scholarly journals COVID term: a bilingual terminology for COVID-19

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Hetong Ma ◽  
Liu Shen ◽  
Haixia Sun ◽  
Zidu Xu ◽  
Li Hou ◽  
...  
Keyword(s):  
Model Building ◽  
Scientific Discovery ◽  
Living Organism ◽  
Treatment Technique ◽  
Health Providers ◽  
Anatomic Site ◽  
Source Selection ◽  
Term Extraction ◽  
Schema Design ◽  
Psychological Assistance

Abstract Background The coronavirus disease (COVID-19), a pneumonia caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has shown its destructiveness with more than one million confirmed cases and dozens of thousands of death, which is highly contagious and still spreading globally. World-wide studies have been conducted aiming to understand the COVID-19 mechanism, transmission, clinical features, etc. A cross-language terminology of COVID-19 is essential for improving knowledge sharing and scientific discovery dissemination. Methods We developed a bilingual terminology of COVID-19 named COVID Term with mapping Chinese and English terms. The terminology was constructed as follows: (1) Classification schema design; (2) Concept representation model building; (3) Term source selection and term extraction; (4) Hierarchical structure construction; (5) Quality control (6) Web service. We built open access for the terminology, providing search, browse, and download services. Results The proposed COVID Term include 10 categories: disease, anatomic site, clinical manifestation, demographic and socioeconomic characteristics, living organism, qualifiers, psychological assistance, medical equipment, instruments and materials, epidemic prevention and control, diagnosis and treatment technique respectively. In total, COVID Terms covered 464 concepts with 724 Chinese terms and 887 English terms. All terms are openly available online (COVID Term URL: http://covidterm.imicams.ac.cn). Conclusions COVID Term is a bilingual terminology focused on COVID-19, the epidemic pneumonia with a high risk of infection around the world. It will provide updated bilingual terms of the disease to help health providers and medical professionals retrieve and exchange information and knowledge in multiple languages. COVID Term was released in machine-readable formats (e.g., XML and JSON), which would contribute to the information retrieval, machine translation and advanced intelligent techniques application.

scholarly journals COVID Term: A Bilingual Terminology for COVID-19

2020 ◽  
Author(s):  
Hetong Ma ◽  
Liu Shen ◽  
Haixia Sun ◽  
Zidu Xu ◽  
Li Hou ◽  
...  
Keyword(s):  
Model Building ◽  
Scientific Discovery ◽  
Living Organism ◽  
Treatment Technique ◽  
Health Providers ◽  
Medical Terminology ◽  
Anatomic Site ◽  
Source Selection ◽  
Term Extraction ◽  
Schema Design

Abstract Background: The coronavirus disease (COVID-19), a pneumonia caused by severe acute respiratory syndrome coronavirus 2(SARS-CoV-2) has shown its destructiveness with more than one million confirmed cases and dozens of thousands of death, which is highly contagious and still spreading globally. World-wide studies have been conducted aiming to understand the COVID-19 mechanism, transmission, clinical features, etc. A cross-language terminology of COVID-19 is essential for improving knowledge sharing and scientific discovery dissemination.Methods: We developed a bilingual terminology of COVID-19 named COVID Term with mapping Chinese and English terms. The terminology was constructed as follows: (1) Classification schema design; (2) Concept representation model building; (3) Term source selection and term extraction; (4) Hierarchical structure construction; (5) Quality control (6) Web service. We built open access for the terminology, providing search, browse, and download services. Results: The proposed COVID Term include 10 categories: disease, anatomic site, clinical manifestation, demographic and socioeconomic characteristics, living organism, qualifiers, psychological assistance, medical equipment, instruments and materials, epidemic prevention and control, diagnosis and treatment technique respectively. In total, COVID Terms covered 464 concepts with 724 Chinese terms and 887 English terms. All terms are openly available online (COVID Term URL: http://covidterm.imicams.ac.cn ). Conclusions: COVID Term is a bilingual terminology focused on COVID-19, the epidemic pneumonia with a high risk of infection around the world. It will provide updated bilingual terms of the disease to help health providers and medical professionals retrieve and exchange information and knowledge in multiple languages. COVID Term was released in machine-readable formats (e.g., XML and JSON), which would contribute to the information retrieval, machine translation and advanced intelligent techniques application. Keywords: COVID-19, Terminology System, Bilingual, Medical Terminology

scholarly journals COVID Term: A Bilingual Terminology for COVID-19

2020 ◽  
Author(s):  
Hetong Ma ◽  
Liu Shen ◽  
Haixia Sun ◽  
Zidu Xu ◽  
Li Hou ◽  
...  
Keyword(s):  
Scientific Discovery ◽  
Living Organism ◽  
Treatment Technique ◽  
Health Providers ◽  
Property Development ◽  
Anatomic Site ◽  
Schema Design ◽  
Psychological Assistance ◽  
Machine Readable ◽  
And Control

Abstract Background The coronavirus disease (COVID-19), a pneumonia caused by severe acute respiratory syndrome coronavirus 2(SARS-CoV-2) has shown its destructiveness with more than one million confirmed cases and dozens of thousands of death, which is highly contagious and still spreading globally. World-wide studies have been conducted aiming to understand COVID-19 mechanism, transmission, clinical features, etc. A cross-language terminology of COVID-19 is essential for improving knowledge sharing and scientific discovery dissemination.Methods We developed a bilingual terminology of COVID-19 with mapping Chinese and English terms. The terminology construction follows the workflow (1) Classification schema design; (2) Concepts and sub-concepts assignment; (3) Terminology editing strategy; (4) Terminology property development; (5) Online deployment. We built open access for the terminology named as COVID Term, providing search, browse, and download services.Results The proposed COVID Term include 10 categories: disease, anatomic site, clinical manifestation, demographic and socioeconomic characteristics, living organism, qualifiers, psychological assistance, medical equipment, instruments and materials, epidemic prevention and control, diagnosis and treatment technique respectively. In total, COVID Terms covered 464 concepts with 724 Chinese terms and 887 English terms. All terms are openly accessible online (COVID Term: http://covidterm.imicams.ac.cn ).Conclusions COVID Term is a bilingual terminology focused on COVID-19, the epidemic pneumonia with a high risk of infection around the world. It will provide updated bilingual terms of the disease to help health providers and medical professionals retrieve and exchange information and knowledge in multiple languages. COVID Term was released in machine-readable formats (e.g., XML and JSON), which would contribute to the machine translation and advanced intelligent techniques.

Radiotherapy use in the treatment of gastrointestinal cancers in Medicare patients: An analysis of a CMS database.

2020 ◽  
Vol 38 (4_suppl) ◽  
pp. 800-800
Author(s):  
Andre A. Konski ◽  
Laura Jean Havrilesky ◽  
Elizabeth Lin Jewell ◽  
Jason A. Efstathiou
Keyword(s):  
Proton Beam ◽  
Proton Beam Therapy ◽  
Treatment Technique ◽  
External Beam ◽  
Anatomic Site ◽  
Gastrointestinal Malignancies ◽  
Icd 10 ◽  
Medicare Patients ◽  
Available Information ◽  
First 90 Days

800 Background: CMS announced plans to pilot an alternative payment model (APM) utilizing a fixed payment per ICD-10 code for 17 malignancies. CMS released payment data used to calculate the base payments. The purpose of this study was to analyze radiotherapy use in patients with gastrointestinal malignancies to obtain baseline utilization and payment prior to the APM start. Methods: The CMS database, CY2015-2017, contained payment, anatomic site and limited patient data on 517,988 patients, 48,032 of which were identified as gastrointestinal (GI) cancers, (Anal (A) Colorectal (CR), Pancreatic (P), Upper GI (UGI), or Liver(L) cancers) all histologies included. Stata 15, College Station, Texas, was used to perform all statistical analysis. Payments were calculated using only CPT codes for radiotherapy and only include the first 90 days after a treatment planning charge was registered. Results: Anatomic site breakdown was 4940 A, 16,099 CR, 6,970 P, 14,750 UGI and 5,273 L cancers. Patient treatments by year were15,697 in 2015, 16,309 in 2016 and 16,026 in 2017. Use of proton therapy and IMRT increased from 2015-2017 (193 (1.2%) to 302 (1.9%) and 8,107(52%) to 8838 (55%), respectively) whereas use of conventional external beam decreased from 6544 (42%) in 2015 to 5377(34%) in 2017. Of interest, 10,336 (668 A, 3363 CR, 2847 L, 1083 P, and 2375 UGI) of the 48,032 patients receiving radiotherapy were coded as not receiving chemotherapy. Mean professional and technical payments were $28,380.97 (SD $6779.18) for proton beam therapy, $18,186.77 (SD $4534.3) for IMRT, $10,724.88 ($4797.19) for conventional external beam therapy, $13,967.66 (SD $5186.92) for stereotactic, and $19,707 (SD$7393.81) for brachytherapy. Results by anatomic site will be presented. Conclusions: This study provides a baseline for comparison for treatment technique and payment once the APM has been completed. These claims are limited by the lack of available information on specific age, stage or other co-morbidities. Use of proton beam therapy has increased in this population, but remains a small percentage of radiotherapy technique used. 25% of patients receiving radiotherapy were not coded as receiving chemotherapy.

scholarly journals HESS Opinions: Incubating deep-learning-powered hydrologic science advances as a community

2018 ◽  
Vol 22 (11) ◽  
pp. 5639-5656 ◽  
Author(s):  
Chaopeng Shen ◽  
Eric Laloy ◽  
Amin Elshorbagy ◽  
Adrian Albert ◽  
Jerad Bales ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Data Science ◽  
Model Building ◽  
Scientific Discovery ◽  
Machine Learning Algorithms ◽  
Data Limitations ◽  
Industry Applications ◽  
Versatile Tool ◽  
Process Based Models

Abstract. Recently, deep learning (DL) has emerged as a revolutionary and versatile tool transforming industry applications and generating new and improved capabilities for scientific discovery and model building. The adoption of DL in hydrology has so far been gradual, but the field is now ripe for breakthroughs. This paper suggests that DL-based methods can open up a complementary avenue toward knowledge discovery in hydrologic sciences. In the new avenue, machine-learning algorithms present competing hypotheses that are consistent with data. Interrogative methods are then invoked to interpret DL models for scientists to further evaluate. However, hydrology presents many challenges for DL methods, such as data limitations, heterogeneity and co-evolution, and the general inexperience of the hydrologic field with DL. The roadmap toward DL-powered scientific advances will require the coordinated effort from a large community involving scientists and citizens. Integrating process-based models with DL models will help alleviate data limitations. The sharing of data and baseline models will improve the efficiency of the community as a whole. Open competitions could serve as the organizing events to greatly propel growth and nurture data science education in hydrology, which demands a grassroots collaboration. The area of hydrologic DL presents numerous research opportunities that could, in turn, stimulate advances in machine learning as well.

Theism and Recent Philosophical Speculation

Philosophy ◽  
1932 ◽  
Vol 7 (27) ◽  
pp. 255-266
Author(s):  
C. F. D'arcy
Keyword(s):  
Scientific Discovery ◽  
Continuous Process ◽  
Living Organism ◽  
Space Time ◽  
Scientific Thought ◽  
The Mind ◽  
The Universe ◽  
Experimental Stage ◽  
Great Development

The recent speculation which I have in view is that which finds its inspiration in the great development of scientific discovery and scientific thought in our day. It would be impossible to range over the whole field. Moreover, the efforts which have been made to frame a comprehensive scheme of thought on the foundation supplied by science are those which are truly characteristic of our time. In recent years, science has been passing beyond the experimental stage, and also beyond the limits which, in former times, made it departmental. The great conception of Evolution, and the intimate linking up of astronomy with chemistry and physics, and of chemistry and physics with physiology and biology, have quite altered our outlook on the universe. The mind of to-day passes from space-time to the electron, and from the electron to the structure of the cell, and from the cell to the living organism, with hardly a jolt. Not that the problems which mark the passage from stage to stage in this series have been solved, far from it; but that the different tages have so much the appearance of closing up into one continuous process that we seem to be approaching a view which will regard the whole as one unbroken development. No wonder that philosophic minds, among students and dreamers speculating on the mystery of the Universe, begin to think that some account of it, based on assured results of science, is becoming possible.

scholarly journals HESS Opinions: Deep learning as a promising avenue toward knowledge discovery in water sciences

2018 ◽  
Author(s):  
Chaopeng Shen ◽  
Eric Laloy ◽  
Adrian Albert ◽  
Fi-John Chang ◽  
Amin Elshorbagy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Knowledge Discovery ◽  
Model Building ◽  
Scientific Discovery ◽  
Machine Learning Algorithms ◽  
Data Compilation ◽  
Industry Applications ◽  
Versatile Tool ◽  
Promising Avenue

Abstract. Recently, deep learning (DL) has emerged as a revolutionary and versatile tool transforming industry applications and generating new and improved capabilities for scientific discovery and model building. The adoption of DL in water science has so far been gradual, but the related fields are now ripe for breakthroughs. This paper proposes that DL-based methods can open up a viable, complementary avenue toward knowledge discovery in hydrologic sciences. In the new avenue, machine-learning algorithms present competing hypotheses that are consistent with data for scientists to further evaluate. Interrogative studies are invoked to interpret DL models. In addition, we lay out several opinions shared by authors: (1) deep learning may bring forth transformative progress to the field of hydrology due to its ability to assimilate big data and identify commonalities and differences; (2) The community may benefit greatly from a variety of shared datasets and open competitions; (3) Big hydrologic data can be obtained via various ways including data compilation and working with citizen scientists, which offers the co-benefits of education and stakeholder engagement; (4) Water sciences, and hydrology in particular, offer a unique set of challenges that can, in turn, stimulate advances in machine learning; and (5) An urgent need for research is hydrology-customized methods for interpreting knowledge extracted by deep learning.

A Simple Scoring Model Predicting the Outcome of COVID-19 Patients: Tanta COVID score

2021 ◽  
Vol 21 ◽  
Author(s):  
Mohammed Elhendawy ◽  
Ferial El-Kalla ◽  
Sherief Abd-Elsalam ◽  
Dalia ElSharawy ◽  
Shaimaa S Soliman ◽  
...  
Keyword(s):  
Scoring System ◽  
Model Building ◽  
Cox Regression ◽  
Laboratory Data ◽  
Discrimination Performance ◽  
University Hospital ◽  
Severe Illness ◽  
Health Providers ◽  
Scoring Model ◽  
Increased Risk

Background & Aim: COVID-19 is a worldwide pandemic with high rates of morbidity and mortality, and an uncertain prognosis leading to an increased risk of infection in health providers and limited hospital care capacities. In this study, we have proposed a predictive, interpretable prognosis scoring system with the use of readily obtained clinical, radiological and laboratory characteristics to accurately predict worsening of the condition and overall survival of patients with COVID -19. Methods: This is a single-center, observational, prospective, cohort study. A total of 347 patients infected with COVID-19 presenting to the Tanta university hospital, Egypt, were enrolled in the study, and clinical, radiological and laboratory data were analyzed. Top-ranked variables were identified and selected to be integrated into a Cox regression model, building the scoring system for accurate prediction of the prognosis of patients with COVID-19. Results: The six variables that were finally selected in the scoring system were lymphopenia, serum CRP, ferritin, D-Dimer, radiological CT lung findings and associated chronic debilitating disease. The scoring system discriminated risk groups with either mild disease or severe illness characterized by respiratory distress (and also those with hypoxia and in need for oxygen therapy or mechanical ventilation) or death. The area under the curve to estimate the discrimination performance of the scoring system was more than 90%. Conclusion: We proposed a simple and clinically useful predictive scoring model for COVID-19 patients. However, additional independent validation will be required before the scoring model can be used commonly.

Project BRAIN: Working Together to Improve Educational Outcomes for Students With Traumatic Brain Injury

2012 ◽  
Vol 22 (3) ◽  
pp. 106-118 ◽  
Author(s):  
Paula Denslow ◽  
Jean Doster ◽  
Kristin King ◽  
Jennifer Rayman
Keyword(s):  
Traumatic Brain Injury ◽  
At Risk ◽  
Brain Injury ◽  
Educational Outcomes ◽  
School Personnel ◽  
Department Of Education ◽  
Health Providers ◽  
Accurate Identification ◽  
Transition Intervention ◽  
Working Together

Children and youth who sustain traumatic brain injury (TBI) are at risk for being unidentified or misidentified and, even if appropriately identified, are at risk of encountering professionals who are ill-equipped to address their unique needs. A comparison of the number of people in Tennessee ages 3–21 years incurring brain injury compared to the number of students ages 3–21 years being categorized and served as TBI by the Department of Education (DOE) motivated us to create this program. Identified needs addressed by the program include the following: (a) accurate identification of students with TBI; (b) training of school personnel; (c) development of linkages and training of hospital personnel; and (d) hospital-school transition intervention. Funded by Health Services and Resources Administration (HRSA) grants with support from the Tennessee DOE, Project BRAIN focuses on improving educational outcomes for students with TBI through the provision of specialized group training and ongoing education for educators, families, and health professionals who support students with TBI. The program seeks to link families, hospitals, and community health providers with school professionals such as speech-language pathologists (SLPs) to identify and address the needs of students with brain injury.

Health Providers Examine Promise of ACOs

2011 ◽  
Vol 9 (6) ◽  
pp. 6-7
Author(s):  
MARY ELLEN SCHNEIDER
Keyword(s):  
Health Providers
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