DECAB-LSTM: Deep Contextualized Attentional Bidirectional LSTM for cancer hallmark classification

Jiang, Longquan; Sun, Xuan; Mercaldo, Francesco; Santone, Antonella

doi:10.1016/j.knosys.2020.106486

Cited by 23 publications

(14 citation statements)

References 37 publications

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“…e ALSTM has shown significant performance in terms of generalization. In [18], deep contextualized attentional bidirectional LSTM (DCABLSTM) was proposed. By utilizing the contextual attention mechanism, DCABLSTM has the ability of learning to attend to the valuable knowledge in a string.…”

Section: Related Workmentioning

confidence: 99%

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Evolving Long Short-Term Memory Network-Based Text Classification

Singh

Dargar

Gupta³

et al. 2022

Computational Intelligence and Neuroscience

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Recently, long short-term memory (LSTM) networks are extensively utilized for text classification. Compared to feed-forward neural networks, it has feedback connections, and thus, it has the ability to learn long-term dependencies. However, the LSTM networks suffer from the parameter tuning problem. Generally, initial and control parameters of LSTM are selected on a trial and error basis. Therefore, in this paper, an evolving LSTM (ELSTM) network is proposed. A multiobjective genetic algorithm (MOGA) is used to optimize the architecture and weights of LSTM. The proposed model is tested on a well-known factory reports dataset. Extensive analyses are performed to evaluate the performance of the proposed ELSTM network. From the comparative analysis, it is found that the LSTM network outperforms the competitive models.

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Section: Related Workmentioning

confidence: 99%

“…Additionally, CLSTM was free from any specific domain. However, [16][17][18][19][20][21][22][23] are sensitive to its initial parameters.…”

Section: Related Workmentioning

confidence: 99%

Evolving Long Short-Term Memory Network-Based Text Classification

Singh

Dargar

Gupta³

et al. 2022

Computational Intelligence and Neuroscience

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“…Attention mechanisms were first introduced in natural language processing (22)(23)(24)(25). Currently, attention mechanisms are also widely used in deep learning to enhance feature extraction (26)(27)(28).…”

Section: Attention Mechanismmentioning

confidence: 99%

RDAU-Net: Based on a Residual Convolutional Neural Network With DFP and CBAM for Brain Tumor Segmentation

Wang

Luan

et al. 2022

Front. Oncol.

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Due to the high heterogeneity of brain tumors, automatic segmentation of brain tumors remains a challenging task. In this paper, we propose RDAU-Net by adding dilated feature pyramid blocks with 3D CBAM blocks and inserting 3D CBAM blocks after skip-connection layers. Moreover, a CBAM with channel attention and spatial attention facilitates the combination of more expressive feature information, thereby leading to more efficient extraction of contextual information from images of various scales. The performance was evaluated on the Multimodal Brain Tumor Segmentation (BraTS) challenge data. Experimental results show that RDAU-Net achieves state-of-the-art performance. The Dice coefficient for WT on the BraTS 2019 dataset exceeded the baseline value by 9.2%.

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“…Innovative approaches to combine BOW-TFIDF inputs with word embeddings, or to use phrases rather than individual words as inputs hence preserving more syntactical meaning, may also yield improvements to algorithmic performance. Finally, alternative deep learning models can also be used to increase classification accuracy-these include alternative architectures such as Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNN) that have shown promise in text classification applications [31,32]. Finally, efforts to implement, utilize and refine these deep learning approaches in a clinical setting will be crucial to improving performance and applicability.…”

Section: Plos Onementioning

confidence: 99%

A natural language processing and deep learning approach to identify child abuse from pediatric electronic medical records

et al. 2021

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Child physical abuse is a leading cause of traumatic injury and death in children. In 2017, child abuse was responsible for 1688 fatalities in the United States, of 3.5 million children referred to Child Protection Services and 674,000 substantiated victims. While large referral hospitals maintain teams trained in Child Abuse Pediatrics, smaller community hospitals often do not have such dedicated resources to evaluate patients for potential abuse. Moreover, identification of abuse has a low margin of error, as false positive identifications lead to unwarranted separations, while false negatives allow dangerous situations to continue. This context makes the consistent detection of and response to abuse difficult, particularly given subtle signs in young, non-verbal patients. Here, we describe the development of artificial intelligence algorithms that use unstructured free-text in the electronic medical record—including notes from physicians, nurses, and social workers—to identify children who are suspected victims of physical abuse. Importantly, only the notes from time of first encounter (e.g.: birth, routine visit, sickness) to the last record before child protection team involvement were used. This allowed us to develop an algorithm using only information available prior to referral to the specialized child protection team. The study was performed in a multi-center referral pediatric hospital on patients screened for abuse within five different locations between 2015 and 2019. Of 1123 patients, 867 records were available after data cleaning and processing, and 55% were abuse-positive as determined by a multi-disciplinary team of clinical professionals. These electronic medical records were encoded with three natural language processing (NLP) algorithms—Bag of Words (BOW), Word Embeddings (WE), and Rules-Based (RB)—and used to train multiple neural network architectures. The BOW and WE encodings utilize the full free-text, while RB selects crucial phrases as identified by physicians. The best architecture was selected by average classification accuracy for the best performing model from each train-test split of a cross-validation experiment. Natural language processing coupled with neural networks detected cases of likely child abuse using only information available to clinicians prior to child protection team referral with average accuracy of 0.90±0.02 and average area under the receiver operator characteristic curve (ROC-AUC) 0.93±0.02 for the best performing Bag of Words models. The best performing rules-based models achieved average accuracy of 0.77±0.04 and average ROC-AUC 0.81±0.05, while a Word Embeddings strategy was severely limited by lack of representative embeddings. Importantly, the best performing model had a false positive rate of 8%, as compared to rates of 20% or higher in previously reported studies. This artificial intelligence approach can help screen patients for whom an abuse concern exists and streamline the identification of patients who may benefit from referral to a child protection team. Furthermore, this approach could be applied to develop computer-aided-diagnosis platforms for the challenging and often intractable problem of reliably identifying pediatric patients suffering from physical abuse.

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DECAB-LSTM: Deep Contextualized Attentional Bidirectional LSTM for cancer hallmark classification

Cited by 23 publications

References 37 publications

Evolving Long Short-Term Memory Network-Based Text Classification

Evolving Long Short-Term Memory Network-Based Text Classification

RDAU-Net: Based on a Residual Convolutional Neural Network With DFP and CBAM for Brain Tumor Segmentation

A natural language processing and deep learning approach to identify child abuse from pediatric electronic medical records

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