A hybrid medical text classification framework: Integrating attentive rule construction and neural network

Li, Xiang; Cui, Menglin; Li, Jingpeng; Bai, Ruibin; Lu, Zheng; Aickelin, Uwe

doi:10.1016/j.neucom.2021.02.069

Cited by 44 publications

(13 citation statements)

References 36 publications

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“…Memristive switches were utilized to hold the required details for the text classification. In [12], a hybrid model was proposed. It has integrated the gated attentionbased BLSTM and the regular expression-based classifier.…”

Section: Introductionmentioning

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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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: Introductionmentioning

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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“…The size of the convolution filter determines how many spatial features can be obtained in one convolution, and thus, choosing a suitable filter size has an important influence on the effect of convolution. Generally, combining convolution kernel sizes with similar results produces a better performance (Li, X., et al, 2021). Therefore, the combination of convolution filters in this paper includes 4 groups, the widths are [2,3,4], [3,4,5], [4,5,6], and [5,6,7].…”

Section: Classification Effect Of Different Bcbga Parametersmentioning

confidence: 99%

A BERT-Based Hybrid Short Text Classification Model Incorporating CNN and Attention-Based BiGRU

Bao

Ren

Luo

et al. 2021

Journal of Organizational and End User Computing

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Short text classification is a research focus for natural language processing (NLP), which is widely used in news classification, sentiment analysis, mail filtering and other fields. In recent years, deep learning techniques are applied to text classification and has made some progress. Different from ordinary text classification, short text has the problem of less vocabulary and feature sparsity, which raise higher request for text semantic feature representation. To address this issue, this paper propose a feature fusion framework based on the Bidirectional Encoder Representations from Transformers (BERT). In this hybrid method, BERT is used to train word vector representation. Convolutional neural network (CNN) capture static features. As a supplement, a bi-gated recurrent neural network (BiGRU) is adopted to capture contextual features. Furthermore, an attention mechanism is introduced to assign the weight of salient words. The experimental results confirmed that the proposed model significantly outperforms the other state-of-the-art baseline methods.

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“…Yet, machine learning models could be resource intensive. Hybrid models leverage the strength of both approaches in terms of combinatorial patterns among words and semantic relationships [ 17 , 18 ] and are adaptive to low-resource settings.…”

Section: Introductionmentioning

confidence: 99%

Extracting Medical Information From Free-Text and Unstructured Patient-Generated Health Data Using Natural Language Processing Methods: Feasibility Study With Real-world Data

Sezgın¹,

Hussain²,

Rust³

et al. 2023

JMIR Form Res

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Background Patient-generated health data (PGHD) captured via smart devices or digital health technologies can reflect an individual health journey. PGHD enables tracking and monitoring of personal health conditions, symptoms, and medications out of the clinic, which is crucial for self-care and shared clinical decisions. In addition to self-reported measures and structured PGHD (eg, self-screening, sensor-based biometric data), free-text and unstructured PGHD (eg, patient care note, medical diary) can provide a broader view of a patient’s journey and health condition. Natural language processing (NLP) is used to process and analyze unstructured data to create meaningful summaries and insights, showing promise to improve the utilization of PGHD. Objective Our aim is to understand and demonstrate the feasibility of an NLP pipeline to extract medication and symptom information from real-world patient and caregiver data. Methods We report a secondary data analysis, using a data set collected from 24 parents of children with special health care needs (CSHCN) who were recruited via a nonrandom sampling approach. Participants used a voice-interactive app for 2 weeks, generating free-text patient notes (audio transcription or text entry). We built an NLP pipeline using a zero-shot approach (adaptive to low-resource settings). We used named entity recognition (NER) and medical ontologies (RXNorm and SNOMED CT [Systematized Nomenclature of Medicine Clinical Terms]) to identify medication and symptoms. Sentence-level dependency parse trees and part-of-speech tags were used to extract additional entity information using the syntactic properties of a note. We assessed the data; evaluated the pipeline with the patient notes; and reported the precision, recall, and F1 scores. Results In total, 87 patient notes are included (audio transcriptions n=78 and text entries n=9) from 24 parents who have at least one CSHCN. The participants were between the ages of 26 and 59 years. The majority were White (n=22, 92%), had more than one child (n=16, 67%), lived in Ohio (n=22, 92%), had mid- or upper-mid household income (n=15, 62.5%), and had higher level education (n=24, 58%). Out of 87 notes, 30 were drug and medication related, and 46 were symptom related. We captured medication instances (medication, unit, quantity, and date) and symptoms satisfactorily (precision >0.65, recall >0.77, F1>0.72). These results indicate the potential when using NER and dependency parsing through an NLP pipeline on information extraction from unstructured PGHD. Conclusions The proposed NLP pipeline was found to be feasible for use with real-world unstructured PGHD to accomplish medication and symptom extraction. Unstructured PGHD can be leveraged to inform clinical decision-making, remote monitoring, and self-care including medical adherence and chronic disease management. With customizable information extraction methods using NER and medical ontologies, NLP models can feasibly extract a broad range of clinical information from unstructured PGHD in low-resource settings (eg, a limited number of patient notes or training data).

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A hybrid medical text classification framework: Integrating attentive rule construction and neural network

Cited by 44 publications

References 36 publications

Evolving Long Short-Term Memory Network-Based Text Classification

Evolving Long Short-Term Memory Network-Based Text Classification

A BERT-Based Hybrid Short Text Classification Model Incorporating CNN and Attention-Based BiGRU

Extracting Medical Information From Free-Text and Unstructured Patient-Generated Health Data Using Natural Language Processing Methods: Feasibility Study With Real-world Data

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