Clinical Natural Language Processing with Deep Learning

Hasan, Sadid A.; Farri, Oladimeji

doi:10.1007/978-3-030-05249-2_5

Cited by 32 publications

(22 citation statements)

References 54 publications

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“…An example of such models is the skip-gram model ( Mikolov, Yih & Zweig, 2013 ) proposed by Mikolov in 2013 and the model proposed by Gauch, Wang & Rachakonda (1999) . Word2Vec ( Mikolov et al, 2013b ), FastText ( Bojanowski et al, 2017 ), GloVe ( Pennington, Socher & Manning, 2014 ) and WOVe ( Ibrahim et al, 2021 ) are all examples of vector learning methods that have been shown to be superior to traditional NLP methods in different text mining applications ( Gu et al, 2019 ; Hasan & Farri, 2019 ). Some of these techniques have been applied in the medical field to build medical ontologies, such as ( Minarro-Giménez, Marin-Alonso & Samwald, 2014 ; Hughes et al, 2017 ; De Vine et al, 2014 ; Minarro-Giménez, Marín-Alonso & Samwald, 2015 ; Wang, Cao & Zhou, 2015 ).…”

Section: Related Workmentioning

confidence: 99%

An automated method to enrich consumer health vocabularies using GloVe word embeddings and an auxiliary lexical resource

Ibrahim¹,

Gauch²,

Salman³

et al. 2021

PeerJ Computer Science

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Background Clear language makes communication easier between any two parties. A layman may have difficulty communicating with a professional due to not understanding the specialized terms common to the domain. In healthcare, it is rare to find a layman knowledgeable in medical terminology which can lead to poor understanding of their condition and/or treatment. To bridge this gap, several professional vocabularies and ontologies have been created to map laymen medical terms to professional medical terms and vice versa. Objective Many of the presented vocabularies are built manually or semi-automatically requiring large investments of time and human effort and consequently the slow growth of these vocabularies. In this paper, we present an automatic method to enrich laymen’s vocabularies that has the benefit of being able to be applied to vocabularies in any domain. Methods Our entirely automatic approach uses machine learning, specifically Global Vectors for Word Embeddings (GloVe), on a corpus collected from a social media healthcare platform to extend and enhance consumer health vocabularies. Our approach further improves the consumer health vocabularies by incorporating synonyms and hyponyms from the WordNet ontology. The basic GloVe and our novel algorithms incorporating WordNet were evaluated using two laymen datasets from the National Library of Medicine (NLM), Open-Access Consumer Health Vocabulary (OAC CHV) and MedlinePlus Healthcare Vocabulary. Results The results show that GloVe was able to find new laymen terms with an F-score of 48.44%. Furthermore, our enhanced GloVe approach outperformed basic GloVe with an average F-score of 61%, a relative improvement of 25%. Furthermore, the enhanced GloVe showed a statistical significance over the two ground truth datasets with P < 0.001. Conclusions This paper presents an automatic approach to enrich consumer health vocabularies using the GloVe word embeddings and an auxiliary lexical source, WordNet. Our approach was evaluated used healthcare text downloaded from MedHelp.org, a healthcare social media platform using two standard laymen vocabularies, OAC CHV, and MedlinePlus. We used the WordNet ontology to expand the healthcare corpus by including synonyms, hyponyms, and hypernyms for each layman term occurrence in the corpus. Given a seed term selected from a concept in the ontology, we measured our algorithms’ ability to automatically extract synonyms for those terms that appeared in the ground truth concept. We found that enhanced GloVe outperformed GloVe with a relative improvement of 25% in the F-score.

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Section: Related Workmentioning

confidence: 99%

An automated method to enrich consumer health vocabularies using GloVe word embeddings and an auxiliary lexical resource

Ibrahim¹,

Gauch²,

Salman³

et al. 2021

PeerJ Computer Science

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“…The hierarchical structure of natural language suggests the use of deep learning via neural networks that have multiple hidden layers (Satapathy et al, 2018, §1.6). Such deep neural networks have outperformed other forms of machine learning for problems in natural language processing (Hasan and Farri, 2019). For instance, Lee et al (2017) used a deep neural network to detect adverse drug events from social media such as Twitter feeds.…”

Section: Natural Language Processing By Deep Learningmentioning

confidence: 99%

Moderating probability distributions for unrepresented uncertainty: Application to sentiment analysis via deep learning

Bickel

2021

Communications in Statistics - Theory and Methods

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The probability distributions that statistical methods use to represent uncertainty fail to capture all of the uncertainty that may be relevant to decision making. A simple way to adjust probability distributions for the uncertainty not represented in their models is to average the distributions with a uniform distribution or another distribution of maximum uncertainty. A decision theoretic framework leads to averaging the distributions by taking the means of the logit transforms of the probabilities. That method does not prevent convergence to the truth, as does taking the means of the probabilities themselves. The mean-logit approach to moderating distributions is applied to natural language processing performed by a deep neural network.

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“…In the field of text similarity detection, techniques based on deep learning moved researchers towards semantically distributed representations [2], [5], [37], [13], [23], [60], [17]. To find the semantic relatedness among question pairs, we proposed a technique based on deep learn-ing.…”

Section: Proposed Workmentioning

confidence: 99%

Deep Learning based Semantic Similarity Detection using Text Data

Mansoor¹,

Rehman²,

Shaheen³

et al. 2020

ITC

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Similarity detection in the text is the main task for a number of Natural Language Processing (NLP) applications. As textual data is comparatively large in quantity and huge in volume than the numeric data, therefore measuring textual similarity is one of the important problems. Most of the similarity detection algorithms are based upon word to word matching, sentence/paragraph matching, and matching of the whole document. In this research, a novel approach is proposed using deep learning models, combining Long Short Term Memory network (LSTM) with Convolutional Neural Network (CNN) for measuring semantics similarity between two questions. The proposed model takes sentence pairs as input to measure the similarity between them. The model is tested on publicly available Quora’s dataset. The model in comparison to the existing techniques gave 87.50 % accuracy which is better than the previous approaches.

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Clinical Natural Language Processing with Deep Learning

Cited by 32 publications

References 54 publications

An automated method to enrich consumer health vocabularies using GloVe word embeddings and an auxiliary lexical resource

An automated method to enrich consumer health vocabularies using GloVe word embeddings and an auxiliary lexical resource

Moderating probability distributions for unrepresented uncertainty: Application to sentiment analysis via deep learning

Deep Learning based Semantic Similarity Detection using Text Data

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