Text Classification for Clinical Trial Operations: Evaluation and Comparison of Natural Language Processing Techniques

Richard, Emma; Reddy, B. Ravinder

doi:10.1007/s43441-020-00236-x

Cited by 6 publications

(6 citation statements)

References 8 publications

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“…Experimental results performed on a set of 1033 text document demonstrated that the classifier provided the best results when the feature set size is small. Richard et al [26] reviewd how the NLP techniques of TF-IDF combined with the supervised machine learning model of SVM and word embedding approaches such as Word2vec could be used to categorize/label protocol deviations across multiple therapeutic areas. Annalisa et al [27] presented an updated survey of 12 machine learning text classifiers applied to a public spam corpus.…”

Section: ) Machine Learningmentioning

confidence: 99%

Word-Level and Pinyin-Level Based Chinese Short Text Classification

Sun

Huo

2022

IEEE Access

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Short text classification is an important branch of Natural Language Processing. Although CNN and RNN have achieved satisfactory results in the text classification tasks, they are difficult to apply to the Chinese short text classification because of the data sparsity and the homophonic typos problems of them. To solve the above problems, word-level and Pinyin-level based Chinese short text classification model is constructed. Since homophones have the same Pinyin, the addition of Pinyin-level features can solve the homophonic typos problem. In addition, due to the introduction of more features, the data sparsity problem of short text can be solved. In order to fully extract the deep hidden features of the short text, a deep learning model based on BiLSTM, Attention and CNN is constructed, and the residual network is used to solve the gradient disappearance problem with the increase of network layers. Additionally, considering that the complex deep learning network structure will increase the text classification time, the Text Center is constructed. When there is a new text input, the text classification task can be quickly realized by calculating the Manhattan distance between the embedding vector of it and the vectors stored in the Text Center. The Accuracy, Precision, Recall and F1 of the proposed model on the simplifyweibo_4_moods dataset are 0.9713, 0.9627, 0.9765 and 0.9696 respectively, and those on the online_shopping_10_cats dataset are 0.9533, 0.9416, 0.9608 and 0.9511 respectively, which are better than that of the baseline method. In addition, the classification time of the proposed model on simplifyweibo_4_moods and online_shopping_10_cats is 0.0042 and 0.0033 respectively, which is far lower than that of the baseline method.

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Section: ) Machine Learningmentioning

confidence: 99%

Word-Level and Pinyin-Level Based Chinese Short Text Classification

Sun

Huo

2022

IEEE Access

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“…Demonstration projects for each were presented, only one of which was published in the peer-reviewed literature. 41,42 There is an opportunity to identify "digital biomarkers" based on algorithms processing various data sets, perhaps derived from wearable technology, such as smart watches, smart phones, or pedometers. 43 For example, output of wearables, such as pedometers that count steps, could be useful to monitor vulnerable elderly patients at risk for rapid deterioration.…”

Section: Opportunitiesmentioning

confidence: 99%

“…Demonstration projects for each were presented, only one of which was published in the peer‐reviewed literature. 41 , 42 …”

Section: Clinical Research (T2)mentioning

confidence: 99%

“…A recent industry meeting on AI in clinical research hosted by the European Medicines Agency featured presentation of multiple opportunities for application of AI in clinical research, including clinical data management—such as data cleaning, fraud detection, protocol violation trend detection, building and validation of synthetic control arms, and classification of clinical events, conditions, or findings. Demonstration projects for each were presented, only one of which was published in the peer‐reviewed literature 41,42 …”

Section: Clinical Research (T2)mentioning

confidence: 99%

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Artificial intelligence in clinical and translational science: Successes, challenges and opportunities

Bernstam

Shireman

Meric‐Bernstam

et al. 2021

Clinical Translational Sci

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“…In order to represent words’ semantic information, Word2Vec maps vocabulary words to fixed-length vectors based on word distribution hypotheses. At present, Word2Vec is widely used not only in dialogue act recognition ( Cerisara et al, 2018 ), topic classification ( Daouadi et al, 2021 ; Mohamed et al, 2021 ), and sentiment analysis ( Bao et al, 2020 ; Muhammad et al, 2021 ) but also in the field of medical science ( Ji et al, 2020 ; Richard and Reddy, 2020 ). However, studies have shown that simply using the NLP method is not entirely feasible because there are certain differences between natural language and TCM formulae ( Li and Yang, 2017 ).…”

Section: Introductionmentioning

confidence: 99%

An Improved Deep Learning Model: S-TextBLCNN for Traditional Chinese Medicine Formula Classification

et al. 2021

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Purpose: This study proposes an S-TextBLCNN model for the efficacy of traditional Chinese medicine (TCM) formula classification. This model uses deep learning to analyze the relationship between herb efficacy and formula efficacy, which is helpful in further exploring the internal rules of formula combination.Methods: First, for the TCM herbs extracted from Chinese Pharmacopoeia, natural language processing (NLP) is used to learn and realize the quantitative expression of different TCM herbs. Three features of herb name, herb properties, and herb efficacy are selected to encode herbs and to construct formula-vector and herb-vector. Then, based on 2,664 formulae for stroke collected in TCM literature and 19 formula efficacy categories extracted from Yifang Jijie, an improved deep learning model TextBLCNN consists of a bidirectional long short-term memory (Bi-LSTM) neural network and a convolutional neural network (CNN) is proposed. Based on 19 formula efficacy categories, binary classifiers are established to classify the TCM formulae. Finally, aiming at the imbalance problem of formula data, the over-sampling method SMOTE is used to solve it and the S-TextBLCNN model is proposed.Results: The formula-vector composed of herb efficacy has the best effect on the classification model, so it can be inferred that there is a strong relationship between herb efficacy and formula efficacy. The TextBLCNN model has an accuracy of 0.858 and an F1-score of 0.762, both higher than the logistic regression (acc = 0.561, F1-score = 0.567), SVM (acc = 0.703, F1-score = 0.591), LSTM (acc = 0.723, F1-score = 0.621), and TextCNN (acc = 0.745, F1-score = 0.644) models. In addition, the over-sampling method SMOTE is used in our model to tackle data imbalance, and the F1-score is greatly improved by an average of 47.1% in 19 models.Conclusion: The combination of formula feature representation and the S-TextBLCNN model improve the accuracy in formula efficacy classification. It provides a new research idea for the study of TCM formula compatibility.

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Text Classification for Clinical Trial Operations: Evaluation and Comparison of Natural Language Processing Techniques

Cited by 6 publications

References 8 publications

Word-Level and Pinyin-Level Based Chinese Short Text Classification

Word-Level and Pinyin-Level Based Chinese Short Text Classification

Artificial intelligence in clinical and translational science: Successes, challenges and opportunities

An Improved Deep Learning Model: S-TextBLCNN for Traditional Chinese Medicine Formula Classification

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