Prediction of Emergency Department Hospital Admission Based on Natural Language Processing and Neural Networks

2017 ◽  
Vol 56 (05) ◽  
pp. 377-389 ◽  
Author(s):  
Xingyu Zhang ◽  
Joyce Kim ◽  
Rachel E. Patzer ◽  
Stephen R. Pitts ◽  
Aaron Patzer ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Emergency Department ◽  
Logistic Regression ◽  
Natural Language Processing ◽  
Natural Language ◽  
Hospital Admission ◽  
Language Processing ◽  
Predictive Accuracy ◽  
Free Text

SummaryObjective: To describe and compare logistic regression and neural network modeling strategies to predict hospital admission or transfer following initial presentation to Emergency Department (ED) triage with and without the addition of natural language processing elements.Methods: Using data from the National Hospital Ambulatory Medical Care Survey (NHAMCS), a cross-sectional probability sample of United States EDs from 2012 and 2013 survey years, we developed several predictive models with the outcome being admission to the hospital or transfer vs. discharge home. We included patient characteristics immediately available after the patient has presented to the ED and undergone a triage process. We used this information to construct logistic regression (LR) and multilayer neural network models (MLNN) which included natural language processing (NLP) and principal component analysis from the patient’s reason for visit. Ten-fold cross validation was used to test the predictive capacity of each model and receiver operating curves (AUC) were then calculated for each model.Results: Of the 47,200 ED visits from 642 hospitals, 6,335 (13.42%) resulted in hospital admission (or transfer). A total of 48 principal components were extracted by NLP from the reason for visit fields, which explained 75% of the overall variance for hospitalization. In the model including only structured variables, the AUC was 0.824 (95% CI 0.818-0.830) for logistic regression and 0.823 (95% CI 0.817-0.829) for MLNN. Models including only free-text information generated AUC of 0.742 (95% CI 0.7310.753) for logistic regression and 0.753 (95% CI 0.742-0.764) for MLNN. When both structured variables and free text variables were included, the AUC reached 0.846 (95% CI 0.839-0.853) for logistic regression and 0.844 (95% CI 0.836-0.852) for MLNN.Conclusions: The predictive accuracy of hospital admission or transfer for patients who presented to ED triage overall was good, and was improved with the inclusion of free text data from a patient’s reason for visit regardless of modeling approach. Natural language processing and neural networks that incorporate patient-reported outcome free text may increase predictive accuracy for hospital admission.

scholarly journals Machine translation using natural language processing

2019 ◽  
Vol 277 ◽  
pp. 02004
Author(s):  
Middi Venkata Sai Rishita ◽  
Middi Appala Raju ◽  
Tanvir Ahmed Harris
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Language Processing ◽  
Deep Neural Network ◽  
English Text ◽  
French Translation ◽  
Rule Based

Machine Translation is the translation of text or speech by a computer with no human involvement. It is a popular topic in research with different methods being created, like rule-based, statistical and examplebased machine translation. Neural networks have made a leap forward to machine translation. This paper discusses the building of a deep neural network that functions as a part of end-to-end translation pipeline. The completed pipeline would accept English text as input and return the French Translation. The project has three main parts which are preprocessing, creation of models and Running the model on English Text.

scholarly journals Pat-in-the-Loop: Declarative Knowledge for Controlling Neural Networks

2020 ◽  
Vol 12 (12) ◽  
pp. 218
Author(s):  
Dario Onorati ◽  
Pierfrancesco Tommasino ◽  
Leonardo Ranaldi ◽  
Francesca Fallucchi ◽  
Fabio Massimo Zanzotto
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Cost Effective ◽  
Specific Class ◽  
Human Control ◽  
The Neural Network ◽  
Parse Trees

The dazzling success of neural networks over natural language processing systems is imposing an urgent need to control their behavior with simpler, more direct declarative rules. In this paper, we propose Pat-in-the-Loop as a model to control a specific class of syntax-oriented neural networks by adding declarative rules. In Pat-in-the-Loop, distributed tree encoders allow to exploit parse trees in neural networks, heat parse trees visualize activation of parse trees, and parse subtrees are used as declarative rules in the neural network. Hence, Pat-in-the-Loop is a model to include human control in specific natural language processing (NLP)-neural network (NN) systems that exploit syntactic information, which we will generically call Pat. A pilot study on question classification showed that declarative rules representing human knowledge, injected by Pat, can be effectively used in these neural networks to ensure correctness, relevance, and cost-effective.

scholarly journals Natural language processing for cognitive therapy: Extracting schemas from thought records

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0257832
Author(s):  
Franziska Burger ◽  
Mark A. Neerincx ◽  
Willem-Paul Brinkman
Keyword(s):  
Mental Health ◽  
Neural Networks ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Recurrent Neural Networks ◽  
Support Vector ◽  
Free Text ◽  
Cognitive Approach ◽  
Text Input

The cognitive approach to psychotherapy aims to change patients’ maladaptive schemas, that is, overly negative views on themselves, the world, or the future. To obtain awareness of these views, they record their thought processes in situations that caused pathogenic emotional responses. The schemas underlying such thought records have, thus far, been largely manually identified. Using recent advances in natural language processing, we take this one step further by automatically extracting schemas from thought records. To this end, we asked 320 healthy participants on Amazon Mechanical Turk to each complete five thought records consisting of several utterances reflecting cognitive processes. Agreement between two raters on manually scoring the utterances with respect to how much they reflect each schema was substantial (Cohen’s κ = 0.79). Natural language processing software pretrained on all English Wikipedia articles from 2014 (GLoVE embeddings) was used to represent words and utterances, which were then mapped to schemas using k-nearest neighbors algorithms, support vector machines, and recurrent neural networks. For the more frequently occurring schemas, all algorithms were able to leverage linguistic patterns. For example, the scores assigned to the Competence schema by the algorithms correlated with the manually assigned scores with Spearman correlations ranging between 0.64 and 0.76. For six of the nine schemas, a set of recurrent neural networks trained separately for each of the schemas outperformed the other algorithms. We present our results here as a benchmark solution, since we conducted this research to explore the possibility of automatically processing qualitative mental health data and did not aim to achieve optimal performance with any of the explored models. The dataset of 1600 thought records comprising 5747 utterances is published together with this article for researchers and machine learning enthusiasts to improve upon our outcomes. Based on our promising results, we see further opportunities for using free-text input and subsequent natural language processing in other common therapeutic tools, such as ecological momentary assessments, automated case conceptualizations, and, more generally, as an alternative to mental health scales.

scholarly journals Prediction of Stroke Outcome Using Natural Language Processing-Based Machine Learning of Radiology Report of Brain MRI

2020 ◽  
Vol 10 (4) ◽  
pp. 286
Author(s):  
Tak Sung Heo ◽  
Yu Seop Kim ◽  
Jeong Myeong Choi ◽  
Yeong Seok Jeong ◽  
Soo Young Seo ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Brain Mri ◽  
Free Text ◽  
Word Level ◽  
Poor Outcomes ◽  
Document Level

Brain magnetic resonance imaging (MRI) is useful for predicting the outcome of patients with acute ischemic stroke (AIS). Although deep learning (DL) using brain MRI with certain image biomarkers has shown satisfactory results in predicting poor outcomes, no study has assessed the usefulness of natural language processing (NLP)-based machine learning (ML) algorithms using brain MRI free-text reports of AIS patients. Therefore, we aimed to assess whether NLP-based ML algorithms using brain MRI text reports could predict poor outcomes in AIS patients. This study included only English text reports of brain MRIs examined during admission of AIS patients. Poor outcome was defined as a modified Rankin Scale score of 3–6, and the data were captured by trained nurses and physicians. We only included MRI text report of the first MRI scan during the admission. The text dataset was randomly divided into a training and test dataset with a 7:3 ratio. Text was vectorized to word, sentence, and document levels. In the word level approach, which did not consider the sequence of words, and the “bag-of-words” model was used to reflect the number of repetitions of text token. The “sent2vec” method was used in the sensation-level approach considering the sequence of words, and the word embedding was used in the document level approach. In addition to conventional ML algorithms, DL algorithms such as the convolutional neural network (CNN), long short-term memory, and multilayer perceptron were used to predict poor outcomes using 5-fold cross-validation and grid search techniques. The performance of each ML classifier was compared with the area under the receiver operating characteristic (AUROC) curve. Among 1840 subjects with AIS, 645 patients (35.1%) had a poor outcome 3 months after the stroke onset. Random forest was the best classifier (0.782 of AUROC) using a word-level approach. Overall, the document-level approach exhibited better performance than did the word- or sentence-level approaches. Among all the ML classifiers, the multi-CNN algorithm demonstrated the best classification performance (0.805), followed by the CNN (0.799) algorithm. When predicting future clinical outcomes using NLP-based ML of radiology free-text reports of brain MRI, DL algorithms showed superior performance over the other ML algorithms. In particular, the prediction of poor outcomes in document-level NLP DL was improved more by multi-CNN and CNN than by recurrent neural network-based algorithms. NLP-based DL algorithms can be used as an important digital marker for unstructured electronic health record data DL prediction.

scholarly journals Tackling Graphical Natural Language Processing’s Problems with Recurrent Neural Networks

2019 ◽  
Vol 54 (5) ◽  
Author(s):  
Ali Sami Sosa ◽  
Saja Majeed Mohammed ◽  
Haider Hadi Abbas ◽  
Israa Al Barazanchi
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Recurrent Neural Network ◽  
Recurrent Neural Networks ◽  
Question Answering ◽  
Structural Information ◽  
Sequential Data

Recent years have witnessed the success of artificial intelligence–based automated systems that use deep learning, especially recurrent neural network-based models, on many natural language processing problems, including machine translation and question answering. Besides, recurrent neural networks and their variations have been extensively studied with respect to several graph problems and have shown preliminary success. Despite these successes, recurrent neural network -based models continue to suffer from several major drawbacks. First, they can only consume sequential data; thus, linearization is required to serialize input graphs, resulting in the loss of important structural information. In particular, graph nodes that are originally located closely to each other can be very far away after linearization, and this introduces great challenges for recurrent neural networks to model their relation. Second, the serialization results are usually very long, so it takes a long time for recurrent neural networks to encode them. In the methodology of this study, we made the resulting graphs more densely connected so that more useful facts could be inferred, and the problem of graphical natural language processing could be easily decoded with graph recurrent neural network. As a result, the performances with single-typed edges were significantly better than the Local baseline, whereas the combination of all types of edges achieved a much better accuracy than just that of the Local using recurrent neural network. In this paper, we propose a novel graph neural network, named graph recurrent network.

scholarly journals Cyberbullying Detection: Hybrid Models Based on Machine Learning and Natural Language Processing Techniques

Electronics ◽  
2021 ◽  
Vol 10 (22) ◽  
pp. 2810
Author(s):  
Chahat Raj ◽  
Ayush Agarwal ◽  
Gnana Bharathy ◽  
Bhuva Narayan ◽  
Mukesh Prasad
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Feature Extraction ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Machine Learning Algorithms ◽  
Classification Methods ◽  
Cyberbullying Detection

The rise in web and social media interactions has resulted in the efortless proliferation of offensive language and hate speech. Such online harassment, insults, and attacks are commonly termed cyberbullying. The sheer volume of user-generated content has made it challenging to identify such illicit content. Machine learning has wide applications in text classification, and researchers are shifting towards using deep neural networks in detecting cyberbullying due to the several advantages they have over traditional machine learning algorithms. This paper proposes a novel neural network framework with parameter optimization and an algorithmic comparative study of eleven classification methods: four traditional machine learning and seven shallow neural networks on two real world cyberbullying datasets. In addition, this paper also examines the effect of feature extraction and word-embedding-techniques-based natural language processing on algorithmic performance. Key observations from this study show that bidirectional neural networks and attention models provide high classification results. Logistic Regression was observed to be the best among the traditional machine learning classifiers used. Term Frequency-Inverse Document Frequency (TF-IDF) demonstrates consistently high accuracies with traditional machine learning techniques. Global Vectors (GloVe) perform better with neural network models. Bi-GRU and Bi-LSTM worked best amongst the neural networks used. The extensive experiments performed on the two datasets establish the importance of this work by comparing eleven classification methods and seven feature extraction techniques. Our proposed shallow neural networks outperform existing state-of-the-art approaches for cyberbullying detection, with accuracy and F1-scores as high as ~95% and ~98%, respectively.

scholarly journals Compression of Convolutional Neural Network for Natural Language Processing

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Krzysztof Wróbel ◽  
Michał Karwatowski ◽  
Maciej Wielgosz ◽  
Marcin Pietroń ◽  
Kazimierz Wiatr
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Original Model ◽  
Floating Point ◽  
Memory Footprint ◽  
Classification Tasks

Convolutional Neural Networks (CNNs) were created for image classification tasks. Quickly, they were applied to other domains, including Natural Language Processing (NLP). Nowadays, the solutions based on artificial intelligence appear on mobile devices and in embedded systems, which places constraints on, among others, the memory and power consumption. Due to CNNs memory and computing requirements, to map them to hardware they need to be compressed.This paper presents the results of compression of the efficient CNNs for sentiment analysis. The main steps involve pruning and quantization. The process of mapping the compressed network to FPGA and the results of this implementation are described. The conducted simulations showed that 5-bit width is enough to ensure no drop in accuracy when compared to the floating point version of the network. Additionally, the memory footprint was significantly reduced (between 85% and 93% comparing to the original model).

scholarly journals Physics Inspired Deep Neural Networks for Top Quark Reconstruction

2020 ◽  
Vol 245 ◽  
pp. 06029
Author(s):  
Kevin Greif ◽  
Kevin Lannon
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Computer Vision ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Top Quark ◽  
Deep Neural Networks ◽  
Great Success ◽  
Fully Connected

Deep neural networks (DNNs) have been applied to the fields of computer vision and natural language processing with great success in recent years. The success of these applications has hinged on the development of specialized DNN architectures that take advantage of specific characteristics of the problem to be solved, namely convolutional neural networks for computer vision and recurrent neural networks for natural language processing. This research explores whether a neural network architecture specific to the task of identifying t → Wb decays in particle collision data yields better performance than a generic, fully-connected DNN. Although applied here to resolved top quark decays, this approach is inspired by an DNN technique for tagging boosted top quarks, which consists of defining custom neural network layers known as the combination and Lorentz layers. These layers encode knowledge of relativistic kinematics applied to combinations of particles, and the output of these specialized layers can then be fed into a fully connected neural network to learn tasks such as classification. This research compares the performance of these physics inspired networks to that of a generic, fully-connected DNN, to see if there is any advantage in terms of classification performance, size of the network, or ease of training.

scholarly journals Development of a Custom Spell-Checker for Emergency Department Data

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Sophie Rand ◽  
Ramona Lall ◽  
Ramona Lall
Keyword(s):  
Emergency Department ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Syndromic Surveillance ◽  
Word List ◽  
Chief Complaint ◽  
Free Text ◽  
Spell Checker ◽  
Future Work

ObjectiveTo share progress on a custom spell-checker for emergency department chief complaint free-text data and demonstrate a spell-checker validation Shiny application.IntroductionEmergency department (ED) syndromic surveillance relies on a chief complaint, which is often a free-text field, and may contain misspelled words, syntactic errors, and healthcare-specific and/or facility-specific abbreviations. Cleaning of the chief complaint field may improve syndrome capture sensitivity and reduce misclassification of syndromes. We are building a spell-checker, customized with language found in ED corpora, as our first step in cleaning our chief complaint field. This exercise would elucidate the value of pre-processing text and would lend itself to future work using natural language processing (NLP) techniques, such as topic modeling. Such a tool could be extensible to other datasets that contain free-text fields, including electronic reportable disease lab and case reporting.MethodsChief complaints may contain words that are incorrect if they are misspelled (e.g.,“patient has herpertension”), or, if the word yields a syntactically incorrect phrase (e.g., the word “huts” in the phrase: “my toe huts”).We are developing a spell-checker tool for chief complaint text using the R and Python programming languages. The first stage in the development of the spell-checker is the identifying and handling of misspellings; future work will address syntactic errors. Known abbreviations are identified using regular expressions, and unknown abbreviations are addressed by the spell-checker. The spell checker performs 4 steps on chief complaint data: identification of misspellings, generation of a substitute candidate word list, word sense disambiguation to identify replacement word, and replacement of the misspelled word, based on methods found in the literature.[1] As the spell-checker requires a dictionary of correctly spelled, healthcare-specific terms including all terms that would appear in an ED corpus, we used vocabularies from the Unified Medical Language System, ED-specific terminology, and domain expert user input. Dictionary construction, misspelling identification algorithms, and word list generation algorithms are in the development stage.Simultaneously, we are building an R Shiny interactive web application for syndromic surveillance analysts to manually correct a subset of misspelled words, which we will use to validate and evaluate the performance of the spell-checker tool.[1] Tolentino HD, Matters MD, Walop W, et al. A UMLS-based spell checker for natural language processing in vaccine safety. BMC Medical Informatics and Decision Making. 2007;7(1). doi:10.1186/1472-6947-7-3.ResultsProject still in development phase.ConclusionsThe audience will learn about important considerations for developing a spell-checker, including those for data structure of a dictionary and algorithms for identification of misplaced words and identification of candidate replacement words. We will demonstrate our word list generation algorithm and the Shiny application which uses these words for spell-checker validation. We will share relevant code; after our presentation, audience members should able to apply code and lessons to their own projects and/or to collaborate with the NYC Department of Health and Mental Hygiene.

scholarly journals Convolution Neural Networks for Blind Image Steganalysis: A Comprehensive Study

2019 ◽  
Vol 11 (2) ◽  
pp. 53-64
Author(s):  
Hanaa Mohsin Ahmed ◽  
Halah Hasan Mahmoud
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
High Accuracy ◽  
Image Steganalysis ◽  
Scene Labeling ◽  
Blind Image ◽  
Comprehensive Study

Recently, Convolution Neural Network is widely applied in Image Classification, Object Detection, Scene labeling, Speech, Natural Language Processing and other fields. In this comprehensive study a variety of scenarios and efforts are surveyed since 2014 at yet, in order to provide a guide to further improve future researchers what CNN-based blind image steganalysis are presented its architecture, performance and limitations. Long-standing and important problem in image steganalysis difficulties mainly lie in how to give high accuracy and low payload in stego or cover images for improving performance of the network.  

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