plain ◽  
Humid Climate ◽  
Area Index ◽  
Flood Susceptibility ◽  
Middle Ganga Plain

This study has developed a new ensemble model and tested another ensemble model for flood susceptibility mapping in the Middle Ganga Plain (MGP). The results of these two models have been quantitatively compared for performance analysis in zoning flood susceptible areas of low altitudinal range, humid subtropical fluvial floodplain environment of the Middle Ganga Plain (MGP). This part of the MGP, which is in the central Ganga River Basin (GRB), is experiencing worse floods in the changing climatic scenario causing an increased level of loss of life and property. The MGP experiencing monsoonal subtropical humid climate, active tectonics induced ground subsidence, increasing population, and shifting landuse/landcover trends and pattern, is the best natural laboratory to test all the susceptibility prediction genre of models to achieve the choice of best performing model with the constant number of input parameters for this type of topoclimatic environmental setting. This will help in achieving the goal of model universality, i.e., finding out the best performing susceptibility prediction model for this type of topoclimatic setting with the similar number and type of input variables. Based on the highly accurate flood inventory and using 12 flood predictors (FPs) (selected using field experience of the study area and literature survey), two machine learning (ML) ensemble models developed by bagging frequency ratio (FR) and evidential belief function (EBF) with classification and regression tree (CART), CART-FR and CART-EBF, were applied for flood susceptibility zonation mapping. Flood and non-flood points randomly generated using flood inventory have been apportioned in 70:30 ratio for training and validation of the ensembles. Based on the evaluation performance using threshold-independent evaluation statistic, area under receiver operating characteristic (AUROC) curve, 14 threshold-dependent evaluation metrices, and seed cell area index (SCAI) meant for assessing different aspects of ensembles, the study suggests that CART-EBF (AUCSR = 0.843; AUCPR = 0.819) was a better performant than CART-FR (AUCSR = 0.828; AUCPR = 0.802). The variability in performances of these novel-advanced ensembles and their comparison with results of other published models espouse the need of testing these as well as other genres of susceptibility models in other topoclimatic environments also. Results of this study are important for natural hazard managers and can be used to compute the damages through risk analysis.

scholarly journals iBitter-Fuse: A Novel Sequence-Based Bitter Peptide Predictor by Fusing Multi-View Features

2021 ◽  
Vol 22 (16) ◽  
pp. 8958
Author(s):  
Phasit Charoenkwan ◽  
Chanin Nantasenamat ◽  
Md. Mehedi Hasan ◽  
Mohammad Ali Moni ◽  
Pietro Lio’ ◽  
...  
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
De Novo ◽  
Predictive Performance ◽  
Support Vector ◽  
Sufficient Information ◽  
Self Assessment ◽  
Accurate Identification ◽  
Bitter Peptides ◽  
Accurate Performance

Accurate identification of bitter peptides is of great importance for better understanding their biochemical and biophysical properties. To date, machine learning-based methods have become effective approaches for providing a good avenue for identifying potential bitter peptides from large-scale protein datasets. Although few machine learning-based predictors have been developed for identifying the bitterness of peptides, their prediction performances could be improved. In this study, we developed a new predictor (named iBitter-Fuse) for achieving more accurate identification of bitter peptides. In the proposed iBitter-Fuse, we have integrated a variety of feature encoding schemes for providing sufficient information from different aspects, namely consisting of compositional information and physicochemical properties. To enhance the predictive performance, the customized genetic algorithm utilizing self-assessment-report (GA-SAR) was employed for identifying informative features followed by inputting optimal ones into a support vector machine (SVM)-based classifier for developing the final model (iBitter-Fuse). Benchmarking experiments based on both 10-fold cross-validation and independent tests indicated that the iBitter-Fuse was able to achieve more accurate performance as compared to state-of-the-art methods. To facilitate the high-throughput identification of bitter peptides, the iBitter-Fuse web server was established and made freely available online. It is anticipated that the iBitter-Fuse will be a useful tool for aiding the discovery and de novo design of bitter peptides

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