scholarly journals A Pilot Study for the Prediction of Liver Function Related Scores Using Breath Biomarkers and Machine Learning

2021 ◽  
Author(s):  
Rakesh Kumar Patnaik ◽  
Yu-Chen Lin ◽  
Ashish Agarwal ◽  
Ming Chih Ho ◽  
J. Andrew Yeh
Keyword(s):  
Machine Learning ◽  
Liver Disease ◽  
Mean Absolute Error ◽  
Clinical Score ◽  
Absolute Error ◽  
Meld Score ◽  
Breath Sample ◽  
Regression Methods ◽  
Apri Score ◽  
Ctp Score

Abstract Liver function test is the first step to diagnose various liver diseases by measuring certain proteins and liver enzymes from the blood sample. After getting the required data of the clinical parameters from the blood test it is possible to calculate Child-Pugh (CTP), AST to PLT ratio (APRI) and Model for end-stage liver disease (MELD) clinical scores that help the doctors about the severity of the disease progression. Volatile organic compounds (VOCs) found in-breath and monitoring their concentration may be a prevailing method for disease diagnosis. In this work, Isoprene, Limonene, and Dimethyl sulphide (DMS) are considered as a potential breath biomarker related to liver disease. A dataset is designed, that includes the biomarkers concentration analysed from the breath sample before and after study subjects performed an exercise. Four regression methods are performed to predict the clinical scores using breath biomarkers data as features set by the machine learning techniques. Regression methods on the dataset for prediction are evaluated by mean absolute error and root mean square error. A significant difference observed for isoprene concentration (p<0.01) and for DMS concentration (p<0.0001) between liver patients and healthy subjects breath sample. Ensemble regression methods are found best suited for the dataset. The mean absolute error for CTP score is 0.07, for APRI score 0.1 and for MELD score 0.7. The R-square value between actual clinical score and predicted clinical score is found to be 0.78, 0.95, and 0.85 for CTP score, APRI score, and MELD score, respectively. These results suggest that breath biomarkers hold a promising approach for non-invasive test and mass screening related to liver disease.

scholarly journals Detection and Severity Evaluation of Combined Rail Defects Using Deep Learning

Vibration ◽  
2021 ◽  
Vol 4 (2) ◽  
pp. 341-356
Author(s):  
Jessada Sresakoolchai ◽  
Sakdirat Kaewunruen
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Machine Learning Techniques ◽  
Rolling Stock ◽  
Raw Data ◽  
Learning Techniques ◽  
Combined Defects

Various techniques have been developed to detect railway defects. One of the popular techniques is machine learning. This unprecedented study applies deep learning, which is a branch of machine learning techniques, to detect and evaluate the severity of rail combined defects. The combined defects in the study are settlement and dipped joint. Features used to detect and evaluate the severity of combined defects are axle box accelerations simulated using a verified rolling stock dynamic behavior simulation called D-Track. A total of 1650 simulations are run to generate numerical data. Deep learning techniques used in the study are deep neural network (DNN), convolutional neural network (CNN), and recurrent neural network (RNN). Simulated data are used in two ways: simplified data and raw data. Simplified data are used to develop the DNN model, while raw data are used to develop the CNN and RNN model. For simplified data, features are extracted from raw data, which are the weight of rolling stock, the speed of rolling stock, and three peak and bottom accelerations from two wheels of rolling stock. In total, there are 14 features used as simplified data for developing the DNN model. For raw data, time-domain accelerations are used directly to develop the CNN and RNN models without processing and data extraction. Hyperparameter tuning is performed to ensure that the performance of each model is optimized. Grid search is used for performing hyperparameter tuning. To detect the combined defects, the study proposes two approaches. The first approach uses one model to detect settlement and dipped joint, and the second approach uses two models to detect settlement and dipped joint separately. The results show that the CNN models of both approaches provide the same accuracy of 99%, so one model is good enough to detect settlement and dipped joint. To evaluate the severity of the combined defects, the study applies classification and regression concepts. Classification is used to evaluate the severity by categorizing defects into light, medium, and severe classes, and regression is used to estimate the size of defects. From the study, the CNN model is suitable for evaluating dipped joint severity with an accuracy of 84% and mean absolute error (MAE) of 1.25 mm, and the RNN model is suitable for evaluating settlement severity with an accuracy of 99% and mean absolute error (MAE) of 1.58 mm.

Machine learning and Grad-Cam based vascular aging assessment using photoplethysmogram (Preprint)

2021 ◽  
Author(s):  
Hangsik Shin
Keyword(s):  
Machine Learning ◽  
Correlation Coefficient ◽  
Age Estimation ◽  
Mean Squared Error ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Vascular Aging ◽  
Squared Error ◽  
Vascular Age

BACKGROUND Arterial stiffness due to vascular aging is a major indicator for evaluating cardiovascular risk. OBJECTIVE In this study, we propose a method of estimating age by applying machine learning to photoplethysmogram for non-invasive vascular age assessment. METHODS The machine learning-based age estimation model that consists of three convolutional layers and two-layer fully connected layers, was developed using segmented photoplethysmogram by pulse from a total of 752 adults aged 19–87 years. The performance of the developed model was quantitatively evaluated using mean absolute error, root-mean-squared-error, Pearson’s correlation coefficient, coefficient of determination. The Grad-Cam was used to explain the contribution of photoplethysmogram waveform characteristic in vascular age estimation. RESULTS Mean absolute error of 8.03, root mean squared error of 9.96, 0.62 of correlation coefficient, and 0.38 of coefficient of determination were shown through 10-fold cross validation. Grad-Cam, used to determine the weight that the input signal contributes to the result, confirmed that the contribution to the age estimation of the photoplethysmogram segment was high around the systolic peak. CONCLUSIONS The machine learning-based vascular aging analysis method using the PPG waveform showed comparable or superior performance compared to previous studies without complex feature detection in evaluating vascular aging. CLINICALTRIAL 2015-0104

scholarly journals Measurement Method for Evaluating the Lockdown Policies during the COVID-19 Pandemic

2020 ◽  
Vol 17 (15) ◽  
pp. 5574 ◽  
Author(s):  
Mohammed Al Zobbi ◽  
Belal Alsinglawi ◽  
Omar Mubin ◽  
Fady Alnajjar
Keyword(s):  
Machine Learning ◽  
Infection Rate ◽  
Global Economy ◽  
Data Analytics ◽  
Measurement Method ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Decision Makers ◽  
Machine Learning Classification ◽  
Data Analyses

Coronavirus Disease 2019 (COVID-19) has affected day to day life and slowed down the global economy. Most countries are enforcing strict quarantine to control the havoc of this highly contagious disease. Since the outbreak of COVID-19, many data analyses have been done to provide close support to decision-makers. We propose a method comprising data analytics and machine learning classification for evaluating the effectiveness of lockdown regulations. Lockdown regulations should be reviewed on a regular basis by governments, to enable reasonable control over the outbreak. The model aims to measure the efficiency of lockdown procedures for various countries. The model shows a direct correlation between lockdown procedures and the infection rate. Lockdown efficiency is measured by finding a correlation coefficient between lockdown attributes and the infection rate. The lockdown attributes include retail and recreation, grocery and pharmacy, parks, transit stations, workplaces, residential, and schools. Our results show that combining all the independent attributes in our study resulted in a higher correlation (0.68) to the dependent value Interquartile 3 (Q3). Mean Absolute Error (MAE) was found to be the least value when combining all attributes.

scholarly journals Machine learning and geostatistical approaches for estimating aboveground biomass in Chinese subtropical forests

2020 ◽  
Author(s):  
huiyi su ◽  
Wenjuan Shen ◽  
Jingrui Wang ◽  
Arshad Ali ◽  
Mingshi Li
Keyword(s):  
Machine Learning ◽  
Forest Management ◽  
Random Forest ◽  
Aboveground Biomass ◽  
Ordinary Kriging ◽  
Temporal Dynamics ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Subtropical Forests ◽  
Management Actions

Abstract Background: Aboveground biomass (AGB) is a fundamental indicator of forest ecosystem productivity and health and hence plays an essential role in evaluating forest carbon reserves and supporting the development of targeted forest management plans. Methods: Here, we proposed a random forest/co-kriging framework that integrates the strengths of machine learning and geostatistical approaches to improve the mapping accuracies of AGB in northern Guangdong province of China. We used Landsat time-series observations, Advanced Land Observing Satellite (ALOS) Phased Array L-band Synthetic Aperture Radar (PALSAR) data, and National Forest Inventory (NFI) plot measurements, to generate the forest AGB maps at three time points (1992, 2002, and 2010) showing the spatio-temporal dynamics of AGB in the subtropical forests in Guangdong, China. Results: The proposed model provided excellent performance for mapping AGB using spectral, textural, and topographical variables, and the radar backscatter coefficients. The root mean square error of the plot-level AGB validation was between 15.62 and 53.78 (t/ha), the mean absolute error ranged from 6.54 to 32.32 t/ha, and the relative improvement over the random forest algorithm was between 3.8% and 17.7%. The highest coefficient of determination (0.81) and the lowest mean absolute error (6.54 t/ha) were observed in the 1992 AGB map. The spectral saturation effect was minimized by adding the PALSAR data to the modeling variable set in 2010. By adding elevation as a covariable, the co-kriging outperformed the ordinary kriging method for the prediction of the AGB residuals, because co-kriging resulted in better interpolation results in the valleys and plains of the study area. Conclusions: Validation of the three AGB maps with an independent dataset indicated that the random forest/co-kriging performed best for AGB prediction, followed by random forest coupled with ordinary kriging (random forest/ordinary kriging), and the random forest model. The proposed random forest/co-kriging framework provides an accurate and reliable method for AGB mapping in subtropical forest regions with complex topography. The resulting AGB maps are suitable for the targeted development of forest management actions to promote carbon sequestration and sustainable forest management in the context of climate change.

scholarly journals Looking Beyond Adsorption Energies to Understand Interactions at Surface Using Machine Learning

2021 ◽  
Author(s):  
Sheena Agarwal ◽  
Kavita Joshi
Keyword(s):  
Machine Learning ◽  
Small Molecules ◽  
Density Functional ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Face Centered Cubic ◽  
Functional Theory ◽  
Full Picture ◽  
Adsorption Energies ◽  
Face Centered

Abstract<br>Identifying factors that influence interactions at the surface is still an active area of research. In this study, we present the importance of analyzing bondlength activation, while interpreting Density Functional Theory (DFT) results, as yet another crucial indicator for catalytic activity. We studied the<br>adsorption of small molecules, such as O 2 , N 2 , CO, and CO 2 , on seven face-centered cubic (fcc) transition metal surfaces (M = Ag, Au, Cu, Ir, Rh, Pt, and Pd) and their commonly studied facets (100, 110, and 111). Through our DFT investigations, we highlight the absence of linear correlation between adsorption energies (E ads ) and bondlength activation (BL act ). Our study indicates the importance of evaluating both to develop a better understanding of adsorption at surfaces. We also developed a Machine Learning (ML) model trained on simple periodic table properties to predict both, E ads and BL act . Our ML model gives an accuracy of Mean Absolute Error (MAE) ∼ 0.2 eV for E ads predictions and 0.02 Å for BL act predictions. The systematic study of the ML features<br>that affect E ads and BL act further reinforces the importance of looking beyond adsorption energies to get a full picture of surface interactions with DFT.<br>

scholarly journals Chemerin Is a Valuable Biomarker in Patients with HCV Infection and Correlates with Liver Injury

Diagnostics ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 974
Author(s):  
Georg Peschel ◽  
Jonathan Grimm ◽  
Karsten Gülow ◽  
Martina Müller ◽  
Christa Buechler ◽  
...  
Keyword(s):  
Liver Disease ◽  
Liver Fibrosis ◽  
Leukocyte Count ◽  
Hepatic Function ◽  
Meld Score ◽  
Hcv Infection ◽  
Alcoholic Fatty Liver ◽  
Advanced Liver Fibrosis ◽  
Apri Score ◽  
Progressive Liver Disease

Hepatitis C virus (HCV)-induced inflammation contributes to progressive liver disease. The chemoattractant protein chemerin is associated with systemic inflammation. We hypothesized that chemerin is a biomarker that predicts the severity of liver disease in HCV patients. Furthermore, we investigated whether serum chemerin levels change during the course of HCV treatment using direct-acting antivirals (DAAs). Therefore, we measured serum concentration of chemerin in a cohort of 82 HCV-infected patients undergoing DAA treatment. Serum chemerin was positively associated with leukocyte count and negatively with markers of hepatic function and the model of end-stage liver disease (MELD) score. Low circulating chemerin levels significantly correlated with advanced liver fibrosis and cirrhosis as measured by the fibrosis-4 (FIB-4) score, the aminotransferase/platelet (AST/PLT) ratio index (APRI) score and the non-alcoholic fatty liver disease (NAFLD) score. Chemerin did not correlate with viral load or viral genotype. Treatment with DAAs did not improve MELD score and leukocyte count within the observation period, up to three months after the end of DAA treatment. Accordingly, chemerin levels remained unchanged during the treatment period. We conclude that low circulating chemerin is a noninvasive biomarker for hepatic dysfunction and advanced liver fibrosis and cirrhosis in HCV infection.

Supervised Machine Learning Algorithms for Evaluation of Solid Lipid Nanoparticles and Particle Size

2019 ◽  
Vol 21 (9) ◽  
pp. 693-699 ◽  
Author(s):  
A. Alper Öztürk ◽  
A. Bilge Gündüz ◽  
Ozan Ozisik
Keyword(s):  
Machine Learning ◽  
Particle Size ◽  
High Speed ◽  
Mean Absolute Error ◽  
Mixing Time ◽  
Learning Algorithms ◽  
Absolute Error ◽  
Machine Learning Algorithms ◽  
Learning Methods ◽  
Machine Learning Methods

Aims and Objectives: Solid Lipid Nanoparticles (SLNs) are pharmaceutical delivery systems that have advantages such as controlled drug release, long-term stability etc. Particle Size (PS) is one of the important criteria of SLNs. These factors affect drug release rate, bio-distribution etc. In this study, the formulation of SLNs using high-speed homogenization technique has been evaluated. The main emphasis of the work is to study whether the effect of mixing time and formulation ingredients on PS can be modeled. For this purpose, different machine learning algorithms have been applied and evaluated using the mean absolute error metric. Materials and Methods: SLNs were prepared by high-speed homogenizaton. PS, size distribution and zeta potential measurements were performed on freshly prepared samples. In order to model the formulation of the particles in terms of mixing time and formulation ingredients and evaluate the predictability of PS depending on these parameters, different machine learning algorithms were applied on the prepared dataset and the performances of the algorithms were also evaluated. Results: PS of SLNs obtained was in the range of 263-498nm. The results present that PS of SLNs can be best estimated by decision tree based methods, among which Random Forest has the least mean absolute error value with 0.028. As a result, the estimation of machine learning algorithms demonstrates that particle size can be estimated by both decision rule-based machine learning methods and function fitting machine learning methods. Conclusion: Our findings present that machine learning methods can be highly useful for determining formulation parameters for further research.

The Risk Of Surgery In Patients With Liver Disease 2

2018 ◽  
Author(s):  
Adrian Reuben
Keyword(s):  
Liver Disease ◽  
Fatty Liver ◽  
Venous Pressure ◽  
Portal Pressure ◽  
Disease Patient ◽  
Case Series ◽  
Meld Score ◽  
End Stage Liver Disease ◽  
Ctp Score ◽  
End Stage

The results of retrospective largescale registry and cohort studies and small case series, substantiate the common perception that operating on a liver disease patient is risky. The preexisting physiological derangements of liver disease may be exacerbated by the trauma of surgery and its complications, which contributes strongly to the aforementioned surgical risks, especially but not exclusively in cirrhotics. The risks of operating on patients with non-cirrhotic liver disease are reviewed with particular emphasis on the poor outcomes in acute hepatitis—especially alcoholic hepatitis—severe fatty liver disease, and obstructive jaundice. The outcomes of a broad spectrum of surgical procedures in cirrhotics (abdominal, cardiothoracic, orthopedic, vascular, etc.) are reviewed, with particular reference to common predictors of survival and morbidity, such as the Child-Turcotte-Pugh (CTP) score/class and the model for end-stage liver disease (MELD) score. The concept is proposed that the height of portal pressure may be a predictive factor of surgical outcome, which derives from experience with hepatic resection and suggests that measurement of hepatic venous pressures may be worthwhile in selected cases. New, non-invasive estimates of liver function are presented. A simple practical pre-operative decision tree is provided. This review contains 5 figures, 3 tables and 91 references Keywords: cirrhosis, fatty liver, hepatic venous pressure gradient, hepatitis, model for end-stage liver disease, operative mortality, portal hypertension, Child-Turcotte-Pugh  

Application of Machine Learning Based Surrogate Model for Prediction of Sectional Temperature of Radially Cooled Gas Turbine Blades

2021 ◽  
Author(s):  
Rishabh Shrivastava ◽  
Nisha Tamar ◽  
Amit Grover ◽  
Debdulal Das
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Mean Absolute Error ◽  
Turbine Blades ◽  
Absolute Error ◽  
Inlet Temperature ◽  
Turbine Inlet Temperature ◽  
Gas Turbine Blades ◽  
Thermal Calculations ◽  
Cooling Air

Abstract Accurate thermal prediction of gas turbine blades is essential to ensure successful operation throughout the design life. Large Gas turbines operate in different conditions based on customer requirements, due to which turbine blades are subjected to variations in thermal loading conditions. Simulating this behavior using conventional finite element modeling involves detailed and time-consuming analyses for calculation of blade temperature, which can be further utilized to assess cyclic and creep life. This paper deals with developing and utilizing machine learning based surrogate models to predict the sectional temperature (output) of a radially cooled blade. The surrogate models are developed to predict the output using turbine inlet temperature, hot gas mass flow, cooling air temperature and cooling air mass flow as input to the machine learning (ML) model. All thermal parameters for ML model have been obtained from CFD based 3D thermal calculations. A comparative study is presented between linear regression, decision tree, random forest, and gradient boost ML models, to select the model with the least mean absolute error. Additionally, hyperparameter optimization is performed using grid search to minimize the error. The results show that the linear regression-based model outputs the least mean absolute error of 6.5°C and the highest dependence of the output is on the turbine inlet temperature, followed by the cooling air temperature. The findings show a good agreement between the predicted output of the surrogate model and multi-dimensional physics based thermal calculations, while offering a considerate reduction in analysis time.

Toward rainfall prediction by machine learning in Perfume River Basin, Thua Thien Hue Province, Vietnam

2021 ◽  
Vol 13 (1) ◽  
pp. 963-976
Author(s):  
Nguyen Hong Giang ◽  
YuRen Wang ◽  
Tran Dinh Hieu ◽  
Quan Thanh Tho ◽  
Le Anh Phuong ◽  
...  
Keyword(s):  
Machine Learning ◽  
River Basin ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Machine Learning Method ◽  
Multi Layer Perceptron ◽  
Rainfall Forecasting ◽  
Rainfall Prediction ◽  
Future Data ◽  
Mlp Model

Abstract This study examines rainfall forecasting for the Perfume (Huong) River basin using the machine learning method. To be precise, statistical measurement indicators are deployed to evaluate the reliability of the actual accumulated data. At the same time, this study applied and compared two popular models of multi-layer perceptron and the k-nearest neighbors (k-NN) with different configurations. The calculated rainfall data are obtained from the Hue, Aluoi, and Namdong hydrological stations, where the rainfall demonstrated a giant impact on the downstream from 1980 to 2018. This study result shows that both models, once fine-tuned properly, enjoyed the performance with standard metrics of R_squared, mean absolute error, Nash–Sutcliffe efficiency, and root-mean-square error. In particular, once Adam stochastic is deployed, the implementation of the MLP model is significantly improving. The promising forecast results encourage us to consider applying these models with future data to help natural disaster non-stop mitigation in the Perfume River basin.

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