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A stacked sequential learning method for investigator name recognition from web-based medical articles

Xiaoli Zhang, Jie Zou, Daniel X. Le, George Thoma, Laurence Likforman-Sulem, Gady Agam
2010 Document Recognition and Retrieval XVII  
In this paper, we present an SVM-based stacked sequential learning method in a novel applicationrecognizing named entities such as the first and last names of investigators from online medical journal  ...  We apply this method to tag words in text paragraphs containing investigator names, and demonstrate that stacked sequential learning improves the performance of a nonsequential base learner such as an  ...  It may be noted that stacked sequential learning can have more than two levels with each level using a different learner.  ... 
doi:10.1117/12.839141 dblp:conf/drr/ZhangZLT10 fatcat:nkoz2ifningmxkzt23pu2uvocq

Technologies Supporting Artificial Intelligence and Robotics Application Development

Yinong Chen, Gennaro De Luca
2021 Journal of Artificial Intelligence and Technology  
In this inaugural issue, we first introduce a few key technologies and platforms supporting the third-generation AI and robotics application development based on stacks of technologies and platforms.  ...  Artificial intelligence (AI) and robotics have gone through three generations of development, from Turing test, logic theory machine, to expert system and self-driving car.  ...  recognition, medical diagnosis, and data mining.  ... 
doi:10.37965/jait.2020.0065 fatcat:wogzcbki6bh6tpuehndt4mx224

A Neural Multi-Task Learning Framework to Jointly Model Medical Named Entity Recognition and Normalization [article]

Sendong Zhao, Ting Liu, Sicheng Zhao, Fei Wang
2018 arXiv   pre-print
State-of-the-art studies have demonstrated the superiority of joint modelling over pipeline implementation for medical named entity recognition and normalization due to the mutual benefits between the  ...  On one hand, our method benefits from the general representations of both tasks provided by multi-task learning.  ...  Acknowledgments We thank anonymous reviewers for their insightful comments and suggestions. This work is supported by NSF IIS-1716432 and IIS-1750326.  ... 
arXiv:1812.06081v1 fatcat:kbgrukywhfh67npm3gzx6z23zu

A Neural Multi-Task Learning Framework to Jointly Model Medical Named Entity Recognition and Normalization

Sendong Zhao, Ting Liu, Sicheng Zhao, Fei Wang
2019 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
State-of-the-art studies have demonstrated the superiority of joint modeling over pipeline implementation for medical named entity recognition and normalization due to the mutual benefits between the two  ...  On one hand, our method benefits from the general representations of both tasks provided by multi-task learning.  ...  We experiment with a multi-task learning architecture based on stacked Bi-LSTM, CNNs and CRF.  ... 
doi:10.1609/aaai.v33i01.3301817 fatcat:dfdtkrzmc5c63hvbt67cuatlka

Front Matter: Volume 7534

Proceedings of SPIE, Laurence Likforman-Sulem, Gady Agam
2010 Document Recognition and Retrieval XVII  
The last two digits indicate publication order within the volume using a Base 36 numbering system employing both numerals and letters.  ...  SPIE uses a six-digit CID article numbering system in which: The first four digits correspond to the SPIE volume number.  ...  Morin, LINA, CNRS, Univ. de Nantes (France) 7534 04 A stacked sequential learning method for investigator name recognition from web-based medical articles [7534-03] X. Zhang, J. Zou, D. X.  ... 
doi:10.1117/12.852295 fatcat:o52uohv42vc5lpfx3b7vfzlicm

Neural Natural Language Processing for Unstructured Data in Electronic Health Records: a Review [article]

Irene Li, Jessica Pan, Jeremy Goldwasser, Neha Verma, Wai Pan Wong, Muhammed Yavuz Nuzumlalı, Benjamin Rosand, Yixin Li, Matthew Zhang, David Chang, R. Andrew Taylor, Harlan M. Krumholz (+1 others)
2021 arXiv   pre-print
In this survey paper, we summarize current neural NLP methods for EHR applications.  ...  We focus on a broad scope of tasks, namely, classification and prediction, word embeddings, extraction, generation, and other topics such as question answering, phenotyping, knowledge graphs, medical dialogue  ...  Named Entity Recognition Named Entity Recognition (NER) is the task of determining whether tokens or spans in a text correspond to certain "named entities" of interest, such as medications and diseases  ... 
arXiv:2107.02975v1 fatcat:nayhw7gadfdzrovycdkvzy75pi

A Survey on Crime Detection And Prediction Techniques

Vinod Gendre
2022 International Journal for Research in Applied Science and Engineering Technology  
A productive wrongdoing forecast framework speeds up the method involved with addressing violations..  ...  In present days sequential criminal cases quickly happen so it is a provoking assignment to anticipate future wrongdoing precisely with better execution.  ...  absent in account; web deal implies selling counterfeit things; protection extortion implies counterfeit protection guaranteed for vehicle harm, medical services costs and other; Visa misrepresentation  ... 
doi:10.22214/ijraset.2022.39785 fatcat:nfj2xb7hifcuhhjsswjkm642re

Learning for Biomedical Information Extraction: Methodological Review of Recent Advances [article]

Feifan Liu, Jinying Chen, Abhyuday Jagannatha, Hong Yu
2016 arXiv   pre-print
Unlike existing reviews covering a holistic view on BioIE, this review focuses on mainly recent advances in learning based approaches, by systematically summarizing them into different aspects of methodological  ...  In addition, we dive into open information extraction and deep learning, two emerging and influential techniques and envision next generation of BioIE.  ...  One popular technique from deep learning is word embedding, which have been widely used in biomedical named entity recognition [46, 172] , medical synonym extraction [173] , medical semantics modeling  ... 
arXiv:1606.07993v1 fatcat:7d5om7zxxzhoviiriasrfwg3xi

An efficient modular framework for automatic LIONC classification of MedIMG using unified medical language

Surbhi Bhatia, Mohammed Alojail, Sudhakar Sengan, Pankaj Dadheech
2022 Frontiers in Public Health  
For Medical Images (MedIMG), we provide a methodology for learning an effective holistic representation (handwritten word pictures as well as radiology reports).  ...  to Predictions of Named Entity Recognition utilising RNN + LSTM + GRM that perform admirably in every trainee for every input purpose.  ...  Apart from trying to implement and obtain results for clinical testing and MedIMG, this article refers to the study of health data, the innovation of using DL methods for obtaining large-scale labelled  ... 
doi:10.3389/fpubh.2022.926229 fatcat:uxj2vdqu6neplmipyhvjtt5yyq

Robust Named Entity Recognition in Idiosyncratic Domains [article]

Sebastian Arnold, Felix A. Gers, Torsten Kilias, Alexander Löser
2016 arXiv   pre-print
We propose a generic and robust approach for high-recall named entity recognition.  ...  Our approach is based on deep contextual sequence learning and utilizes stacked bidirectional LSTM networks.  ...  Acknowledgements Our work is funded by the Federal Ministry of Economic Affairs and Energy (BMWi) under grant agreement 01MD15010B (Project: Smart Data Web).  ... 
arXiv:1608.06757v1 fatcat:evvglqwqfnczzixaaaake6fzdi

Boosting-based ensemble learning with penalty profiles for automatic Thai unknown word recognition

Jakkrit TeCho, Cholwich Nattee, Thanaruk Theeramunkong
2012 Computers and Mathematics with Applications  
This paper proposes a method to improve boosting-based ensemble learning with penalty profiles via an application of automatic unknown word recognition in Thai language.  ...  Treating a sequential problem as a non-sequential problem, the unknown word recognition is required to include a process to rank a set of generated candidates for a potential unknown word position.  ...  In the ML-based approach, unknown word recognition can be viewed as a process to detect new compound words in a text using a supervised learning algorithm with features based on statistics from a collected  ... 
doi:10.1016/j.camwa.2011.11.062 fatcat:k3qgv6us4bf3pli6donf32esty

Named Entity Recognition in Biomedical Domain: A Survey

T. M., D. Manjula, Shruthi Shridhar
2019 International Journal of Computer Applications  
Named Entity Recognition (NER) is one of the major tasks in Natural Language Processing (NLP). NER has been an active area of research for the past twenty years.  ...  It is a subtask of information extraction, where the structured text is extracted from unstructured text.  ...  CONCLUSION This paper provides a review of Named Entity Recognition methods in the biomedical field.  ... 
doi:10.5120/ijca2019918469 fatcat:n2cumq3lpjgqblf64otnoxal64

MOSQUITO-NET: A deep learning based CADx system for malaria diagnosis along with model interpretation using GradCam and class activation maps [article]

Aayush Kumar, Sanat B Singh, Suresh Chandra Satapathy, Minakhi Rout
2020 arXiv   pre-print
Therefore, it can be wildly preferred for diagnosis in remote and countryside areas where there is a lack of medical facilities.  ...  State of the art Computer-aided diagnostic techniques based on deep learning algorithms such as CNNs, with end to end feature extraction and classification, have widely contributed to various image recognition  ...  We propose a sequential convnet (CNN) for the computer aided diagnosis system for malaria classification.  ... 
arXiv:2006.10547v2 fatcat:j2ncpuckdjbhlb5p6hegqtvg3u

Deep Learning for Computer Vision: A Brief Review

Athanasios Voulodimos, Nikolaos Doulamis, Anastasios Doulamis, Eftychios Protopapadakis
2018 Computational Intelligence and Neuroscience  
Finally, a brief overview is given of future directions in designing deep learning schemes for computer vision problems and the challenges involved therein.  ...  Over the last years deep learning methods have been shown to outperform previous state-of-the-art machine learning techniques in several fields, with computer vision being one of the most prominent cases  ...  Greek national funds through the action titled "Reinforcement of Postdoctoral Researchers," in the framework of the Operational Programme "Human Resources Development Program, Education and Lifelong Learning  ... 
doi:10.1155/2018/7068349 pmid:29487619 pmcid:PMC5816885 fatcat:yeawpj32onfutegmkqpx4p6tsa

Deep Learning Application Pros And Cons Over Algorithm

Ata Jahangir Moshayedi, Atanu Shuvam Roy, Amin Kolahdooz, Yang Shuxin
2022 EAI Endorsed Transactions on AI and Robotics  
has changed the field of application, such as voice recognition, chat robots, search technology and vision; and (3) deep learning can have a significant impact on the application domain and benefit from  ...  This review provides a general overview of a new concept and the growing benefits and popularity of deep learning, which can help researchers and students interested in deep learning methods.  ...  But DL has been in use for a longer period in digital games. Digital game-based learning or DGBL is an old learning method to learn about player goals and playing patterns.  ... 
doi:10.4108/airo.v1i.19 fatcat:wmfb5cjayngmzdrv6git4o3pce
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