Aspect-based sentiment analysis of drug reviews using multi-task learning based dual BiLSTM model

Rani, Somiya; Jain, Amita

doi:10.1007/s11042-023-16360-3

Cited by 8 publications

(2 citation statements)

References 43 publications

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“…This layer performs element‐wise summations of the representations from the previous layers 47 . The concatenation of two independent networks is represented by

{h}_{\mathrm{Final}}

as shown in Equation ().

$$ {h}_{\mathrm{Final}}=\left[{h}_{\mathrm{BiMogrifier}\hbox{-} \mathrm{LSTM}}+{h}_{\mathrm{BiLSTM}}\right].

…”

Section: Proposed Methodologymentioning

confidence: 99%

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CCheXR‐Attention: Clinical concept extraction and chest x‐ray reports classification using modified Mogrifier and bidirectional LSTM with multihead attention

Rani,

Jain,

Kumar

et al. 2024

Int J Imaging Syst Tech

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Radiology reports cover different aspects from radiological observation to the diagnosis of an imaging examination, such as x‐rays, magnetic resonance imaging, and computed tomography scans. Abundant patient information presented in radiology reports poses a few major challenges. First, radiology reports follow a free‐text reporting format, which causes the loss of a large amount of information in unstructured text. Second, the extraction of important features from these reports is a huge bottleneck for machine learning models. These challenges are important, particularly the extraction of key features such as symptoms, comparison/priors, technique, finding, and impression because they facilitate the decision‐making on patients' health. To alleviate this issue, a novel architecture CCheXR‐Attention is proposed to extract the clinical features from the radiological reports and classify each report into normal and abnormal categories based on the extracted information. We have proposed a modified Mogrifier long short‐term memory model and integrated a multihead attention method to extract the more relevant features. Experimental outcomes on two benchmark datasets demonstrated that the proposed model surpassed state‐of‐the‐art models.

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“…This layer performs element‐wise summations of the representations from the previous layers 47 . The concatenation of two independent networks is represented by

{h}_{\mathrm{Final}}

as shown in Equation ().

$$ {h}_{\mathrm{Final}}=\left[{h}_{\mathrm{BiMogrifier}\hbox{-} \mathrm{LSTM}}+{h}_{\mathrm{BiLSTM}}\right].

…”

Section: Proposed Methodologymentioning

confidence: 99%

“…This layer performs element-wise summations of the representations from the previous layers. 47 The concatenation of two independent networks is represented by h Final as shown in Equation (16).…”

Section: Concatenation Layermentioning

confidence: 99%