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Improving the Identification fi cation of Diabetic Retinopathy and Related Conditions in the Electronic Health Record Using Natural Language Processing Methods

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OPHTHALMOLOGY SCIENCE 2024年第6期4卷 100578-100578页

作者： Harrigian, Keith Tran, Diep Tang, Tina Gonzales, Anthony Nagy, Paul Kharrazi, Hadi Dredze, Mark Cai, Cindy X. Johns Hopkins Univ Dept Comp Sci Baltimore MD USA Johns Hopkins Sch Med Wilmer Eye Inst Baltimore MD USA Johns Hopkins Univ Johns Hopkins Sch Med Dept Biomed Informat & Data Sci Baltimore MD USA Johns Hopkins Univ Ctr Populat Hlth Informat Technol Bloomberg Sch Publ Hlth Baltimore MD USA

Purpose: To compare the performance of 3 phenotyping methods in identifying diabetic retinopathy (DR) and related clinical conditions. Design: Three phenotyping methods were used to identify clinical conditions including unspecified DR, nonproliferative DR (NPDR) (mild, moderate, severe), consolidated NPDR (unspecified DR or any NPDR), proliferative DR, diabetic macular edema (DME), vitreous hemorrhage, retinal detachment (RD) (tractional RD or combined tractional and rhegmatogenous RD), and neovascular glaucoma (NVG). The first method used only International Classification of Diseases, 10th Revision (ICD-10) diagnosis codes (ICD-10 Lookup System). The next 2 methods used a bidirectional encoder representations from transformers with a dense Multilayer Perceptron output layer natural language processing (NLP) framework. The NLP framework was applied either to free-text of provider notes (Text-Only NLP System) or both free-text and ICD-10 diagnosis codes (Text-and-International Classification of Diseases [ICD] NLP System). Subjects: Adults >= 18 years with diabetes mellitus seen at the Wilmer Eye Institute. Methods: We compared the performance of the 3 phenotyping methods in identifying the DR related conditions with gold standard chart review. We also compared the estimated disease prevalence using each method. Main Outcome Measures: Performance of each method was reported as the macro F1 score. The agreement between the methods was calculated using the kappa statistic. Prevalence estimates were also calculated for each method. Results: A total of 91 097 patients and 692 486 office visits were included in the study. Compared with the gold standard, the Text-and-ICD NLP System had the highest F1 score for most clinical conditions (range 0.39-0.64). The agreement between the ICD-10 Lookup System and Text-Only NLP System varied (kappa of 0.21-0.81). The prevalence of DR and related conditions ranged from 1.1% for NVG to 17.9% for DME (using the Text-and-ICD NLP System)

关键词： Clinical free-text notes Diabetic retinopathy Electronic health record Natural language processing Prevalence BERT bidirectional encoder representations from transformers DME diabetic macular edema DR diabetic retinopathy EHR electronic health record ICD International Classification of Diseases ICD-10 International Classification of Diseases, 10th Revision ME macular edema MLP Multilayer Perceptron NLP natural language processing NPDR nonproliferative diabetic retinopathy NVG neovascular glaucoma PDR proliferative diabetic retinopathy RD retinal detachment TRD tractional retinal detachment VH vitreous hemorrhage

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Natural language processing: fast forwarding research to the "good stuff" Natural language processing for nutrition

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AMERICAN JOURNAL OF CLINICAL NUTRITION 2023年第3期117卷 449-450页

作者： Lindquist, Joseph M. US Mil Acad Dept Math Sci West Point NY 10996 USA

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TAQS: An Arabic Question Similarity System Using Transfer Learning of BERT With BiLSTM

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IEEE ACCESS 2022年 10卷 91509-91523页

作者： Alshammari, Waad AlHumoud, Sarah Imam Mohammad Ibn Saud Islamic Univ Coll Comp & Informat Sci Dept Comp Sci Riyadh 11652 Saudi Arabia

With the rapid increase of Arabic content on the web comes an increased need for short and accurate answers to queries. Machine question answering has appeared as an important emerging field for progress in natural language processing techniques. Machine learning performance surpasses that of humans in some areas, such as natural language processing and text analysis, especially with large amounts of data. There are two main contributions of this research. First, we propose the Tawasul Arabic question similarity (TAQS) system with four Arabic semantic question similarity models using deep learning techniques. Second, we curated and used an Arabic customer service question-similarity dataset with a 44,404 entries of question-answer pairs, called "Tawasul." For TAQS, first, we use transfer learning to extract the contextualized bidirectional encoder representations from transformers (BERT) embedding with bidirectional long short-term memory (BiLSTM) in two different ways. Specifically, we propose two architectures: the BERT contextual representation with BiLSTM (BERT-BiLSTM) and the hybrid transfer BERT contextual representation with BiLSTM (HT-BERT-BiLSTM). The hybrid transfer representation combines two transfer learning techniques. Second, we fine-tuned two versions of bidirectional encoder representations from transformers for Arabic language (AraBERT). The results show that the HT-BERT-BiLSTM with the features of Layer 12 reaches an accuracy of 94.45%, where the fine-tuning of AraBERTv2 and AraBERTv0.2 achieve 93.10% and 93.90% accuracy, respectively, for the Tawasul dataset. Our proposed TAQS model surpassed the performance of the state-of-the-art BiLSTM with SkipGram by a gain of 43.19% in accuracy.

关键词： Neural networks Semantics Bit error rate Question answering (information retrieval) transformers Natural language processing Deep learning Ranking (statistics) Deep learning Text analysis Encoding Natural language processing question answering question similarity question ranking deep learning neural network bidirectional encoder representations from transformers word embedding

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A Long-Text Classification Method of Chinese News Based on BERT and CNN

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IEEE ACCESS 2022年 10卷 34046-34057页

作者： Chen, Xinying Cong, Peimin Lv, Shuo Dalian Jiaotong Univ Sch Comp & Commun Engn Dalian 116028 Peoples R China

Text Classification is an important research area in natural language processing (NLP) that has received a considerable amount of scholarly attention in recent years. However, real Chinese online news is characterized by long text, a large amount of information and complex structure, which also reduces the accuracy of Chinese long text classification as a result. To improve the accuracy of long text classification of Chinese news, we propose a BERT-based local feature convolutional network (LFCN) model including four novel modules. First, to address the limitation of bidirectional encoder representations from transformers (BERT) on the length of the max input sequence, we propose a named Dynamic LEAD-n (DLn) method to extract short texts within the long text based on the traditional LEAD digest algorithm. In Text-Text encoder (TTE) module, we use BERT pretrained language model to complete the sentence-level feature vector representation of a news text and to capture global features by using the attention mechanism to identify correlated words in text. After that, we propose a CNN-based local feature convolution (LFC) module to capture local features in text, such as key phrases. Finally, the feature vectors generated by the different operations over several different periods are fused and used to predict the category of a news text. Experimental results show that the new method further improves the accuracy of long text classification of Chinese news.

关键词： Bit error rate Task analysis Convolutional neural networks Feature extraction Lead transformers Convolution bidirectional encoder representations from transformers Chinese long text classification convolutional neural network natural language processing representation learning

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Advanced Multi-Task Reinforcement Learning Utilising Task-Adaptive Episodic Memory with Hypernetwork Integration 2

Advanced Multi-Task Reinforcement Learning Utilising Task-Ad...

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Prasanna Kumar, R. Prasanna Kumar, B.G. Sri Ramakrishna Prabhu, A. Upadhyay, Yash Teja, Nunna Vishnu Swamy, Penta Aravinda Amrita Vishwa Vidyapeetham School of Computing Computer Science and Engineering Chennai India

ISBN: (纸本)9798350327533

In deep reinforcement learning, sampling ineffi-ciency is addressed by mimicking human learning which lever-Ages past experiences stored in the hippocampus. Integrating this idea, the proposed approach utilizes a task-conditioned hypernetwork retrieval network with MobileNet for feature extraction from past experiences. Furthermore, BERT was in-Tegrated for advanced natural language understanding, enabling our DRL agents to better interpret and act upon language-based tasks. This approach ensures dynamic adaptability and fosters a more efficient collaboration between retrieval and decision-making processes. Evaluation in MiniGrid demonstrates that the proposed method outperforms strong baselines, showing significant advancements in deep reinforcement learning with natural language processing capabilities. This advancement not only marks progress in sampling efficiency but also opens new avenues for multi-Task learning, paving the way for more intelligent, autonomous AI systems. © 2024 IEEE.

关键词： bidirectional encoder representations from transformers Deep Reinforcement Learning Episodic Memory Hypernetwork Multi-Task Learning

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Natural Language Processing for Automated Classification of Qualitative Data from Interviews of Patients With Cancer

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VALUE IN HEALTH 2022年第12期25卷 1995-2002页

作者： Fang, Chao Markuzon, Natasha Patel, Nikunj Rueda, Juan-David AstraZeneca Oncol Biometr ML AI 760 Winter St Waltham MA 02451 USA AstraZeneca US Med Affairs Gaithersburg MD USA AstraZeneca Oncol Market Access & Pricing Gaithersburg MD USA

Objectives: This study sought to explore the use of novel natural language processing (NLP) methods for classifying unstructured, qualitative textual data from interviews of patients with cancer to identify patient-reported symptoms and impacts on quality of life. Methods: We tested the ability of 4 NLP models to accurately classify text from interview transcripts as "symptom," "quality of life impact" and "other." Interview data sets from patients with hepatocellular carcinoma (HCC) (n = 25), biliary tract cancer (BTC) (n = 23), and gastric cancer (n = 24) were used. Models were cross-validated with transcript subsets designated for training, validation, and testing. Multiclass classification performance of the 4 models was evaluated at paragraph and sentence level using the HCC testing data set and analyzed by the one-versus-rest technique quantified by the receiver operating characteristic area under the curve (ROC AUC) score. Results: NLP models accurately classified multiclass text from patient interviews. The bidirectional encoder representations from transformers model generally outperformed all other models at paragraph and sentence level. The highest predictive performance of the bidirectional encoder representations from transformers model was observed using the HCC data set to train and BTC data set to test (mean ROC AUC, 0.940 [SD 0.028]), with similarly high predictive performance using balanced and imbalanced training data sets from BTC and gastric cancer populations. Conclusions: NLP models were accurate in predicting multiclass classification of text from interviews of patients with cancer, with most surpassing 0.9 ROC AUC at paragraph level. NLP may be a useful tool for scaling up processing of patient interviews in clinical studies and, thus, could serve to facilitate patient input into drug development and improving patient care.

关键词： bidirectional encoder representations from transformers natural language processing patient interviews patient-reported outcomes

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Political Hate Speech Detection and Lexicon Building: A Study in Taiwan

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IEEE ACCESS 2022年 10卷 44337-44346页

作者： Wang, Chih-Chien Day, Min-Yuh Wu, Chun-Lian Natl Taipei Univ Grad Inst Informat Management New Taipei 23741 Taiwan

There is the minimal restriction to users' speech in cyberspace. The Internet provides a space where people can freely present their speech, which puts a Utopian sense of freedom of speech into practice. However, the appearance of hate speech is a significant side effect of online freedom of speech. Some users use hate speech to attack others, making the attacked targets uncomfortable. The proliferation of hate speech poses severe challenges to cyber society. Users may hope that social media platforms and online communities promote anti-hate speech. However, hate speech detection is still a developing technology that requires system developers to create a method to detect unacceptable hate speech while maintaining the online freedom of speech environment. No excellence detection approach has yet been proposed, although some literature has focused on it. The current study proposes an approach to build a political hate speech lexicon and train artificial intelligence classifiers to detect hate speech. Our academic and practical contributions include the collection of a Chinese hate speech dataset, creating a Chinese hate speech lexicon, and developing both a deep learning-based and a lexicon-based approach to detect Chinese hate speech. Although we focus on Chinese hate speech detection, our proposed hate speech detection system and hate speech lexicon development approach can also be used for other languages.

关键词： Hate speech Social networking (online) Blogs Support vector machines Feature extraction Logistics Convolutional neural networks BERT bidirectional encoder representations from transformers deep learning hate speech lexicon N-gram natural language processing TF-IDF

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A Cross-Attention BERT-Based Framework for Continuous Sign Language Recognition

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IEEE SIGNAL PROCESSING LETTERS 2022年 29卷 1818-1822页

作者： Zhou, Zhenxing Tam, Vincent W. L. Lam, Edmund Y. Univ Hong Kong Dept Elect & Elect Engn Hong Kong Peoples R China

Continuous sign language recognition (CSLR) is a challenging task involving various signal processing techniques to infer the sequences of glosses performed by signers. Existing approaches in CSLR typically use multiple input modalities such as the raw video data and the extracted hand images to improve their recognition accuracy. However, the large modality differences make it difficult to define an integrative framework to effectively exchange and combine the knowledge obtained from different modalities such that they can complement each other for improving the framework's robustness against the gesture variations and background noises in CSLR. To address this issue, we propose a novel cross-attention deep learning framework named the CA-SignBERT. This framework utilizes multiple bidirectional encoder representations from transformers (BERT) models to analyze the information from different modalities. Among these BERT models, we introduce a special cross-attention mechanism to ensure an efficient inter-modality knowledge exchange. Besides, an innovative weight control module is proposed to dynamically hybridize their outputs. Experimental results reveal that the CA-SignBERT framework attains state-of-the-art performance in four benchmark CSLR datasets.

关键词： Feature extraction Bit error rate Weight control Data mining Assistive technologies Training Gesture recognition Attention mechanism sign language recognition bidirectional encoder representations from transformers

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A BERT-Based Automatic Scorino Model of Korean Language Learners Essay

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JOURNAL OF INFORMATION PROCESSING SYSTEMS 2022年第2期18卷 282-291页

作者： Lee, Jung Hee Park, Ji Su Shon, Jin Gon Kyung Hee Univ Dept Korean Language Educ Language 2 Seoul South Korea Jeonju Univ Dept Comp Sci & Engn Jeonju South Korea Korea Natl Open Univ Dept Comp Sci Seoul South Korea

This research applies a pre-trained bidirectional encoder representations from transformers (BERT) handwriting recognition model to predict foreign Korean-language learners' writing scores. A corpus of 586 answers to midterm and final exams written by foreign learners at the Intermediate 1 level was acquired and used for pre-training, resulting in consistent performance, even with small datasets. The test data were pre-processed and fine-tuned, and the results were calculated in the form of a score prediction. The difference between the prediction and actual score was then calculated. An accuracy of 95.8% was demonstrated, indicating that the prediction results were strong overall;hence, the tool is suitable for the automatic scoring of Korean written test answers, including grammatical errors, written by foreigners. These results are particularly meaningful in that the data included written language text produced by foreign learners, not native speakers.

关键词： Automatic Writing Scoring bidirectional encoder representations from transformers Korean as a Foreign Language Natural Language Processing

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MDL-CPI: Multi-view deep learning model for compound-protein interaction prediction

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METHODS 2022年 204卷 418-427页

作者： Wei, Lesong Long, Wentao Wei, Leyi Hainan Normal Univ Sch Math & Stat Hainan Peoples R China Univ Tsukuba Dept Comp Sci Tsukuba 3058577 Japan Shandong Univ Sch Software Jinan Peoples R China Shandong Univ Joint SDU NTU Ctr Artificial Intelligence Res C FA Jinan Peoples R China

Elucidating the mechanisms of Compound-Protein Interactions (CPIs) plays an essential role in drug discovery and development. Many computational efforts have been done to accelerate the development of this field. However, the current predictive performance is still not satisfactory, and existing methods consider only protein and compound features, ignoring their interactive information. In this study, we propose a multi-view deep learning method named MDL-CPI for CPI prediction. To sufficiently extract discriminative information, we introduce a hybrid architecture that leverages BERT (bidirectional encoder representations from transformers) and CNN (Convolutional Neural Network) to extract protein features from a sequential perspective, use the GNN (Graph Neural Networks) to extract compound features from a structural perspective, and generate a unified feature space by using AE2 (Autoencoder in Autoencoder Networks) network to learn the interactive information between BERT-CNN and Graph embeddings. Comparative results on benchmark datasets show that our proposed method exhibits better performance compared to existing CPI prediction methods, demonstrating the strong predictive ability of our model. Importantly, we demonstrate that the learned interactive information between compounds and proteins is critical to improve predictive performance. We release our source code and dataset at: https://***/Longwt123/MDL-CPI.

关键词： Compound-protein interaction Multi-view learning Graph Neural Networks bidirectional encoder representations from transformers

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