scholarly journals Comparison of Word Embeddings for Extraction from Medical Records

2019 ◽  
Vol 16 (22) ◽  
pp. 4360
Author(s):  
Aleksei Dudchenko ◽  
Georgy Kopanitsa
Keyword(s):  
Neural Network ◽  
Medical Records ◽  
Data Extraction ◽  
Russian Language ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Network Architectures ◽  
Medical Texts ◽  
Convolutional Network ◽  
Personalized Health

This paper is an extension of the work originally presented in the 16th International Conference on Wearable, Micro and Nano Technologies for Personalized Health. Despite using electronic medical records, free narrative text is still widely used for medical records. To make data from texts available for decision support systems, supervised machine learning algorithms might be successfully applied. In this work, we developed and compared a prototype of a medical data extraction system based on different artificial neural network architectures to process free medical texts in the Russian language. Three classifiers were applied to extract entities from snippets of text. Multi-layer perceptron (MLP) and convolutional neural network (CNN) classifiers showed similar results to all three embedding models. MLP exceeded convolutional network on pipelines that used the embedding model trained on medical records with preliminary lemmatization. Nevertheless, the highest F-score was achieved by CNN. CNN slightly exceeded MLP when the biggest word2vec model was applied (F-score 0.9763).

scholarly journals A Real-Time Artificial Intelligence-Assisted System to Predict Weaning from Ventilator Immediately after Lung Resection Surgery

2021 ◽  
Vol 18 (5) ◽  
pp. 2713
Author(s):  
Ying-Jen Chang ◽  
Kuo-Chuan Hung ◽  
Li-Kai Wang ◽  
Chia-Hung Yu ◽  
Chao-Kun Chen ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Medical Records ◽  
Lung Resection ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Bayes Classifier ◽  
Integrated Risk Assessment ◽  
Patient Comprehension ◽  
Integrated Risk ◽  
Physician Patient

Assessment of risk before lung resection surgery can provide anesthesiologists with information about whether a patient can be weaned from the ventilator immediately after surgery. However, it is difficult for anesthesiologists to perform a complete integrated risk assessment in a time-limited pre-anesthetic clinic. We retrospectively collected the electronic medical records of 709 patients who underwent lung resection between 1 January 2017 and 31 July 2019. We used the obtained data to construct an artificial intelligence (AI) prediction model with seven supervised machine learning algorithms to predict whether patients could be weaned immediately after lung resection surgery. The AI model with Naïve Bayes Classifier algorithm had the best testing result and was therefore used to develop an application to evaluate risk based on patients’ previous medical data, to assist anesthesiologists, and to predict patient outcomes in pre-anesthetic clinics. The individualization and digitalization characteristics of this AI application could improve the effectiveness of risk explanations and physician–patient communication to achieve better patient comprehension.

scholarly journals A Convolutional Neural Networks Based Method for Anthracnose Infected Walnut Tree Leaves Identification

2020 ◽  
Vol 10 (2) ◽  
pp. 469 ◽  
Author(s):  
Athanasios Anagnostis ◽  
Gavriela Asiminari ◽  
Elpiniki Papageorgiou ◽  
Dionysis Bochtis
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
State Of The Art ◽  
Initial Step ◽  
Machine Learning Algorithms ◽  
Site Classification ◽  
Network Architectures ◽  
Tree Leaves ◽  
Mode A

Anthracnose is a fungal disease that infects a large number of trees worldwide, damages intensively the canopy, and spreads with ease to neighboring trees, resulting in the potential destruction of whole crops. Even though it can be treated relatively easily with good sanitation, proper pruning and copper spraying, the main issue is the early detection for the prevention of spreading. Machine learning algorithms can offer the tools for the on-site classification of healthy and affected leaves, as an initial step towards managing such diseases. The purpose of this study was to build a robust convolutional neural network (CNN) model that is able to classify images of leaves, depending on whether or not these are infected by anthracnose, and therefore determine whether a tree is infected. A set of images were used both in grayscale and RGB mode, a fast Fourier transform was implemented for feature extraction, and a CNN architecture was selected based on its performance. Finally, the best performing method was compared with state-of-the-art convolutional neural network architectures.

scholarly journals Enhancing Health Risk Prediction with Deep Learning on Big Data and Revised Fusion Node Paradigm

2017 ◽  
Vol 2017 ◽  
pp. 1-18 ◽  
Author(s):  
Hongye Zhong ◽  
Jitian Xiao
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Information Fusion ◽  
Data Extraction ◽  
Experimental System ◽  
Health Data ◽  
Machine Learning Algorithms ◽  
Logical Inference ◽  
Fusion Model ◽  
Prediction Systems

With recent advances in health systems, the amount of health data is expanding rapidly in various formats. This data originates from many new sources including digital records, mobile devices, and wearable health devices. Big health data offers more opportunities for health data analysis and enhancement of health services via innovative approaches. The objective of this research is to develop a framework to enhance health prediction with the revised fusion node and deep learning paradigms. Fusion node is an information fusion model for constructing prediction systems. Deep learning involves the complex application of machine-learning algorithms, such as Bayesian fusions and neural network, for data extraction and logical inference. Deep learning, combined with information fusion paradigms, can be utilized to provide more comprehensive and reliable predictions from big health data. Based on the proposed framework, an experimental system is developed as an illustration for the framework implementation.

scholarly journals Machine learning model development for predicting road transport GHG emissions in Canada

2019 ◽  
Vol 53 (2) ◽  
pp. 55-72
Author(s):  
Mohd Jawad Ur Rehman Khan ◽  
Anjali Awasthi
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Multilayer Perceptron ◽  
Ghg Emissions ◽  
Predictive Performance ◽  
Road Transport ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Artificial Neural

Abstract Prediction of greenhouse gas (GHG) emissions is important to minimise their negative impact on climate change and global warming. In this article, we propose new models based on data mining and supervised machine learning algorithms (regression and classification) for predicting GHG emissions arising from passenger and freight road transport in Canada. Four models are investigated, namely, artificial neural network multilayer perceptron, multiple linear regression, multinomial logistic regression and decision tree models. From the results, it was found that artificial neural network multilayer perceptron model showed better predictive performance over other models. Ensemble technique (Bagging & Boosting) was applied on the developed multilayer perceptron model, which significantly improved the model’s predictive performance.

scholarly journals Gaze Gesture Recognition by Graph Convolutional Networks

2021 ◽  
Vol 8 ◽  
Author(s):  
Lei Shi ◽  
Cosmin Copot ◽  
Steve Vanlanduit
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Eye Tracking ◽  
Gesture Recognition ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Convolutional Network ◽  
Convolutional Networks ◽  
Research Areas ◽  
Artificial Neural Network Ann

Gaze gestures are extensively used in the interactions with agents/computers/robots. Either remote eye tracking devices or head-mounted devices (HMDs) have the advantage of hands-free during the interaction. Previous studies have demonstrated the success of applying machine learning techniques for gaze gesture recognition. More recently, graph neural networks (GNNs) have shown great potential applications in several research areas such as image classification, action recognition, and text classification. However, GNNs are less applied in eye tracking researches. In this work, we propose a graph convolutional network (GCN)–based model for gaze gesture recognition. We train and evaluate the GCN model on the HideMyGaze! dataset. The results show that the accuracy, precision, and recall of the GCN model are 97.62%, 97.18%, and 98.46%, respectively, which are higher than the other compared conventional machine learning algorithms, the artificial neural network (ANN) and the convolutional neural network (CNN).

scholarly journals Automated detection of grapes and leaves in viticulture with a YOLOv2 neural network

2018 ◽  
Author(s):  
Andreza Aparecida dos Santos ◽  
Sandra Eliza Fontes de Avila ◽  
Thiago Teixeira dos Santos
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Learning Task ◽  
Automated Detection ◽  
Supervised Machine Learning ◽  
Convolutional Network ◽  
Grape Varieties

In this work, we modeled the problem of detection of fruit and leaves in viticulture for proximal applications as a supervised machine learning task. We created and manually labeled a database of images obtained at Guaspari Winery. In total, the database consists of 11.883 images of bunch of grapes and leaves. We trained a convolutional network with YOLOv2 architecture to locate and classify bunch of grapes and leaves. Quantitative tests have shown results for detection and classification with precision of 100%, recall of 74,22% and F1-Score up to 85,2% for the class “grape”. Also, qualitative tests show that the model generalizes well when tested on photographs of other grape varieties. These results are promising and are moving towards the possibility of application in the field.

scholarly journals Applying Machine Learning to the Development of Prediction Models for Bank Deposit Subscription

2022 ◽  
Vol 9 (1) ◽  
pp. 1-12
Author(s):  
Sipu Hou ◽  
Zongzhen Cai ◽  
Jiming Wu ◽  
Hongwei Du ◽  
Peng Xie
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Prediction Models ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Data Field ◽  
Important Evidence

It is not easy for banks to sell their term-deposit products to new clients because many factors will affect customers’ purchasing decision and because banks may have difficulties to identify their target customers. To address this issue, we use different supervised machine learning algorithms to predict if a customer will subscribe a bank term deposit and then compare the performance of these prediction models. Specifically, the current paper employs these five algorithms: Naïve Bayes, Decision Tree, Random Forest, Support Vector Machine and Neural Network. This paper thus contributes to the artificial intelligence and Big Data field with an important evidence of the best performed model for predicting bank term deposit subscription.

scholarly journals Predicting rural patients� use of eHealth through supervised machine learning algorithms: A study on Portable Health Clinic in Bangladesh (Preprint)

2018 ◽  
Author(s):  
Nazmul Hossain ◽  
Fumihiko Yokota ◽  
Akira Fukuda ◽  
Ashir Ahmed
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Logistic Regression ◽  
Predictive Accuracy ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Rural Patients

BACKGROUND Predictive analytics through machine learning has been extensively using across industries including eHealth and mHealth for analyzing patient’s health data, predicting diseases, enhancing the productivity of technology or devices used for providing healthcare services and so on. However, not enough studies were conducted to predict the usage of eHealth by rural patients in developing countries. OBJECTIVE The objective of this study is to predict rural patients’ use of eHealth through supervised machine learning algorithms and propose the best-fitted model after evaluating their performances in terms of predictive accuracy. METHODS Data were collected between June and July 2016 through a field survey with structured questionnaire form 292 randomly selected rural patients in a remote North-Western sub-district of Bangladesh. Four supervised machine learning algorithms namely logistic regression, boosted decision tree, support vector machine, and artificial neural network were chosen for this experiment. A ‘correlation-based feature selection’ technique was applied to include the most relevant but not redundant features into the model. A 10-fold cross-validation technique was applied to reduce bias and over-fitting of the data. RESULTS Logistic regression outperformed other three algorithms with 85.9% predictive accuracy, 86.4% precision, 90.5% recall, 88.1% F-score, and AUC of 91.5% followed by neural network, decision tree and support vector machine with the accuracy rate of 84.2%, 82.9 %, and 80.4% respectively. CONCLUSIONS The findings of this study are expected to be helpful for eHealth practitioners in selecting appropriate areas to serve and dealing with both under-capacity and over-capacity by predicting the patients’ response in advance with a certain level of accuracy and precision.

scholarly journals ECG Signal Classification Using Deep Learning Techniques Based on the PTB-XL Dataset

Entropy ◽  
2021 ◽  
Vol 23 (9) ◽  
pp. 1121
Author(s):  
Sandra Śmigiel ◽  
Krzysztof Pałczyński ◽  
Damian Ledziński
Keyword(s):  
Neural Network ◽  
Network Architectures ◽  
Main Field ◽  
Successful Result ◽  
Convolutional Network ◽  
Ecg Signals ◽  
Learning Techniques ◽  
Disease Entities ◽  
Test Sets

The analysis and processing of ECG signals are a key approach in the diagnosis of cardiovascular diseases. The main field of work in this area is classification, which is increasingly supported by machine learning-based algorithms. In this work, a deep neural network was developed for the automatic classification of primary ECG signals. The research was carried out on the data contained in a PTB-XL database. Three neural network architectures were proposed: the first based on the convolutional network, the second on SincNet, and the third on the convolutional network, but with additional entropy-based features. The dataset was divided into training, validation, and test sets in proportions of 70%, 15%, and 15%, respectively. The studies were conducted for 2, 5, and 20 classes of disease entities. The convolutional network with entropy features obtained the best classification result. The convolutional network without entropy-based features obtained a slightly less successful result, but had the highest computational efficiency, due to the significantly lower number of neurons.

Sign in / Sign up

Export Citation Format

Share Document