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A Novel and Reliable Framework of Patient Deterioration Prediction in Intensive Care Unit Based on Long Short-Term Memory-Recurrent Neural Network

Tariq I. Alshwaheen, Yuan W. Hau, Nizar Ass'ad, Mahmoud M. AbuAlSamen
2020 IEEE Access  
In this paper, a new model was proposed based on Long Short-Term Memory-Recurrent Neural Network (LSTM-RNN) to predict deterioration of ICU patients.  ...  INDEX TERMS Genetic algorithm, long short-term memory, patient deterioration, prediction framework, and recurrent neural network.  ...  Common deep learning models are convolutional neural networks (CNNs) [45] and recurrent neural networks (RNNs) [46] .  ... 
doi:10.1109/access.2020.3047186 fatcat:vil2w5eg4fakzjwanuyv2h7ysq

SF-CNN: Deep Text Classification and Retrieval for Text Documents

R. Sarasu, K. K. Thyagharajan, N. R. Shanker
2023 Intelligent Automation and Soft Computing  
Traditional deep learning methods such as Convolutional Neural Network and Recurrent Neural Network never use semantic representation for bag-of-words.  ...  To solve the above problem, Semantic Featured Convolution Neural Networks (SF-CNN) is proposed to obtain the key relationships among the searching keywords and build a structure for matching the words  ...  Attention Based Hierarchical Recurrent Neural Network (DPA-HNN), Recurrent Neural Network -Convolutional Neural Network (RNN-CNN), Large Scope Based CNN (LSS-CNN) and Deep Convolutional Network (DCNN)  ... 
doi:10.32604/iasc.2023.027429 fatcat:r2czwj5p6jdntkr3lgkp23erma

Attention based automated radiology report generation using CNN and LSTM

Mehreen Sirshar, Muhammad Faheem Khalil Paracha, Muhammad Usman Akram, Norah Saleh Alghamdi, Syeda Zainab Yousuf Zaidi, Tatheer Fatima, Yifan Peng
2022 PLoS ONE  
A novel approach to address this issue was proposed, based on the continuous integration of convolutional neural networks and long short-term memory for detecting diseases, followed by the attention mechanism  ...  The automated generation of radiology reports provides X-rays and has tremendous potential to enhance the clinical diagnosis of diseases in patients.  ...  The proposed model applies a deep convolutional neural network (CNN) as an encoder to an RNN.  ... 
doi:10.1371/journal.pone.0262209 pmid:34990477 pmcid:PMC8736265 fatcat:swbsvyxfcna4hiftd3ezsr4dwm

Self-training improves Recurrent Neural Networks performance for Temporal Relation Extraction

Chen Lin, Timothy Miller, Dmitriy Dligach, Hadi Amiri, Steven Bethard, Guergana Savova
2018 Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis  
Neural network models are oftentimes restricted by limited labeled instances and resort to advanced architectures and features for cutting edge performance.  ...  We propose to build a recurrent neural network with multiple semantically heterogeneous embeddings within a self-training framework.  ...  Martin and the anonymous reviewers for their valuable suggestions and constructive criticism. The Titan Xp GPU used for this research was donated by the NVIDIA Corporation.  ... 
doi:10.18653/v1/w18-5619 dblp:conf/acl-louhi/LinMDABS18 fatcat:iwfr426eozg7djvy4onh74wmx4

CNN-CASS: CNN for Classification of Coronary Artery Stenosis Score in MPR Images [article]

Mariia Dobko, Bohdan Petryshak, Oles Dobosevych
2020 arXiv   pre-print
For stenosis score classification, the method shows improved performance comparing to previous works, achieving 80% accuracy on the patient level. Our code is publicly available.  ...  We overcome both limitations by applying a different automated approach based on ShuffleNet V2 network architecture and testing it on the proposed collected dataset of MPR images, which is bigger than  ...  , and Jan Kybic for valuable feedback.  ... 
arXiv:2001.08593v1 fatcat:iqyzf2lmj5c7fipvrugxfolykq

Deep CNN with Residual Connections and Range Normalization for Clinical Text Classification

Jonah. K. Kenei, Juliet. C. Moso, Elisha T. Opiyo Omullo, Robert Oboko
2019 Computer Science and Information Technology  
Deep learning methods are based on neural network architectures such as CNN (Convolutional Neural Networks) with many layers.  ...  To the best of our knowledge, this is the first time that sentence embedding and deep convolutional neural networks with residual connections and range normalization have been simultaneously applied to  ...  For the references, we referred many articles and research papers of many authors and institutions which are available in online and offline.  ... 
doi:10.13189/csit.2019.070402 fatcat:mecf5sifbfd6be5enbd7ykr6am

Multimodal Learning for Cardiovascular Risk Prediction using EHR Data [article]

Ayoub Bagheri, T. Katrien J. Groenhof, Wouter B. Veldhuis, Pim A. de Jong, Folkert W. Asselbergs, Daniel L. Oberski
2020 arXiv   pre-print
In the experiments, we compare performance of different deep neural network (DNN) architectures including convolutional neural network and long short-term memory in scenarios of using clinical variables  ...  and chest X-ray radiology reports.  ...  Acknowledgments The authors would like to thank Erik-Jan van Kesteren for his comments.  ... 
arXiv:2008.11979v1 fatcat:4qgn4jtuxncihboeuca3wtxj7q

Deep learning in generating radiology reports: A survey

Maram Mahmoud A. Monshi, Josiah Poon, Vera Chung
2020 Artificial Intelligence in Medicine  
So far, the most common approach has been to utilize publicly available datasets and develop DL models that integrate convolutional neural networks (CNN) for image analysis alongside recurrent neural networks  ...  We focus our investigation on the following critical challenges: understanding radiology text/image structures and datasets, applying DL algorithms (mainly CNN and RNN), generating radiology text, and  ...  Supervised learning mainly infers a mapping function ( ) from input to output such as multilayer perceptron (MLP), recurrent neural network (RNN), and convolutional neural network (CNN).  ... 
doi:10.1016/j.artmed.2020.101878 pmid:32425358 pmcid:PMC7227610 fatcat:ccy2g2rh2zavdjjvvjlv7poxau

Bone Age Assessment Empowered with Deep Learning: A Survey, Open Research Challenges and Future Directions

Muhammad Waqas Nadeem, Hock Guan Goh, Abid Ali, Muzammil Hussain, Muhammad Adnan Khan, Vasaki a/p Ponnusamy
2020 Diagnostics  
Both supervised and unsupervised approaches are being used to extract and learn features as well as for the multi-level representation of pattern recognition and classification.  ...  , prediction, and classification).  ...  The convolutional neural network (CNN), deep belief network (DBN), and recurrent neural network (RNN) are powerful deep-learning models for image recognition, segmentation, prediction, and classification  ... 
doi:10.3390/diagnostics10100781 pmid:33022947 fatcat:k2bqi6crzjf3zlbpchehhjkwx4

Deep Learning in Medical Imaging: General Overview

June-Goo Lee, Sanghoon Jun, Young-Won Cho, Hyunna Lee, Guk Bae Kim, Joon Beom Seo, Namkug Kim
2017 Korean Journal of Radiology  
Recurrent Neural Network Recurrent neural network (RNN) is a class of ANN specialized for temporal data including speech and handwriting, where connections between units form a cycle with a one way direction  ...  Architecture of convolutional neural networks, including input, Conv., and FC layers.  ... 
doi:10.3348/kjr.2017.18.4.570 pmid:28670152 pmcid:PMC5447633 fatcat:5ope624545ecvjvmuq2b7mnt3u

Automatic Diagnosis Coding of Radiology Reports: A Comparison of Deep Learning and Conventional Classification Methods

Sarvnaz Karimi, Xiang Dai, Hamedh Hassanzadeh, Anthony Nguyen
2017 BioNLP 2017  
We identify optimal parameters for setting up a convolutional neural network for autocoding with comparable results to that of conventional methods.  ...  We investigate the applicability of deep learning at autocoding of radiology reports using International Classification of Diseases (ICD).  ...  For text classification, two dominant methods are: (1) Convolutional Neural Networks (CNNs) from the category of feed-forward neural networks; and (2) Long Short-Term Memory (LSTM) with a recurrent neural  ... 
doi:10.18653/v1/w17-2342 dblp:conf/bionlp/KarimiDHN17 fatcat:np5r6eqrhvct3l5hmc2msrj6hm

A comparative study of machine learning methods for automated identification of radioisotopes using NaI gamma-ray spectra

S.M. Galib, P.K. Bhowmik, A.V. Avachat, H.K. Lee
2021 Nuclear Engineering and Technology  
The experimental results show that fusion of classical neural networks and modern deep learning architecture is a suitable choice for interpreting gamma spectra data where real-time and remote detection  ...  Machine learning models such as: fully connected, recurrent, convolutional, and gradient boosted decision trees, are applied under a wide variety of testing conditions, and their advantage and disadvantage  ...  Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) are some of the state-of-theart methods for modelling spatially/temporally auto-correlated data (i.e., visual imagery, audio, text,  ... 
doi:10.1016/ fatcat:4ocpzh7xxjfatgsunyysjckto4

Clinical Trial Classification of SNS24 Calls with Neural Networks

Hua Yang, Teresa Gonçalves, Paulo Quaresma, Renata Vieira, Rute Veladas, Cátia Sousa Pinto, João Oliveira, Maria Cortes Ferreira, Jéssica Morais, Ana Raquel Pereira, Nuno Fernandes, Carolina Gonçalves
2022 Future Internet  
Three different deep learning architectures, namely convolutional neural network (CNN), recurrent neural network (RNN), and transformers-based approaches are applied across a total number of 269,654 call  ...  The CNN, RNN, and transformers-based model each achieve an accuracy of 76.56%, 75.88%, and 78.15% over the test set in the preliminary experiments.  ...  The main contributions of this paper are summarized as follows: • Three deep learning (DL) based classification architectures are compared, namely CNN (convolutional neural network), RNN (recurrent neural  ... 
doi:10.3390/fi14050130 fatcat:4v37sb2uwnbcthus2enfbg36xa

Interpreting chest X-rays via CNNs that exploit hierarchical disease dependencies and uncertainty labels [article]

Hieu H. Pham, Tung T. Le, Dat Q. Tran, Dat T. Ngo, Ha Q. Nguyen
2020 arXiv   pre-print
This paper presents a supervised multi-label classification framework based on deep convolutional neural networks (CNNs) for predicting the risk of 14 common thoracic diseases.  ...  Chest radiography is one of the most common types of diagnostic radiology exams, which is critical for screening and diagnosis of many different thoracic diseases.  ...  The authors gratefully acknowledge Jeremy Irvin from the Machine Learning Group, Stanford University for helping us evaluate the proposed method on the hidden test set of CheXpert.  ... 
arXiv:1911.06475v3 fatcat:3vrne4xotjghrglcjwxwdpnkya

Automatic ICD-10 Classification of Diseases from Dutch Discharge Letters

Ayoub Bagheri, Arjan Sammani, Peter Van Der Heijden, Folkert Asselbergs, Daniel Oberski
2020 Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies  
Performance of all systems is evaluated for both the easier chapter-level ICD codes and single-label version of the task found in the literature, as well as for the lower-level ICD hierarchy and multi-label  ...  In this study, we benchmark the state-of-the-art ICD classification systems and two baseline systems on a large dataset constructed from Dutch cardiology discharge letters at UMCU hospital.  ...  On the output of the recurrent layer, a fully connected neural network with the setting in CNN was applied for classification of the ICD-10 codes.  ... 
doi:10.5220/0009372602810289 dblp:conf/biostec/BagheriSHAO20 fatcat:ananifbbqrf63fsu4eoo5f2viy
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