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 10 publications

(5 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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“…Transforming the unstructured data into structured form could be a crucial task in the medical field, since admission notes, clinical notes, or discharge summary are associated with a patient's history (Nuthakki et al, 2019). Consequently, some studies focus on predicting medical codes from discharge summary (Heo et al, 2021). This can also be viewed as a Admission notes have to be split from the discharge summary first.…”

Section: Analysis Of Unstructured Medical Datamentioning

confidence: 99%

“…However, existing research on unstructured medical data for different tasks, including medication recommendations, was used in earlier practice. For instance, extract symptoms (Sondhi et al, 2012;Tahabi et al, 2023a) using UMLS MetaMap (Aronson, 2001) or predict structured medical codes from unstructured clinical notes through Pre-trained Language Models (PLMs) (Heo et al, 2021). BERT has also been found to underperform in clinical text classification tasks, which may be due to its pretraining and WordPiece tokenization (Gao et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Enhancing text comprehension for Question Answering with Contrastive Learning

Lee¹,

Lee²

2023

Proceedings of the 8th Workshop on Representation Learning for NLP (RepL4NLP 2023)

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Most of the existing medication recommendation models are predicted with only structured data such as medical codes, with the remaining other large amount of unstructured or semi-structured data underutilization. To increase the utilization effectively, we proposed a method of enhancing medication recommendation with Large Language Model (LLM) text representation. LLM harnesses powerful language understanding and generation capabilities, enabling the extraction of information from complex and lengthy unstructured data such as clinical notes which contain complex terminology. This method can be applied to several existing base models we selected and improve medication recommendation performance with the combination representation of text and medical codes experiments on two different datasets.LLM text representation alone can even demonstrate a comparable ability to the medical code representation alone. Overall, this is a general method that can be applied to other models for improved recommendations.

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

Cited by 10 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

Enhancing text comprehension for Question Answering with Contrastive Learning

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

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