scholarly journals Predicting Mortality Risk in Patients with COVID-19 Using Artificial Intelligence to Help Medical Decision-Making

2020 ◽  
Author(s):  
Mohammad Pourhomayoun ◽  
Mahdi Shakibi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Mortality Rate ◽  
Mortality Risk ◽  
Confusion Matrix ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Medical Decision ◽  
Support Vector ◽  
Depth Analysis

AbstractIn the wake of COVID-19 disease, caused by the SARS-CoV-2 virus, we designed and developed a predictive model based on Artificial Intelligence (AI) and Machine Learning algorithms to determine the health risk and predict the mortality risk of patients with COVID-19. In this study, we used documented data of 117,000 patients world-wide with laboratory-confirmed COVID-19. This study proposes an AI model to help hospitals and medical facilities decide who needs to get attention first, who has higher priority to be hospitalized, triage patients when the system is overwhelmed by overcrowding, and eliminate delays in providing the necessary care. The results demonstrate 93% overall accuracy in predicting the mortality rate. We used several machine learning algorithms including Support Vector Machine (SVM), Artificial Neural Networks, Random Forest, Decision Tree, Logistic Regression, and K-Nearest Neighbor (KNN) to predict the mortality rate in patients with COVID-19. In this study, the most alarming symptoms and features were also identified. Finally, we used a separate dataset of COVID-19 patients to evaluate our developed model accuracy, and used confusion matrix to make an in-depth analysis of our classifiers and calculate the sensitivity and specificity of our model.

scholarly journals Virtual Screening of DPP-4 Inhibitors Using QSAR-Based Artificial Intelligence and Molecular Docking of Hit Compounds to DPP-8 and DPP-9 Enzymes

2020 ◽  
Author(s):  
Oky Hermansyah ◽  
Alhadi Bustamam ◽  
Arry Yanuar
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Molecular Docking ◽  
Side Effects ◽  
Virtual Screening ◽  
Learning Algorithms ◽  
Qsar Model ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Screening Methods

Abstract Background: Dipeptidyl Peptidase-4 (DPP-4) inhibitors are becoming an essential drug in the treatment of type 2 diabetes mellitus, but some classes of these drugs have side effects such as joint pain that can become severe to pancreatitis. It is thought that these side effects appear related to their inhibition against enzymes DPP-8 and DPP-9. Objective: This study aims to find DPP-4 inhibitor hit compounds that are selective against the DPP-8 and DPP-9 enzymes. By building a virtual screening workflow using the Quantitative Structure-Activity Relationship (QSAR) method based on artificial intelligence (AI), millions of molecules from the database can be screened for the DPP-4 enzyme target with a faster time compared to other screening methods. Result: Five regression machine learning algorithms and four classification machine learning algorithms were used to build virtual screening workflows. The algorithm that qualifies for the regression QSAR model was Support Vector regression with R 2 pred 0.78, while the classification QSAR model was Random Forest with 92.21% accuracy. The virtual screening results of more than 10 million molecules from the database, obtained 2,716 hit compounds with pIC50 above 7.5. Molecular docking results of several potential hit compounds to the DPP-4, DPP-8 and DPP-9 enzymes, obtained CH0002 hit compound that has a high inhibitory potential against the DPP-4 enzyme and low inhibition of the DPP-8 and DPP-9 enzymes. Conclusion: This research was able to produce DPP-4 inhibitor hit compounds that are potential to DPP-4 and selective to DPP-8 and DPP-9 enzymes so that they can be further developed in the DPP-4 inhibitors discovery. The resulting virtual screening workflow can be applied to the discovery of hit compounds on other targets. Keywords: Artificial Intelligence; DPP-4; KNIME; Machine Learning; QSAR; Virtual Screening

scholarly journals Virtual Screening of DPP-4 Inhibitors Using QSAR-Based Artificial Intelligence and Molecular Docking of Hit Compounds to DPP-8 and DPP-9 Enzymes

2020 ◽  
Author(s):  
Oky Hermansyah ◽  
Alhadi Bustamam ◽  
Arry Yanuar
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Molecular Docking ◽  
Side Effects ◽  
Virtual Screening ◽  
Learning Algorithms ◽  
Qsar Model ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Screening Methods

Abstract Background: Dipeptidyl Peptidase-4 (DPP-4) inhibitors are becoming an essential drug in the treatment of type 2 diabetes mellitus, but some classes of these drugs have side effects such as joint pain that can become severe to pancreatitis. It is thought that these side effects appear related to their inhibition against enzymes DPP-8 and DPP-9. Objective: This study aims to find DPP-4 inhibitor hit compounds that are selective against the DPP-8 and DPP-9 enzymes. By building a virtual screening workflow using the Quantitative Structure-Activity Relationship (QSAR) method based on artificial intelligence (AI), millions of molecules from the database can be screened for the DPP-4 enzyme target with a faster time compared to other screening methods. Result: Five regression machine learning algorithms and four classification machine learning algorithms were used to build virtual screening workflows. The algorithm that qualifies for the regression QSAR model was Support Vector regression with R 2 pred 0.78, while the classification QSAR model was Random Forest with 92.21% accuracy. The virtual screening results of more than 10 million molecules from the database, obtained 2,716 hit compounds with pIC50 above 7.5. Molecular docking results of several potential hit compounds to the DPP-4, DPP-8 and DPP-9 enzymes, obtained CH0002 hit compound that has a high inhibitory potential against the DPP-4 enzyme and low inhibition of the DPP-8 and DPP-9 enzymes. Conclusion: This research was able to produce DPP-4 inhibitor hit compounds that are potential to DPP-4 and selective to DPP-8 and DPP-9 enzymes so that they can be further developed in the DPP-4 inhibitors discovery. The resulting virtual screening workflow can be applied to the discovery of hit compounds on other targets.

scholarly journals Automatic email response suggestion for support departments within a university

2018 ◽  
Author(s):  
Aditya Parameswaran ◽  
Dibyendu Mishra ◽  
Sanchit Bansal ◽  
Vinayak Agarwal ◽  
Anjali Goyal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Information Technology ◽  
Language Processing ◽  
Confusion Matrix ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Academic Affairs ◽  
Support Vector ◽  
Support Functions ◽  
Significant Difference

Background. Office of Academic Affairs (OAA), Office of Student Life (OSL) and Information Technology Helpdesk (ITD) are support functions within a university which receives hundreds of email messages on the daily basis. A large percentage of emails received by these departments are frequent and commonly used queries or request for information. Responding to every query by manually typing is a tedious and time consuming task and an automated approach for email response suggestion can save lot of time. Methods. We propose an application and solution approach for automatically generating and suggesting short email responses to support queries in a university environment. Our proposed solution can be used as one tap or one click solution for responding to various types of queries raised by faculty members and students in a university. We create a dataset for the application domain and make it publicly available. We apply a machine learning framework for classifying emails into categories such as office of academic affairs or information technology department. We apply a machine learning based classification approach for sub-category level classification also. We apply text pre-processing techniques, feature selection, support vector machine and naïve naive classifiers. We present an approach to overcome various natural language processing based challenges in the text. Results. We conduct a series of experiments and evaluate the approach using confusion matrix and accuracy based metrics. We study the discriminatory power of features and compare their relevance for the classification task. Our experimental results reveal that the proposed approach is effective. We conclude from our experiments that discriminatory features can be extracted from the text within our specific domain and automatic email response suggestion can be accurately created using machine learning algorithms and framework. We experiment with two different learning algorithms and observe that SVM outperforms Naïve Bayes. We achieve a classification accuracy of above $85\%$ for all the classes and sub-classes. Discussion. Our experiments on email response suggestion are conducted on a corpus consists of short and frequent emails by a university function but the proposed approach and techniques can be generalized to other domains also. We observe that different classifiers give different results and there is a significant difference in the predictive power of features.

scholarly journals Artificial Intelligence Algorithms for Discovering New Active Compounds Targeting TRPA1 Pain Receptors

AI ◽  
2020 ◽  
Vol 1 (2) ◽  
pp. 276-285
Author(s):  
Dragos Paul Mihai ◽  
Cosmin Trif ◽  
Gheorghe Stancov ◽  
Denise Radulescu ◽  
George Mihai Nitulescu
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Drug Discovery ◽  
Random Forests ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Oxidized Lipids ◽  
Active Compounds ◽  
Machine Learning Classification

Transient receptor potential ankyrin 1 (TRPA1) is a ligand-gated calcium channel activated by cold temperatures and by a plethora of electrophilic environmental irritants (allicin, acrolein, mustard-oil) and endogenously oxidized lipids (15-deoxy-∆12, 14-prostaglandin J2 and 5, 6-eposyeicosatrienoic acid). These oxidized lipids work as agonists, making TRPA1 a key player in inflammatory and neuropathic pain. TRPA1 antagonists acting as non-central pain blockers are a promising choice for future treatment of pain-related conditions having advantages over current therapeutic choices A large variety of in silico methods have been used in drug design to speed up the development of new active compounds such as molecular docking, quantitative structure-activity relationship models (QSAR), and machine learning classification algorithms. Artificial intelligence methods can significantly improve the drug discovery process and it is an attractive field that can bring together computer scientists and experts in drug development. In our paper, we aimed to develop three machine learning algorithms frequently used in drug discovery research: feedforward neural networks (FFNN), random forests (RF), and support vector machines (SVM), for discovering novel TRPA1 antagonists. All three machine learning methods used the same class of independent variables (multilevel neighborhoods of atoms descriptors) as prediction of activity spectra for substances (PASS) software. The model with the highest accuracy and most optimal performance metrics was the random forest algorithm, showing 99% accuracy and 0.9936 ROC AUC. Thus, our study emphasized that simpler and robust machine learning algorithms such as random forests perform better in correctly classifying TRPA1 antagonists since the dimension of the dependent variables dataset is relatively modest.

scholarly journals Determining the Tiers of a Supply Chain Using Machine Learning Algorithms

Symmetry ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 1934
Author(s):  
Kyoung Jong Park
Keyword(s):  
Machine Learning ◽  
Supply Chain ◽  
Nearest Neighbor ◽  
Confusion Matrix ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Accurate Information ◽  
K Nearest Neighbor ◽  
Multi Class Classification

Companies in the same supply chain influence each other, so sharing information enables more efficient supply chain management. An efficient supply chain must have a symmetry of information between participating entities, but in reality, the information is asymmetric, causing problems. The sustainability of the supply chain continues to be threatened because companies are reluctant to disclose information to others. If companies participating in the supply chain do not disclose accurate information, the next best way to improve the sustainability of the supply chain is to use data from the supply chain to determine each enterprise’s information. This study takes data from the supply chain and then uses machine learning algorithms to find which enterprise the data refer to when new data from unknown sources arise. The machine learning algorithms used are logistic regression, random forest, naive Bayes, decision tree, support vector machine, k-nearest neighbor, and multi-layer perceptron. Indicators for evaluating the performance of multi-class classification machine learning methods are accuracy, confusion matrix, precision, recall, and F1-score. The experimental results showed that LR and MLP accurately predicted companies (tiers), but NB, DT, RF, SVM, and K-NN did not accurately predict companies. In addition, the performance similarity of machine learning algorithms through experiments was classified into LR and MLP groups, NB and DT groups, and RF, SVM, and K-NN groups.

scholarly journals Predicting Breast Cancer: A Comparative Analysis of Machine Learning Algorithms

2020 ◽  
Vol 3 ◽  
pp. 455-459
Author(s):  
Pulung Hendro Prastyo ◽  
I Gede Yudi Paramartha ◽  
Michael S. Moses Pakpahan ◽  
Igi Ardiyanto
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Confusion Matrix ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Support Vector ◽  
Learning Approaches ◽  
K Nearest Neighbors ◽  
Common Cancer

Breast cancer is the most common cancer among women (43.3 incidents per 100.000 women), with the highest mortality (14.3 incidents per 100.000 women). Early detection is critical for survival. Using machine learning approaches, the problem can be effectively classified, predicted, and analyzed. In this study, we compared eight machine learning algorithms: Gaussian Naïve Bayes (GNB), k-Nearest Neighbors (K-NN), Support Vector Machine(SVM), Random Forest (RF), AdaBoost, Gradient Boosting (GB), XGBoost, and Multi-Layer Perceptron (MLP). The experiment is conducted using Breast Cancer Wisconsin datasets, confusion matrix, and 5-folds cross-validation. Experimental results showed that XGBoost provides the best performance. XGBoost obtained accuracy (97,19%), recall (96,75%), precision (97,28%), F1-score (96,99%), and AUC (99,61%). Our result showed that XGBoost is the most effective method to predict breast cancer in the Breast Cancer Wisconsin dataset.

scholarly journals Automatic email response suggestion for support departments within a university

2018 ◽  
Author(s):  
Aditya Parameswaran ◽  
Dibyendu Mishra ◽  
Sanchit Bansal ◽  
Vinayak Agarwal ◽  
Anjali Goyal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Information Technology ◽  
Language Processing ◽  
Confusion Matrix ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Academic Affairs ◽  
Support Vector ◽  
Support Functions ◽  
Significant Difference

Background. Office of Academic Affairs (OAA), Office of Student Life (OSL) and Information Technology Helpdesk (ITD) are support functions within a university which receives hundreds of email messages on the daily basis. A large percentage of emails received by these departments are frequent and commonly used queries or request for information. Responding to every query by manually typing is a tedious and time consuming task and an automated approach for email response suggestion can save lot of time. Methods. We propose an application and solution approach for automatically generating and suggesting short email responses to support queries in a university environment. Our proposed solution can be used as one tap or one click solution for responding to various types of queries raised by faculty members and students in a university. We create a dataset for the application domain and make it publicly available. We apply a machine learning framework for classifying emails into categories such as office of academic affairs or information technology department. We apply a machine learning based classification approach for sub-category level classification also. We apply text pre-processing techniques, feature selection, support vector machine and naïve naive classifiers. We present an approach to overcome various natural language processing based challenges in the text. Results. We conduct a series of experiments and evaluate the approach using confusion matrix and accuracy based metrics. We study the discriminatory power of features and compare their relevance for the classification task. Our experimental results reveal that the proposed approach is effective. We conclude from our experiments that discriminatory features can be extracted from the text within our specific domain and automatic email response suggestion can be accurately created using machine learning algorithms and framework. We experiment with two different learning algorithms and observe that SVM outperforms Naïve Bayes. We achieve a classification accuracy of above $85\%$ for all the classes and sub-classes. Discussion. Our experiments on email response suggestion are conducted on a corpus consists of short and frequent emails by a university function but the proposed approach and techniques can be generalized to other domains also. We observe that different classifiers give different results and there is a significant difference in the predictive power of features.

Development of innovative hygienic technologies for preservation of the workers’ reproductive health

2020 ◽  
pp. 792-792
Author(s):  
M. A. Fesenko ◽  
G. V. Golovaneva ◽  
A. V. Miskevich
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Reproductive Health ◽  
Reproductive System ◽  
Preventive Measures ◽  
Learning Algorithms ◽  
Reproductive Function ◽  
Machine Learning Algorithms ◽  
New Model

The new model «Prognosis of men’ reproductive function disorders» was developed. The machine learning algorithms (artificial intelligence) was used for this purpose, the model has high prognosis accuracy. The aim of the model applying is prioritize diagnostic and preventive measures to minimize reproductive system diseases complications and preserve workers’ health and efficiency.

Artificial Intelligence, Machine Learning and Calculation of Intraocular Lens Power

2020 ◽  
Vol 237 (12) ◽  
pp. 1430-1437
Author(s):  
Achim Langenbucher ◽  
Nóra Szentmáry ◽  
Jascha Wendelstein ◽  
Peter Hoffmann
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Regression Model ◽  
Prediction Error ◽  
Learning Algorithms ◽  
Anterior Chamber Depth ◽  
Gaussian Process Regression ◽  
Machine Learning Algorithms ◽  
Effect Sizes ◽  
Axial Position

Abstract Background and Purpose In the last decade, artificial intelligence and machine learning algorithms have been more and more established for the screening and detection of diseases and pathologies, as well as for describing interactions between measures where classical methods are too complex or fail. The purpose of this paper is to model the measured postoperative position of an intraocular lens implant after cataract surgery, based on preoperatively assessed biometric effect sizes using techniques of machine learning. Patients and Methods In this study, we enrolled 249 eyes of patients who underwent elective cataract surgery at Augenklinik Castrop-Rauxel. Eyes were measured preoperatively with the IOLMaster 700 (Carl Zeiss Meditec), as well as preoperatively and postoperatively with the Casia 2 OCT (Tomey). Based on preoperative effect sizes axial length, corneal thickness, internal anterior chamber depth, thickness of the crystalline lens, mean corneal radius and corneal diameter a selection of 17 machine learning algorithms were tested for prediction performance for calculation of internal anterior chamber depth (AQD_post) and axial position of equatorial plane of the lens in the pseudophakic eye (LEQ_post). Results The 17 machine learning algorithms (out of 4 families) varied in root mean squared/mean absolute prediction error between 0.187/0.139 mm and 0.255/0.204 mm (AQD_post) and 0.183/0.135 mm and 0.253/0.206 mm (LEQ_post), using 5-fold cross validation techniques. The Gaussian Process Regression Model using an exponential kernel showed the best performance in terms of root mean squared error for prediction of AQDpost and LEQpost. If the entire dataset is used (without splitting for training and validation data), comparison of a simple multivariate linear regression model vs. the algorithm with the best performance showed a root mean squared prediction error for AQD_post/LEQ_post with 0.188/0.187 mm vs. the best performance Gaussian Process Regression Model with 0.166/0.159 mm. Conclusion In this paper we wanted to show the principles of supervised machine learning applied to prediction of the measured physical postoperative axial position of the intraocular lenses. Based on our limited data pool and the algorithms used in our setting, the benefit of machine learning algorithms seems to be limited compared to a standard multivariate regression model.

scholarly journals Hierarchical Tactile Sensation Integration from Prosthetic Fingertips Enables Multi-Texture Surface Recognition

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4324
Author(s):  
Moaed A. Abd ◽  
Rudy Paul ◽  
Aparna Aravelli ◽  
Ou Bai ◽  
Leonel Lagos ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Sliding Contact ◽  
Tactile Sensation ◽  
Support Vector ◽  
Textured Surfaces ◽  
K Nearest Neighbor ◽  
Tactile Sensors ◽  
Time Frequency

Multifunctional flexible tactile sensors could be useful to improve the control of prosthetic hands. To that end, highly stretchable liquid metal tactile sensors (LMS) were designed, manufactured via photolithography, and incorporated into the fingertips of a prosthetic hand. Three novel contributions were made with the LMS. First, individual fingertips were used to distinguish between different speeds of sliding contact with different surfaces. Second, differences in surface textures were reliably detected during sliding contact. Third, the capacity for hierarchical tactile sensor integration was demonstrated by using four LMS signals simultaneously to distinguish between ten complex multi-textured surfaces. Four different machine learning algorithms were compared for their successful classification capabilities: K-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), and neural network (NN). The time-frequency features of the LMSs were extracted to train and test the machine learning algorithms. The NN generally performed the best at the speed and texture detection with a single finger and had a 99.2 ± 0.8% accuracy to distinguish between ten different multi-textured surfaces using four LMSs from four fingers simultaneously. The capability for hierarchical multi-finger tactile sensation integration could be useful to provide a higher level of intelligence for artificial hands.

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