scholarly journals A clinical specific BERT developed with huge size of Japanese clinical narrative

2020 ◽  
Author(s):  
Yoshimasa Kawazoe ◽  
Daisaku Shibata ◽  
Emiko Shinohara ◽  
Eiji Aramaki ◽  
Kazuhiko Ohe
Keyword(s):  
Clinical Medicine ◽  
The Other ◽  
Language Models ◽  
Clinical Text ◽  
Domain Specific ◽  
Clinical Narrative ◽  
Great Performance ◽  
The University ◽  
Entire Dataset ◽  
Large Corpus

Generalized language models that pre-trained with a large corpus have achieved great performance on natural language tasks. While many pre-trained transformers for English are published, few models are available for Japanese text, especially in clinical medicine. In this work, we demonstrate a development of a clinical specific BERT model with a huge size of Japanese clinical narrative and evaluated it on the NTCIR-13 MedWeb that has pseudo-Twitter messages about medical concerns with eight labels. Approximately 120 millions of clinical text stored at the University of Tokyo Hospital were used as dataset. The BERT-base was pre-trained with the entire dataset and a vocabulary including 25,000 tokens. The pre-training was almost saturated at about 4 epochs, and the accuracies of Masked LM and Next Sentence Prediction were 0.773 and 0.975, respectively. The developed BERT tends to show higher performances on the MedWeb task than the other nonspecific BERTs, however, no significant differences were found. The advantage of training on domain-specific texts may become apparent in the more complex tasks on actual clinical text, and such corpus for the evaluation is required to be developed.

scholarly journals A clinical specific BERT developed using a huge Japanese clinical text corpus

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0259763
Author(s):  
Yoshimasa Kawazoe ◽  
Daisaku Shibata ◽  
Emiko Shinohara ◽  
Eiji Aramaki ◽  
Kazuhiko Ohe
Keyword(s):  
Natural Language ◽  
Clinical Medicine ◽  
The Other ◽  
Language Models ◽  
Huge Amount ◽  
Clinical Text ◽  
Great Performance ◽  
The University ◽  
Entire Dataset ◽  
Large Corpus

Generalized language models that are pre-trained with a large corpus have achieved great performance on natural language tasks. While many pre-trained transformers for English are published, few models are available for Japanese text, especially in clinical medicine. In this work, we demonstrate the development of a clinical specific BERT model with a huge amount of Japanese clinical text and evaluate it on the NTCIR-13 MedWeb that has fake Twitter messages regarding medical concerns with eight labels. Approximately 120 million clinical texts stored at the University of Tokyo Hospital were used as our dataset. The BERT-base was pre-trained using the entire dataset and a vocabulary including 25,000 tokens. The pre-training was almost saturated at about 4 epochs, and the accuracies of Masked-LM and Next Sentence Prediction were 0.773 and 0.975, respectively. The developed BERT did not show significantly higher performance on the MedWeb task than the other BERT models that were pre-trained with Japanese Wikipedia text. The advantage of pre-training on clinical text may become apparent in more complex tasks on actual clinical text, and such an evaluation set needs to be developed.

Industrial Technology Graduates’ Acquired Skills and Competencies from the University

2019 ◽  
Vol 5 (3) ◽  
pp. 189
Author(s):  
Amado C Gequinto ◽  
Do Mads
Keyword(s):  
Social Media ◽  
Text Messaging ◽  
Global Market ◽  
The Other ◽  
Global Markets ◽  
Industrial Technology ◽  
The University ◽  
Descriptive Method

Skills and competencies are highly regarded in todays global market. Different agencies specifically those seeking for  technologists, technicians, and engineers, have stressed out that skills and competencies as major components  for individual workers.  This aimed to determine  the relevance and appropriateness of acquired skills and competencies by industrial technology graduates, and determine the extent of use of skills and competencies in the current employment. Review of related literatures and studies have been considered in the realization, understanding, analysis, and interpretation of this research exploration. A descriptive method of research was used with 78 graduates from 2015-2016 and 117 graduates from 2016-2017, who participated in the study survey process. The BatStateU Standardized Questionnaire was used to gather data. A brief interview and talk during the visit of alumni in the university was also considered, as well as the other means of social media like email, facebook, messenger, and text messaging.   Results show that skills and competecnices acquired by industrial technology graduates are all relevant and appropriate.  The study also found that there is some to great extent use of acquired skills and competencies to their current employment. The study implies that the acquired skills and competencies from the university significantly provided the graduates the opportunities ins the national and global markets and industries.

Oxford Assess and Progress: Clinical Medicine

2019 ◽  
Keyword(s):  
Correct Answer ◽  
Clinical Medicine ◽  
The Other ◽  
The Third

Maximise your exam success with this essential revision guide. The third edition of Oxford Assess and Progress: Clinical Medicine features over 550 Single Best Answer questions. Packed with questions written by practicing clinicians and educators, this revision tool is an authoritative guide on core clinical topics and professional themes. Each question is accompanied by extensive feedback which explains not only the rationale of the correct answer, but why the other options are incorrect. Further reading resources and cross-references to the Oxford Handbook of Clinical Medicine have been fully updated to expand your revision further. Progess to exam success with the third edition of Oxford Assess and Progress: Clinical Medicine.

scholarly journals Is the Letter ‘t’ in the Word ‘gourmet’? Disruption in Task-Evoked Connectivity Networks in Adults with Impaired Literacy Skills

NeuroSci ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 75-94
Author(s):  
Kulpreet Cheema ◽  
William E. Hodgetts ◽  
Jacqueline Cummine
Keyword(s):  
Developmental Disorders ◽  
Brain Networks ◽  
Literacy Skills ◽  
Resting State Networks ◽  
Skilled Readers ◽  
University Of Alberta ◽  
Domain Specific ◽  
Probe Task ◽  
The University ◽  
General Networks

Much work has been done to characterize domain-specific brain networks associated with reading, but very little work has been done with respect to spelling. Our aim was to characterize domain-specific spelling networks (SpNs) and domain-general resting state networks (RSNs) in adults with and without literacy impairments. Skilled and impaired adults were recruited from the University of Alberta. Participants completed three conditions of an in-scanner spelling task called a letter probe task (LPT). We found highly connected SpNs for both groups of individuals, albeit comparatively more connections for skilled (50) vs. impaired (43) readers. Notably, the SpNs did not correlate with spelling behaviour for either group. We also found relationships between SpNs and RSNs for both groups of individuals, this time with comparatively fewer connections for skilled (36) vs. impaired (53) readers. Finally, the RSNs did predict spelling performance in a limited manner for the skilled readers. These results advance our understanding of brain networks associated with spelling and add to the growing body of literature that describes the important and intricate connections between domain-specific networks and domain-general networks (i.e., resting states) in individuals with and without developmental disorders.

scholarly journals Collecting specialty-related medical terms: Development and evaluation of a resource for Spanish

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Pilar López-Úbeda ◽  
Alexandra Pomares-Quimbaya ◽  
Manuel Carlos Díaz-Galiano ◽  
Stefan Schulz
Keyword(s):  
Language Processing ◽  
Classification Problem ◽  
Snomed Ct ◽  
Language Resources ◽  
Clinical Specialty ◽  
Controlled Vocabularies ◽  
Clinical Text ◽  
Domain Specific ◽  
Medical Terms ◽  
Core Vocabulary

Abstract Background Controlled vocabularies are fundamental resources for information extraction from clinical texts using natural language processing (NLP). Standard language resources available in the healthcare domain such as the UMLS metathesaurus or SNOMED CT are widely used for this purpose, but with limitations such as lexical ambiguity of clinical terms. However, most of them are unambiguous within text limited to a given clinical specialty. This is one rationale besides others to classify clinical text by the clinical specialty to which they belong. Results This paper addresses this limitation by proposing and applying a method that automatically extracts Spanish medical terms classified and weighted per sub-domain, using Spanish MEDLINE titles and abstracts as input. The hypothesis is biomedical NLP tasks benefit from collections of domain terms that are specific to clinical subdomains. We use PubMed queries that generate sub-domain specific corpora from Spanish titles and abstracts, from which token n-grams are collected and metrics of relevance, discriminatory power, and broadness per sub-domain are computed. The generated term set, called Spanish core vocabulary about clinical specialties (SCOVACLIS), was made available to the scientific community and used in a text classification problem obtaining improvements of 6 percentage points in the F-measure compared to the baseline using Multilayer Perceptron, thus demonstrating the hypothesis that a specialized term set improves NLP tasks. Conclusion The creation and validation of SCOVACLIS support the hypothesis that specific term sets reduce the level of ambiguity when compared to a specialty-independent and broad-scope vocabulary.

scholarly journals An Interesting and Productive Research and Clinical Career

2020 ◽  
Vol 39 (3) ◽  
pp. 182-188
Author(s):  
Samuel M. Cohen
Keyword(s):  
High School ◽  
Clinical Medicine ◽  
Medical Center ◽  
Basic Research ◽  
Nebraska Medical ◽  
Good Fortune ◽  
University Of Wisconsin ◽  
Productive Research ◽  
The University

To begin, I wish to thank the Academy of Toxicological Sciences for bestowing this honor on me. I have had a rewarding career in basic research and clinical medicine, beginning with research in high school and always planning on becoming a physician. I have had the good fortune of having outstanding mentors, wonderful parents, and a supportive and intuitive wife and family. This article provides a brief overview of some of the events of my career and individuals who have played a major role, beginning with the M.D./Ph.D. program at the University of Wisconsin, pathology residency and faculty at St. Vincent Hospital, Worcester, Massachusetts, a year as visiting professor at Nagoya City University, and my career at the University of Nebraska Medical Center since 1981. This could not have happened without the strong input and support from these individuals, the numerous students, residents and fellows with whom I have learned so much, and the more than 500 terrific collaborators.

scholarly journals Higher Education and Sustainable Development Goals (SDG)—Potential Contribution of the Undergraduate Courses of the School of Social Sciences of the University of Évora

2021 ◽  
Vol 13 (4) ◽  
pp. 1828
Author(s):  
Elisa Chaleta ◽  
Margarida Saraiva ◽  
Fátima Leal ◽  
Isabel Fialho ◽  
António Borralho
Keyword(s):  
Social Sciences ◽  
Economic Growth ◽  
Sustainable Development ◽  
Sustainable Development Goals ◽  
Quality Education ◽  
The Other ◽  
Potential Contribution ◽  
Decent Work ◽  
Development Goals ◽  
The University

In this work we analyzed the mapping of Sustainable Development Goals in the curricular units of the undergraduate courses of the School of Social Sciences at the University of Évora. Of a total of 449 curricular units, only 374 had students enrolled in 2020/2021. The data presented refer to the 187 course units that had Sustainable Development Goals in addition to SDG4 (Quality Education) assigned to all the course units. Considering the set of curricular units, the results showed that the most mentioned objectives were those related to Gender Equality (SDG 5), Reduced Inequalities (SDG 10), Decent Work and Economic Growth (SDG 8) and Peace, Justice and Strong Institutions (SDG 16). Regarding the differences between the departments, which are also distinct scientific areas, we have observed that the Departments of Economics and Management had more objectives related to labor and economic growth, while the other departments mentioned more objectives related to inequalities, gender or other.

scholarly journals Development of Speech Recognition Systems in Emergency Call Centers

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 634
Author(s):  
Alakbar Valizada ◽  
Natavan Akhundova ◽  
Samir Rustamov
Keyword(s):  
Speech Recognition ◽  
Markov Model ◽  
Hidden Markov ◽  
Call Centers ◽  
The Other ◽  
Language Models ◽  
Emergency Call ◽  
Acoustic Model ◽  
Different Types ◽  
Recognition Systems

In this paper, various methodologies of acoustic and language models, as well as labeling methods for automatic speech recognition for spoken dialogues in emergency call centers were investigated and comparatively analyzed. Because of the fact that dialogue speech in call centers has specific context and noisy, emotional environments, available speech recognition systems show poor performance. Therefore, in order to accurately recognize dialogue speeches, the main modules of speech recognition systems—language models and acoustic training methodologies—as well as symmetric data labeling approaches have been investigated and analyzed. To find an effective acoustic model for dialogue data, different types of Gaussian Mixture Model/Hidden Markov Model (GMM/HMM) and Deep Neural Network/Hidden Markov Model (DNN/HMM) methodologies were trained and compared. Additionally, effective language models for dialogue systems were defined based on extrinsic and intrinsic methods. Lastly, our suggested data labeling approaches with spelling correction are compared with common labeling methods resulting in outperforming the other methods with a notable percentage. Based on the results of the experiments, we determined that DNN/HMM for an acoustic model, trigram with Kneser–Ney discounting for a language model and using spelling correction before training data for a labeling method are effective configurations for dialogue speech recognition in emergency call centers. It should be noted that this research was conducted with two different types of datasets collected from emergency calls: the Dialogue dataset (27 h), which encapsulates call agents’ speech, and the Summary dataset (53 h), which contains voiced summaries of those dialogues describing emergency cases. Even though the speech taken from the emergency call center is in the Azerbaijani language, which belongs to the Turkic group of languages, our approaches are not tightly connected to specific language features. Hence, it is anticipated that suggested approaches can be applied to the other languages of the same group.

James Bertram Collip, 1892-1965

1973 ◽  
Vol 19 ◽  
pp. 234-267 ◽  
Keyword(s):  
Family Life ◽  
Clinical Medicine ◽  
National Research Council ◽  
Pituitary Hormones ◽  
University Of Alberta ◽  
University Of Toronto ◽  
Mcgill University ◽  
Medical Importance ◽  
The University ◽  
Happy Family

James Bertram Collip was a pioneer in endocrine research, especially in its biochemical aspects. Following an excellent training in biochemistry under Professor A. B. Macallum, F.R.S., at the University of Toronto, he spent thirteen years at the University of Alberta in Edmonton. There was a momentous year at the University of Toronto about midway through the Edmonton period; this coincided with the discovery of insulin by Sir Frederick G. Banting, F.R.S., and Professor Charles S. Best, F.R.S., and the experience altered the course of his career. Henceforth, Professor Collip’s life was dominated by an urge to discover hormones that would be useful in clinical medicine. Success attended these efforts, first in the isolation of the parthyroid hormone, called parathormone, while he was at the University of Alberta and later in the identification of placental and pituitary hormones during particularly fruitful years at McGill University. There were other important facets to Professor Collip’s career. These included the training of young scientists, many of whom subsequently came to occupy positions of responsibility, work with the National Research Council of Canada, and in his latter years an important contribution as Dean of the Faculty of Medicine, University of Western Ontario. In addition to a life of fulfilment through accomplishments of scientific and medical importance, Professor Collip’s career was enriched by a happy family life and by the friendship of a host of individuals who were attracted to his brilliance as a scientist and his warm personality.

scholarly journals 4. On some Congenital Deformities of the Human Skull

1866 ◽  
Vol 5 ◽  
pp. 444-449
Author(s):  
Wm. Turner
Keyword(s):  
Natural History ◽  
Natural History Museum ◽  
The Other ◽  
History Museum ◽  
Human Skull ◽  
Congenital Deformities ◽  
Premature Closure ◽  
University Of Edinburgh ◽  
The University ◽  
Living Person

1st, Scaphocephalus.—After making reference to his previous papers, more especially to that in which he had described several specimens of the scaphocephalic skull, in which he had discussed the influence exercised on the production of deformities of the cranium, by a premature closure or obliteration of the sutures, and to the recent memoirs of Professor von Düben of Stockholm,† and Dr John Thurnam, the author proceeded to relate two additional cases of scaphocephalus to those he had already recorded. He had met with one of these in the head of a living person, the other in a skull in the Natural History Museum of the University of Edinburgh.

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