Medical Code Prediction from Discharge Summary: Document to Sequence BERT using Sequence Attention

Heo, Tak-Sung; Yoo, Yongmin; Park, YeongJoon; Jo, Byeong-Cheol; Lee, Kyounguk; Kim, Kyungsun

doi:10.1109/icmla52953.2021.00201

Cited by 6 publications

(3 citation statements)

References 14 publications

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“…In the study of [27], BERT was proposed to predict the ICD (International Classification of Diseases) codes, resulting in an F1 value of 0.68. Named Entity Recognition (NER) was modeled using the bidirectional LSTM-RNN [28] model and a transfer learning technique was used for the limited availability of labeled data for Chinese medical records.…”

Section: Deep Learning Methodsmentioning

confidence: 99%

Topic Modeling and Sentiment Analysis of Online Education in the COVID-19 Era Using Social Networks Based Datasets

2022

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Sentiment Analysis (SA) is a technique to study people’s attitudes related to textual data generated from sources like Twitter. This study suggested a powerful and effective technique that can tackle the large contents and can specifically examine the attitudes, sentiments, and fake news of “E-learning”, which is considered a big challenge, as online textual data related to the education sector is considered of great importance. On the other hand, fake news and misinformation related to COVID-19 have confused parents, students, and teachers. An efficient detection approach should be used to gather more precise information in order to identify COVID-19 disinformation. Tweet records (people’s opinions) have gained significant attention worldwide for understanding the behaviors of people’s attitudes. SA of the COVID-19 education sector still does not provide a clear picture of the information available in these tweets, especially if this misinformation and fake news affect the field of E-learning. This study has proposed denoising AutoEncoder to eliminate noise in information, the attentional mechanism for a fusion of features as parts where a fusion of multi-level features and ELM-AE with LSTM is applied for the task of SA classification. Experiments show that our suggested approach obtains a higher F1-score value of 0.945, compared with different state-of-the-art approaches, with various sizes of testing and training datasets. Based on our knowledge, the proposed model can learn from unified features set to obtain good performance, better results than one that can be learned from the subset of features.

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Section: Deep Learning Methodsmentioning

confidence: 99%

Topic Modeling and Sentiment Analysis of Online Education in the COVID-19 Era Using Social Networks Based Datasets

2022

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“…There are also cases where deep learning has been applied in the medical field. Reference [42] proposed a method to find out the name of a disease through the clinical note of patients using deep learning. Reference [43] proposed a method for predicting the deterioration of dementia based on medical records.…”

Section: Application Of Deep Learning In Various Fieldsmentioning

confidence: 99%

Improving Shopping Mall Revenue by Real-Time Customized Digital Coupon Issuance

Seo

Yoo²

2023

IEEE Access

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With the development of big data and deep learning technology, big data and deep learning technology have also been applied to the marketing field, which was a part of business administration. Customer churn management is one of the most important areas of marketing. In this paper, we proposed a method to prevent customer churn and increase purchase conversion rate by issuing customized discount coupons to customers with high churn rate based on big data in real time. After segmenting customer segments with two-dimensional segment analysis, a real-time churn rate estimation model based on clickstream data was generated for each segment. After that, we issued customized coupons to our customers. Finally, we tested the conversion rate and sales growth. A two-dimensional cluster analysis-based churn rate estimation combined with a recommendation system was found to be significantly more useful than the respective simple models. Using this proposed model, it is possible to increase sales by automatically estimating the customer's churn probability and shopping propensity without the burden of marketing costs in the online shopping mall.

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“…Hierarchical methods leveraging the hierarchical structure of ICD codes are also present [12]. Transformer-based methods such as bi-directional encoder representation from Transformer (BERT) which carried state-of-the-art results in the area of natural language processing have also been proposed in the field of automated ICD coding [13,14].…”

Section: Related Workmentioning

confidence: 99%

A Residual CNN Model for ICD Assignment

Cheng¹,

Ting²,

Ho³

2022

Proceedings of the International Conference on Computer, Information Technology and Intelligent Computing (CITIC 2022)

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International Classification of Diseases (ICD) has been used as a standardized way of classifying a diagnosis or a medical procedure. ICD has also been employed to keep track of illness progression and treatment purposes. However, the assignment methods often require manual input of medical professionals and therefore time consuming and prone to human errors. By automating the assignment of ICD-9 codes to clinical notes we can effectively save time and human resources. In this light, this study proposed a residual convolution neural network leveraging label co-occurrence to measure label correlation and a label attention mechanism to capture label-dependent features. The model was fine-tuned by changing its hyper-parameters which have included dropout probabilities, CNN kernel size and its output size. The empirical findings suggested that the model has outperformed conventional approaches with 93.6% for Micro-AUC, 91.8% for Macro-AUC, 70.0% Micro-F1, and 64.6% for Macro-F1.

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Medical Code Prediction from Discharge Summary: Document to Sequence BERT using Sequence Attention

Cited by 6 publications

References 14 publications

Topic Modeling and Sentiment Analysis of Online Education in the COVID-19 Era Using Social Networks Based Datasets

Topic Modeling and Sentiment Analysis of Online Education in the COVID-19 Era Using Social Networks Based Datasets

Improving Shopping Mall Revenue by Real-Time Customized Digital Coupon Issuance

A Residual CNN Model for ICD Assignment

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