scholarly journals Automatic Diagnosis of Spinal Disorders on Radiographic Images: Leveraging Existing Unstructured Datasets With Natural Language Processing

2021 ◽  
pp. 219256822110269
Author(s):  
Fabio Galbusera ◽  
Andrea Cina ◽  
Tito Bassani ◽  
Matteo Panico ◽  
Luca Maria Sconfienza
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Lumbar Spine ◽  
Natural Language ◽  
Language Processing ◽  
Large Set ◽  
Learning Models ◽  
Radiological Findings ◽  
Radiographic Images

Study Design: Retrospective study. Objectives: Huge amounts of images and medical reports are being generated in radiology departments. While these datasets can potentially be employed to train artificial intelligence tools to detect findings on radiological images, the unstructured nature of the reports limits the accessibility of information. In this study, we tested if natural language processing (NLP) can be useful to generate training data for deep learning models analyzing planar radiographs of the lumbar spine. Methods: NLP classifiers based on the Bidirectional Encoder Representations from Transformers (BERT) model able to extract structured information from radiological reports were developed and used to generate annotations for a large set of radiographic images of the lumbar spine (N = 10 287). Deep learning (ResNet-18) models aimed at detecting radiological findings directly from the images were then trained and tested on a set of 204 human-annotated images. Results: The NLP models had accuracies between 0.88 and 0.98 and specificities between 0.84 and 0.99; 7 out of 12 radiological findings had sensitivity >0.90. The ResNet-18 models showed performances dependent on the specific radiological findings with sensitivities and specificities between 0.53 and 0.93. Conclusions: NLP generates valuable data to train deep learning models able to detect radiological findings in spine images. Despite the noisy nature of reports and NLP predictions, this approach effectively mitigates the difficulties associated with the manual annotation of large quantities of data and opens the way to the era of big data for artificial intelligence in musculoskeletal radiology.

scholarly journals Wave2Vec: Vectorizing Electroencephalography Bio-Signal for Prediction of Brain Disease

2018 ◽  
Vol 15 (8) ◽  
pp. 1750 ◽  
Author(s):  
Seonho Kim ◽  
Jungjoon Kim ◽  
Hong-Woo Chun
Keyword(s):  
Artificial Intelligence ◽  
Time Series ◽  
Feature Selection ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Data Analysis ◽  
Natural Language ◽  
Real Number ◽  
Real Time ◽  
Language Processing

Interest in research involving health-medical information analysis based on artificial intelligence, especially for deep learning techniques, has recently been increasing. Most of the research in this field has been focused on searching for new knowledge for predicting and diagnosing disease by revealing the relation between disease and various information features of data. These features are extracted by analyzing various clinical pathology data, such as EHR (electronic health records), and academic literature using the techniques of data analysis, natural language processing, etc. However, still needed are more research and interest in applying the latest advanced artificial intelligence-based data analysis technique to bio-signal data, which are continuous physiological records, such as EEG (electroencephalography) and ECG (electrocardiogram). Unlike the other types of data, applying deep learning to bio-signal data, which is in the form of time series of real numbers, has many issues that need to be resolved in preprocessing, learning, and analysis. Such issues include leaving feature selection, learning parts that are black boxes, difficulties in recognizing and identifying effective features, high computational complexities, etc. In this paper, to solve these issues, we provide an encoding-based Wave2vec time series classifier model, which combines signal-processing and deep learning-based natural language processing techniques. To demonstrate its advantages, we provide the results of three experiments conducted with EEG data of the University of California Irvine, which are a real-world benchmark bio-signal dataset. After converting the bio-signals (in the form of waves), which are a real number time series, into a sequence of symbols or a sequence of wavelet patterns that are converted into symbols, through encoding, the proposed model vectorizes the symbols by learning the sequence using deep learning-based natural language processing. The models of each class can be constructed through learning from the vectorized wavelet patterns and training data. The implemented models can be used for prediction and diagnosis of diseases by classifying the new data. The proposed method enhanced data readability and intuition of feature selection and learning processes by converting the time series of real number data into sequences of symbols. In addition, it facilitates intuitive and easy recognition, and identification of influential patterns. Furthermore, real-time large-capacity data analysis is facilitated, which is essential in the development of real-time analysis diagnosis systems, by drastically reducing the complexity of calculation without deterioration of analysis performance by data simplification through the encoding process.

Leveraging Natural Language Processing Applications Using Machine Learning

2020 ◽  
pp. 338-360
Author(s):  
Janjanam Prabhudas ◽  
C. H. Pradeep Reddy
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Text Summarization ◽  
Feature Representation ◽  
Learning Models ◽  
Primary Focus ◽  
And Performance

The enormous increase of information along with the computational abilities of machines created innovative applications in natural language processing by invoking machine learning models. This chapter will project the trends of natural language processing by employing machine learning and its models in the context of text summarization. This chapter is organized to make the researcher understand technical perspectives regarding feature representation and their models to consider before applying on language-oriented tasks. Further, the present chapter revises the details of primary models of deep learning, its applications, and performance in the context of language processing. The primary focus of this chapter is to illustrate the technical research findings and gaps of text summarization based on deep learning along with state-of-the-art deep learning models for TS.

Detection of Pneumothorax with Deep Learning Models: Learning From Radiologist Labels vs Natural Language Processing Model Generated Labels

2021 ◽  
Author(s):  
James Thomas Patrick Decourcy Hallinan ◽  
Mengling Feng ◽  
Dianwen Ng ◽  
Soon Yiew Sia ◽  
Vincent Tze Yang Tiong ◽  
...  
Keyword(s):  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Learning Models

scholarly journals The Survey: Advances in Natural Language Processing using Deep Learning*

2021 ◽  
Vol 12 (4) ◽  
pp. 1035-1040
Author(s):  
Vamsi Krishna Vedantam
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Successful Implementation ◽  
Critical Areas ◽  
Processing Techniques ◽  
Automatic Text ◽  
Linguistic Models

Natural Language Processing using Deep Learning is one of the critical areas of Artificial Intelligence to focus in the next decades. Over the last few years, Artificial intelligence had evolved by maturing critical areas in research and development. The latest developments in Natural Language Processing con- tributed to the successful implementation of machine translations, linguistic models, Speech recognitions, automatic text generations applications. This paper covers the recent advancements in Natural Language Processing using Deep Learning and some of the much-waited areas in NLP to look for in the next few years. The first section explains Deep Learning architecture, Natural Language Processing techniques followed by the second section that highlights the developments in NLP using Deep learning and the last part by concluding the critical takeaways from my article.

scholarly journals Artificial Intelligence in Business

2020 ◽  
pp. 1-38
Author(s):  
Amandeep Kaur ◽  
◽  
Anjum Mohammad Aslam ◽  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Case Studies ◽  
Language Processing ◽  
Learning Abilities ◽  
The Core

In this chapter we discuss the core concept of Artificial Intelligence. We define the term of Artificial Intelligence and its interconnected terms such as Machine learning, deep learning, Neural Networks. We describe the concept with the perspective of its usage in the area of business. We further analyze various applications and case studies which can be achieved using Artificial Intelligence and its sub fields. In the area of business already numerous Artificial Intelligence applications are being utilized and will be expected to be utilized more in the future where machines will improve the Artificial Intelligence, Natural language processing, Machine learning abilities of humans in various zones.

Comparison of deep learning models for natural language processing-based classification of non-English head CT reports

2020 ◽  
Vol 62 (10) ◽  
pp. 1247-1256 ◽  
Author(s):  
Yiftach Barash ◽  
Gennadiy Guralnik ◽  
Noam Tau ◽  
Shelly Soffer ◽  
Tal Levy ◽  
...  
Keyword(s):  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Learning Models ◽  
Head Ct

Adversarial Attacks on Deep-learning Models in Natural Language Processing

2020 ◽  
Vol 11 (3) ◽  
pp. 1-41 ◽  
Author(s):  
Wei Emma Zhang ◽  
Quan Z. Sheng ◽  
Ahoud Alhazmi ◽  
Chenliang Li
Keyword(s):  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Learning Models

scholarly journals Artificial Intelligence Surgery: How Do We Get to Autonomous Actions in Surgery?

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5526
Author(s):  
Andrew A. Gumbs ◽  
Isabella Frigerio ◽  
Gaya Spolverato ◽  
Roland Croner ◽  
Alfredo Illanes ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Computer Vision ◽  
Interventional Radiology ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Paradigm Shift ◽  
Timely Manner

Most surgeons are skeptical as to the feasibility of autonomous actions in surgery. Interestingly, many examples of autonomous actions already exist and have been around for years. Since the beginning of this millennium, the field of artificial intelligence (AI) has grown exponentially with the development of machine learning (ML), deep learning (DL), computer vision (CV) and natural language processing (NLP). All of these facets of AI will be fundamental to the development of more autonomous actions in surgery, unfortunately, only a limited number of surgeons have or seek expertise in this rapidly evolving field. As opposed to AI in medicine, AI surgery (AIS) involves autonomous movements. Fortuitously, as the field of robotics in surgery has improved, more surgeons are becoming interested in technology and the potential of autonomous actions in procedures such as interventional radiology, endoscopy and surgery. The lack of haptics, or the sensation of touch, has hindered the wider adoption of robotics by many surgeons; however, now that the true potential of robotics can be comprehended, the embracing of AI by the surgical community is more important than ever before. Although current complete surgical systems are mainly only examples of tele-manipulation, for surgeons to get to more autonomously functioning robots, haptics is perhaps not the most important aspect. If the goal is for robots to ultimately become more and more independent, perhaps research should not focus on the concept of haptics as it is perceived by humans, and the focus should be on haptics as it is perceived by robots/computers. This article will discuss aspects of ML, DL, CV and NLP as they pertain to the modern practice of surgery, with a focus on current AI issues and advances that will enable us to get to more autonomous actions in surgery. Ultimately, there may be a paradigm shift that needs to occur in the surgical community as more surgeons with expertise in AI may be needed to fully unlock the potential of AIS in a safe, efficacious and timely manner.

scholarly journals Natural language inference by deep learning method

2022 ◽  
Vol 355 ◽  
pp. 03028
Author(s):  
Saihan Li ◽  
Zhijie Hu ◽  
Rong Cao
Keyword(s):  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Learning Method ◽  
Word Embeddings ◽  
Learning Models ◽  
The Relationship ◽  
Inference Task

Natural Language inference refers to the problem of determining the relationships between a premise and a hypothesis, it is an emerging area of natural language processing. The paper uses deep learning methods to complete natural language inference task. The dataset includes 3GPP dataset and SNLI dataset. Gensim library is used to get the word embeddings, there are 2 methods which are word2vec and doc2vec to map the sentence to array. 2 deep learning models DNNClassifier and Attention are implemented separately to classify the relationship between the proposals from the telecommunication area dataset. The highest accuracy of the experiment is 88% and we found that the quality of the dataset decided the upper bound of the accuracy.

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