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  1. Book ; Online: Multi label classification of Artificial Intelligence related patents using Modified D2SBERT and Sentence Attention mechanism

    Yoo, Yongmin / Heo, Tak-Sung / Lim, Dongjin / Seo, Deaho

    2023  

    Abstract: Patent classification is an essential task in patent information management and patent knowledge mining. It is very important to classify patents related to artificial intelligence, which is the biggest topic these days. However, artificial intelligence- ... ...

    Abstract Patent classification is an essential task in patent information management and patent knowledge mining. It is very important to classify patents related to artificial intelligence, which is the biggest topic these days. However, artificial intelligence-related patents are very difficult to classify because it is a mixture of complex technologies and legal terms. Moreover, due to the unsatisfactory performance of current algorithms, it is still mostly done manually, wasting a lot of time and money. Therefore, we present a method for classifying artificial intelligence-related patents published by the USPTO using natural language processing technique and deep learning methodology. We use deformed BERT and sentence attention overcome the limitations of BERT. Our experiment result is highest performance compared to other deep learning methods.
    Keywords Computer Science - Computation and Language ; Computer Science - Artificial Intelligence
    Subject code 401 ; 006
    Publishing date 2023-03-03
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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    Inter-library loan at ZB MED

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  2. Book ; Online: Solar cell patent classification method based on keyword extraction and deep neural network

    Yoo, Yongmin / Lim, Dongjin / Heo, Tak-Sung

    2021  

    Abstract: With the growing impact of ESG on businesses, research related to renewable energy is receiving great attention. Solar cells are one of them, and accordingly, it can be said that the research value of solar cell patent analysis is very high. Patent ... ...

    Abstract With the growing impact of ESG on businesses, research related to renewable energy is receiving great attention. Solar cells are one of them, and accordingly, it can be said that the research value of solar cell patent analysis is very high. Patent documents have high research value. Being able to accurately analyze and classify patent documents can reveal several important technical relationships. It can also describe the business trends in that technology. And when it comes to investment, new industrial solutions will also be inspired and proposed to make important decisions. Therefore, we must carefully analyze patent documents and utilize the value of patents. To solve the solar cell patent classification problem, we propose a keyword extraction method and a deep neural network-based solar cell patent classification method. First, solar cell patents are analyzed for pretreatment. It then uses the KeyBERT algorithm to extract keywords and key phrases from the patent abstract to construct a lexical dictionary. We then build a solar cell patent classification model according to the deep neural network. Finally, we use a deep neural network-based solar cell patent classification model to classify power patents, and the training accuracy is greater than 95%. Also, the validation accuracy is about 87.5%. It can be seen that the deep neural network method can not only realize the classification of complex and difficult solar cell patents, but also have a good classification effect.
    Keywords Computer Science - Information Retrieval ; Computer Science - Computation and Language
    Subject code 006
    Publishing date 2021-09-17
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  3. Book ; Online: A novel hybrid methodology of measuring sentence similarity

    Yoo, Yongmin / Heo, Tak-Sung / Park, Yeongjoon

    2021  

    Abstract: The problem of measuring sentence similarity is an essential issue in the natural language processing (NLP) area. It is necessary to measure the similarity between sentences accurately. There are many approaches to measuring sentence similarity. Deep ... ...

    Abstract The problem of measuring sentence similarity is an essential issue in the natural language processing (NLP) area. It is necessary to measure the similarity between sentences accurately. There are many approaches to measuring sentence similarity. Deep learning methodology shows a state-of-the-art performance in many natural language processing fields and is used a lot in sentence similarity measurement methods. However, in the natural language processing field, considering the structure of the sentence or the word structure that makes up the sentence is also important. In this study, we propose a methodology combined with both deep learning methodology and a method considering lexical relationships. Our evaluation metric is the Pearson correlation coefficient and Spearman correlation coefficient. As a result, the proposed method outperforms the current approaches on a KorSTS standard benchmark Korean dataset. Moreover, it performs a maximum of 65% increase than only using deep learning methodology. Experiments show that our proposed method generally results in better performance than those with only a deep learning model.
    Keywords Computer Science - Artificial Intelligence
    Subject code 006
    Publishing date 2021-05-03
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  4. Book ; Online: Medical Code Prediction from Discharge Summary

    Heo, Tak-Sung / Yoo, Yongmin / Park, Yeongjoon / Jo, Byeong-Cheol

    Document to Sequence BERT using Sequence Attention

    2021  

    Abstract: Clinical notes are unstructured text generated by clinicians during patient encounters. Clinical notes are usually accompanied by a set of metadata codes from the international classification of diseases (ICD). ICD code is an important code used in a ... ...

    Abstract Clinical notes are unstructured text generated by clinicians during patient encounters. Clinical notes are usually accompanied by a set of metadata codes from the international classification of diseases (ICD). ICD code is an important code used in a variety of operations, including insurance, reimbursement, medical diagnosis, etc. Therefore, it is important to classify ICD codes quickly and accurately. However, annotating these codes is costly and time-consuming. So we propose a model based on bidirectional encoder representations from transformer (BERT) using the sequence attention method for automatic ICD code assignment. We evaluate our ap-proach on the MIMIC-III benchmark dataset. Our model achieved performance of Macro-aver-aged F1: 0.62898 and Micro-averaged F1: 0.68555, and is performing better than a performance of the previous state-of-the-art model. The contribution of this study proposes a method of using BERT that can be applied to documents and a sequence attention method that can capture im-portant sequence information appearing in documents.
    Keywords Computer Science - Artificial Intelligence
    Subject code 005
    Publishing date 2021-06-15
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  5. Book ; Online: DAGAM

    Jo, Byeong-Cheol / Heo, Tak-Sung / Park, Yeongjoon / Yoo, Yongmin / Cho, Won Ik / Kim, Kyungsun

    Data Augmentation with Generation And Modification

    2022  

    Abstract: Text classification is a representative downstream task of natural language processing, and has exhibited excellent performance since the advent of pre-trained language models based on Transformer architecture. However, in pre-trained language models, ... ...

    Abstract Text classification is a representative downstream task of natural language processing, and has exhibited excellent performance since the advent of pre-trained language models based on Transformer architecture. However, in pre-trained language models, under-fitting often occurs due to the size of the model being very large compared to the amount of available training data. Along with significant importance of data collection in modern machine learning paradigm, studies have been actively conducted for natural language data augmentation. In light of this, we introduce three data augmentation schemes that help reduce underfitting problems of large-scale language models. Primarily we use a generation model for data augmentation, which is defined as Data Augmentation with Generation (DAG). Next, we augment data using text modification techniques such as corruption and word order change (Data Augmentation with Modification, DAM). Finally, we propose Data Augmentation with Generation And Modification (DAGAM), which combines DAG and DAM techniques for a boosted performance. We conduct data augmentation for six benchmark datasets of text classification task, and verify the usefulness of DAG, DAM, and DAGAM through BERT-based fine-tuning and evaluation, deriving better results compared to the performance with original datasets.
    Keywords Computer Science - Computation and Language ; Computer Science - Artificial Intelligence
    Subject code 004 ; 006
    Publishing date 2022-04-06
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  6. Article ; Online: Deep learning based prediction of prognosis in nonmetastatic clear cell renal cell carcinoma.

    Byun, Seok-Soo / Heo, Tak Sung / Choi, Jeong Myeong / Jeong, Yeong Seok / Kim, Yu Seop / Lee, Won Ki / Kim, Chulho

    Scientific reports

    2021  Volume 11, Issue 1, Page(s) 1242

    Abstract: Survival analyses for malignancies, including renal cell carcinoma (RCC), have primarily been conducted using the Cox proportional hazards (CPH) model. We compared the random survival forest (RSF) and DeepSurv models with the CPH model to predict ... ...

    Abstract Survival analyses for malignancies, including renal cell carcinoma (RCC), have primarily been conducted using the Cox proportional hazards (CPH) model. We compared the random survival forest (RSF) and DeepSurv models with the CPH model to predict recurrence-free survival (RFS) and cancer-specific survival (CSS) in non-metastatic clear cell RCC (nm-cRCC) patients. Our cohort included 2139 nm-cRCC patients who underwent curative-intent surgery at six Korean institutions between 2000 and 2014. The data of two largest hospitals' patients were assigned into the training and validation dataset, and the data of the remaining hospitals were assigned into the external validation dataset. The performance of the RSF and DeepSurv models was compared with that of CPH using Harrel's C-index. During the follow-up, recurrence and cancer-specific deaths were recorded in 190 (12.7%) and 108 (7.0%) patients, respectively, in the training-dataset. Harrel's C-indices for RFS in the test-dataset were 0.794, 0.789, and 0.802 for CPH, RSF, and DeepSurv, respectively. Harrel's C-indices for CSS in the test-dataset were 0.831, 0.790, and 0.834 for CPH, RSF, and DeepSurv, respectively. In predicting RFS and CSS in nm-cRCC patients, the performance of DeepSurv was superior to that of CPH and RSF. In no distant time, deep learning-based survival predictions may be useful in RCC patients.
    MeSH term(s) Adult ; Aged ; Carcinoma, Renal Cell/mortality ; Databases, Factual ; Deep Learning ; Disease-Free Survival ; Female ; Humans ; Kidney Neoplasms/mortality ; Male ; Middle Aged ; Predictive Value of Tests ; Retrospective Studies ; Survival Rate
    Language English
    Publishing date 2021-01-13
    Publishing country England
    Document type Clinical Trial ; Journal Article ; Multicenter Study ; Research Support, Non-U.S. Gov't
    ZDB-ID 2615211-3
    ISSN 2045-2322 ; 2045-2322
    ISSN (online) 2045-2322
    ISSN 2045-2322
    DOI 10.1038/s41598-020-80262-9
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article: Prediction of Stroke Outcome Using Natural Language Processing-Based Machine Learning of Radiology Report of Brain MRI.

    Heo, Tak Sung / Kim, Yu Seop / Choi, Jeong Myeong / Jeong, Yeong Seok / Seo, Soo Young / Lee, Jun Ho / Jeon, Jin Pyeong / Kim, Chulho

    Journal of personalized medicine

    2020  Volume 10, Issue 4

    Abstract: Brain magnetic resonance imaging (MRI) is useful for predicting the outcome of patients with acute ischemic stroke (AIS). Although deep learning (DL) using brain MRI with certain image biomarkers has shown satisfactory results in predicting poor outcomes, ...

    Abstract Brain magnetic resonance imaging (MRI) is useful for predicting the outcome of patients with acute ischemic stroke (AIS). Although deep learning (DL) using brain MRI with certain image biomarkers has shown satisfactory results in predicting poor outcomes, no study has assessed the usefulness of natural language processing (NLP)-based machine learning (ML) algorithms using brain MRI free-text reports of AIS patients. Therefore, we aimed to assess whether NLP-based ML algorithms using brain MRI text reports could predict poor outcomes in AIS patients. This study included only English text reports of brain MRIs examined during admission of AIS patients. Poor outcome was defined as a modified Rankin Scale score of 3-6, and the data were captured by trained nurses and physicians. We only included MRI text report of the first MRI scan during the admission. The text dataset was randomly divided into a training and test dataset with a 7:3 ratio. Text was vectorized to word, sentence, and document levels. In the word level approach, which did not consider the sequence of words, and the "bag-of-words" model was used to reflect the number of repetitions of text token. The "sent2vec" method was used in the sensation-level approach considering the sequence of words, and the word embedding was used in the document level approach. In addition to conventional ML algorithms, DL algorithms such as the convolutional neural network (CNN), long short-term memory, and multilayer perceptron were used to predict poor outcomes using 5-fold cross-validation and grid search techniques. The performance of each ML classifier was compared with the area under the receiver operating characteristic (AUROC) curve. Among 1840 subjects with AIS, 645 patients (35.1%) had a poor outcome 3 months after the stroke onset. Random forest was the best classifier (0.782 of AUROC) using a word-level approach. Overall, the document-level approach exhibited better performance than did the word- or sentence-level approaches. Among all the ML classifiers, the multi-CNN algorithm demonstrated the best classification performance (0.805), followed by the CNN (0.799) algorithm. When predicting future clinical outcomes using NLP-based ML of radiology free-text reports of brain MRI, DL algorithms showed superior performance over the other ML algorithms. In particular, the prediction of poor outcomes in document-level NLP DL was improved more by multi-CNN and CNN than by recurrent neural network-based algorithms. NLP-based DL algorithms can be used as an important digital marker for unstructured electronic health record data DL prediction.
    Language English
    Publishing date 2020-12-16
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2662248-8
    ISSN 2075-4426
    ISSN 2075-4426
    DOI 10.3390/jpm10040286
    Database MEDical Literature Analysis and Retrieval System OnLINE

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