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Longitudinal Analysis of Discussion Topics in an Online Breast Cancer Community using Convolutional Neural Networks [article]

Shaodian Zhang, Edouard Grave, Elizabeth Sklar, Noemie Elhadad
2016 arXiv   pre-print
In this paper, we provide a multi-class schema, an annotated dataset, and supervised classifiers based on convolutional neural network (CNN) and other models for the task of classifying discussion topics  ...  We apply the CNN classifier to the most popular breast cancer online community, and carry out a longitudinal analysis to show topic distributions and topic changes throughout members' participation.  ...  ; (3) dimensionality reduction were carried out by doing Named Entity Recognition (using Stanford NER (Finkel et al., 2005) ) to recognize Person, Location, Organization names as well as special tokens  ... 
arXiv:1603.08458v3 fatcat:gojwpxdomngonlenxeguvoko7i

"Construction of Digestive System Tumor Knowledge Graph based on Chinese Electronic Medical Records" (Preprint)

Xiaolei Xiu, Qing Qian, Sizhu Wu
2020 JMIR Medical Informatics  
Then, this research built a knowledge graph schema containing 7 classes and 16 kinds of semantic relationships and accomplished the DSTKG by knowledge extraction, named entity linking, and drawing the  ...  Compared with other Chinese tumor knowledge graphs, the DSTKG can represent more granular entities, properties, and semantic relationships.  ...  Acknowledgments The authors would like to thank the CCKS 2018 CNER challenge organizers for providing the data source.  ... 
doi:10.2196/18287 pmid:33026359 fatcat:64yoa24625c43icvqwba3s764u

Improved Biomedical Word Embeddings in the Transformer Era [article]

Jiho Noh, Ramakanth Kavuluru
2020 arXiv   pre-print
They are leveraged in downstream tasks using various neural architectures that are designed to optimize task-specific objectives that might further tune such embeddings.  ...  We provide our code and embeddings for public use for downstream applications and research endeavors:  ...  Especially, as of now, BioNLP almost exclusively relies on DNNs to obtain state-of-the-art results in named entity recognition (NER), relation extraction (RE), and entity/concept linking or normalization  ... 
arXiv:2012.11808v2 fatcat:bis5kguhnjcsfodwhac5hgzms4

Longitudinal analysis of discussion topics in an online breast cancer community using convolutional neural networks

Shaodian Zhang, Edouard Grave, Elizabeth Sklar, Noémie Elhadad
2017 Journal of Biomedical Informatics  
; (3) dimensionality reduction were carried out by doing Named Entity Recognition (using Stanford NER [35] ) to recognize Person, Location, Organization names as well as special tokens such as number,  ...  To train for the annotations, coders practiced annotating the 439 sentences (37 posts) referred to above using the annotation guidelines.  ... 
doi:10.1016/j.jbi.2017.03.012 pmid:28323113 pmcid:PMC5708301 fatcat:nhyqmt2lpjfmpll3oezljg5rti

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.  ...  Recently, however, newer neural network and deep learning approaches to Natural Language Processing (NLP) have made considerable advances, outperforming traditional statistical and rule-based systems on  ...  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

Pre-trained Language Models in Biomedical Domain: A Systematic Survey [article]

Benyou Wang, Qianqian Xie, Jiahuan Pei, Prayag Tiwari, Zhao Li, Jie fu
2021 arXiv   pre-print
., biomedical text, electronic health records, protein, and DNA sequences for various biomedical tasks.  ...  Pre-trained language models (PLMs) have been the de facto paradigm for most natural language processing (NLP) tasks.  ...  biomedical named entity recognition.  ... 
arXiv:2110.05006v2 fatcat:aykwfhgi4jgmfovissgdvknny4

What can natural language processing do for clinical decision support?

Dina Demner-Fushman, Wendy W. Chapman, Clement J. McDonald
2009 Journal of Biomedical Informatics  
This review primarily focuses on the recently renewed interest in development of fundamental NLP methods and advances in the NLP systems for CDS.  ...  The current solutions to challenges posed by distinct sublanguages, intended user groups, and support goals are discussed. Published by Elsevier Inc.  ...  We also thank Kevin Bretonnel Cohen for inspiration and valuable comments, and the anonymous reviewers for the detailed analysis and helpful comments.  ... 
doi:10.1016/j.jbi.2009.08.007 pmid:19683066 pmcid:PMC2757540 fatcat:tcgi3ra4rzbtzf2z7ayys3ptny

Automated Machine Learning for Healthcare and Clinical Notes Analysis

Akram Mustafa, Mostafa Rahimi Azghadi
2021 Computers  
To that end, we first introduce the AutoML technology and review its various tools and techniques.  ...  For this to happen, a promising method is AutoML for clinical notes analysis, which is an unexplored research area representing a gap in ML research.  ...  Neural networks can be used for multi-label classification, too. In [18] , an open-source tool named MEKA [142] was used for neural network-based multi-label classification.  ... 
doi:10.3390/computers10020024 fatcat:sojvfgq255f3zeccsunzwu4ve4

A survey on data‐efficient algorithms in big data era

Amina Adadi
2021 Journal of Big Data  
This has triggered a serious debate in both the industrial and academic communities calling for more data-efficient models that harness the power of artificial learners while achieving good results with  ...  Unfortunately, many application domains do not have access to big data because acquiring data involves a process that is expensive or time-consuming.  ...  The proposed framework is applicable to various types of neural architectures, including CNN for sentiment analysis, and RNN for named entity recognition.  ... 
doi:10.1186/s40537-021-00419-9 fatcat:v4uahsvhlzdldlxqf24bshmja4

DeepTag: inferring all-cause diagnoses from clinical notes in under-resourced medical domain [article]

Allen Nie, Ashley Zehnder, Rodney L. Page, Arturo L. Pineda, Manuel A. Rivas, Carlos D. Bustamante, James Zou
2018 arXiv   pre-print
In order to reduce the coding burden for veterinary clinical practice and aid translational research, we have developed a deep learning algorithm, DeepTag, which automatically infers diagnostic codes from  ...  Large scale veterinary clinical records can become a powerful resource for patient care and research.  ...  Acknowledgements We would like to acknowledge the help of Devin Johnsen for her help in annotating the private practice records used in this work.  ... 
arXiv:1806.10722v2 fatcat:yim4g2nxbrbdpolmixkp6rcxiu

The essential moral self

Nina Strohminger, Shaun Nichols
2014 Cognition  
To determine categories for analysis, five coders blind to the aims of the study rated which of the four categories each of the 62 traits fit into.  ...  Subjects were asked to imagine that it's possible for a soul to leave one body and inhabit another one, and that this had just happened to a man named Jim, whose soul now inhabited a new body with none  ...  After the operation, all the right neural connections between the old brain and the replacement brain tissue have been made.  ... 
doi:10.1016/j.cognition.2013.12.005 pmid:24503450 fatcat:zsvmisfvbvdy7cjoorsjj2r6ky

A Review on the Role of Machine Learning in Enabling IoT Based Healthcare Applications

Hemantha Krishna Bharadwaj, Aayush Agarwal, Vinay Chamola, Naga Rajiv Lakkaniga, Vikas Hassija, Mohsen Guizani, Biplab Sikdar
2021 IEEE Access  
In healthcare, practical use of a model requires it to be highly accurate and to have ample measures against security attacks.  ...  This paper aims to serve both as a compilation as well as a review of the various state of the art applications of ML algorithms currently being integrated with H-IoT.  ...  [31] demonstrated the use of k-means clustering on MRI images for information deduction to speed up the detection of brain tumors. VOLUME 9, 2021 H.  ... 
doi:10.1109/access.2021.3059858 fatcat:gwdku6me2fhzbfwf7agtt5vmse

Comparative Analysis of Different Machine Learning Classifiers for the Prediction of Chronic Diseases [chapter]

Rajesh Singh, Anita Gehlot, Dharam Buddhi
2022 Comparative Analysis of Different Machine Learning Classifiers for the Prediction of Chronic Diseases  
Precise diagnosis of these diseases on time is very significant for maintaining a healthy life.  ...  A comparative study of different machine learning classifiers for chronic disease prediction viz Heart Disease & Diabetes Disease is done in this paper.  ...  However, still further research is needed to overcome the challenges and making the Stirling engine commercially viable and cost effective. for multiple applications.  ... 
doi:10.13052/rp-9788770227667 fatcat:da47mjbbyzfwnbpde7rgbrlppe

Abstracts Presented at the Thirty–Fourth Annual International Neuropsychological Society Conference, February 1–4, 2006, Boston, Massachussetts, USA

2006 Journal of the International Neuropsychological Society  
Results : Using ANCOVA, TOMM scores accounted for a significant amount of variance in test performance on Digit Span and all subtests of the CVLT-II (η 2 ranged from .133 to .248), but not Trails A or  ...  With the exception of CVLT-II recognition, SCZ and PMI groups did not significantly differ on neuropsychological test performance.  ...  Mood and anterograde memory were assessed before and after the end of treatment. Recall and recognition of retrograde stimuli were assessed two days following the end of treatment.  ... 
doi:10.1017/s1355617706069918 fatcat:cy3cbwlgrvccfnn6w7inplceni

Cellular, Wide-Area, and Non-Terrestrial IoT: A Survey on 5G Advances and the Road Towards 6G [article]

Mojtaba Vaezi, Amin Azari, Saeed R. Khosravirad, Mahyar Shirvanimoghaddam, M. Mahdi Azari, Danai Chasaki, Petar Popovski
2021 arXiv   pre-print
The next wave of wireless technologies is proliferating in connecting things among themselves as well as to humans.  ...  The ultimate goal is to extract insights from this data and develop solutions that improve quality of life and generate new revenue.  ...  , to name a few.  ... 
arXiv:2107.03059v1 fatcat:i7rxlipsd5eojgpx3x5yxioozq
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