The number of concepts from each semantic group that were used by each annotator and the reference standard for coding the 20 medical text snippets.

Generation of silver standard concept annotations from biomedical texts with special relevance to phenotypes.

Anika Oellrich
Nigel Collier
Damian Smedley
Tudor Groza

Electronic health records and scientific articles possess differing linguistic characteristics that ...

The number of differentially expressed transcripts common and specific for each sample type.

Cristiana Paina (615997)
Stephen L. Byrne (630395)
Cristian Domnisoru (630396)
Torben Asp (34644)

September 2014

The number of differentially expressed transcripts common and specific for each sample type.</p

The relative number of documents on the web for each of the 15 fields as defined by MAS.

Madian Khabsa (562098)
C. Lee Giles (562099)

May 2014

The relative number of documents on the web for each of the 15 fields as defined by MAS.</p

Number of concepts associated with each semantic group and extracted from SNOMED CT to create the terminology setting.

Jose Antonio Miñarro-Giménez (558772)
Catalina Martínez-Costa (6160640)
Daniel Karlsson (199317)
Stefan Schulz (86207)
Kirstine Rosenbeck Gøeg (6160643)

December 2018

Number of concepts associated with each semantic group and extracted from SNOMED CT to create the te...

Number of annotated terms and unique terms in the harmonized set and in the full patent set of the gold standard corpus after disambiguation.

Saber A. Akhondi (636394)
Alexander G. Klenner (636395)
Christian Tyrchan (636396)
Anil K. Manchala (636397)
Kiran Boppana (477814)
Daniel Lowe (401476)
Marc Zimmermann (636398)
Sarma A. R. P. Jagarlapudi (636399)
Roger Sayle (636400)
Jan A. Kors (173657)
Sorel Muresan (435355)

September 2014

Number of annotated terms and unique terms in the harmonized set and in the full patent set of th...

Studies within each medical condition/injury.

Aoife Healy (4961899)
Sybil Farmer (4961905)
Anand Pandyan (4961896)
Nachiappan Chockalingam (4961902)

March 2018

Breakdown of number of studies within each medical condition/injury, and the number of studies wi...

Number of annotated terms and unique terms within the harmonized set prior to disambiguation.

Saber A. Akhondi (636394)
Alexander G. Klenner (636395)
Christian Tyrchan (636396)
Anil K. Manchala (636397)
Kiran Boppana (477814)
Daniel Lowe (401476)
Marc Zimmermann (636398)
Sarma A. R. P. Jagarlapudi (636399)
Roger Sayle (636400)
Jan A. Kors (173657)
Sorel Muresan (435355)

September 2014

Number of annotated terms and unique terms within the harmonized set prior to disambiguation.</p

The number of regions of interest examined in the corpus callosum per group.

Shrushrita Sharma (3929930)
Yunyan Zhang (1269093)

April 2017

The number of regions of interest examined in the corpus callosum per group.</p

The 8 most common medical and surgical diagnosis categories and their relative frequency amongst all admissions.

Lynne Sykes (296298)
Philip A. Kalra (602225)
Darren Green (3020607)

April 2019

The 8 most common medical and surgical diagnosis categories and their relative frequency amongst all...

Semantic categories identified distinguishing between the number of different lexical items (types) per participant group, and the number of actual instances of relevant lexis (tokens) identified within the keyword lists in each category.

Elvira Perez (175657)
Melody Turner (625220)
Anthony Fisher (625221)
Joanna Lockwood (625222)
David Daley (625223)

September 2014

Semantic categories identified distinguishing between the number of different lexical items (type...

The number of characters (terms) present in at least 2, 10, 50, and 100 articles.

Katja C. Seltmann (187790)
Zsolt Pénzes (43582)
Matthew J. Yoder (187772)
Matthew A. Bertone (187785)
Andrew R. Deans (187801)

February 2013

The number of characters (terms) present in at least 2, 10, 50, and 100 articles.</p

1

A. J. Benham
P. Kováč
M. G. Petterson
I. Rojkovic
M. T. Styles
A. G. Gunn
J. A. Mckervey
A. Wasy

January 2015

the number of words of text – 5 264 the number of references – 52 the number of tables and figures –...

The total number of texts obtained per language and the number and percentage of texts retained in the database.

Johanna K. Kaakinen (12066085)
Egon Werlen (13924439)
Yvonne Kammerer (755309)
Cengiz Acartürk (9551952)
Xavier Aparicio (3342005)
Thierry Baccino (502753)
Ugo Ballenghein (6280079)
Per Bergamin (13924442)
Núria Castells (13924445)
Armanda Costa (13924448)
Isabel Falé (13924451)
Olga Mégalakaki (13924454)
Susana Ruiz Fernández (4244875)

October 2022

The total number of texts obtained per language and the number and percentage of texts retained in t...

Number of syndromes identified in each case using information from individual test requests.

Fernanda C. Dórea (386726)
C. Anne Muckle (386727)
David Kelton (386728)
JT. McClure (386729)
Beverly J. McEwen (386730)
W. Bruce McNab (386731)
Javier Sanchez (313760)
Crawford W. Revie (84674)

March 2013

Number of syndromes identified in each case using information from individual test requests.</p

MedNorm: A Corpus and Embeddings for Cross-terminology Medical Concept Normalisation

Belousov, M (via Mendeley Data)

June 2019

MedNorm is a corpus of 27,979 textual descriptions simultaneously mapped to both MedDRA and SNOMED-C...

Generation of silver standard concept annotations from biomedical texts with special relevance to phenotypes.

Anika Oellrich
Nigel Collier
Damian Smedley
Tudor Groza

Electronic health records and scientific articles possess differing linguistic characteristics that ...

The number of differentially expressed transcripts common and specific for each sample type.

Cristiana Paina (615997)
Stephen L. Byrne (630395)
Cristian Domnisoru (630396)
Torben Asp (34644)

September 2014

The number of differentially expressed transcripts common and specific for each sample type.</p

The relative number of documents on the web for each of the 15 fields as defined by MAS.

Madian Khabsa (562098)
C. Lee Giles (562099)

May 2014

The relative number of documents on the web for each of the 15 fields as defined by MAS.</p

Number of concepts associated with each semantic group and extracted from SNOMED CT to create the terminology setting.

Jose Antonio Miñarro-Giménez (558772)
Catalina Martínez-Costa (6160640)
Daniel Karlsson (199317)
Stefan Schulz (86207)
Kirstine Rosenbeck Gøeg (6160643)

December 2018

Number of concepts associated with each semantic group and extracted from SNOMED CT to create the te...

Number of annotated terms and unique terms in the harmonized set and in the full patent set of the gold standard corpus after disambiguation.

Saber A. Akhondi (636394)
Alexander G. Klenner (636395)
Christian Tyrchan (636396)
Anil K. Manchala (636397)
Kiran Boppana (477814)
Daniel Lowe (401476)
Marc Zimmermann (636398)
Sarma A. R. P. Jagarlapudi (636399)
Roger Sayle (636400)
Jan A. Kors (173657)
Sorel Muresan (435355)

September 2014

Number of annotated terms and unique terms in the harmonized set and in the full patent set of th...

Studies within each medical condition/injury.

Aoife Healy (4961899)
Sybil Farmer (4961905)
Anand Pandyan (4961896)
Nachiappan Chockalingam (4961902)

March 2018

Breakdown of number of studies within each medical condition/injury, and the number of studies wi...

Number of annotated terms and unique terms within the harmonized set prior to disambiguation.

Saber A. Akhondi (636394)
Alexander G. Klenner (636395)
Christian Tyrchan (636396)
Anil K. Manchala (636397)
Kiran Boppana (477814)
Daniel Lowe (401476)
Marc Zimmermann (636398)
Sarma A. R. P. Jagarlapudi (636399)
Roger Sayle (636400)
Jan A. Kors (173657)
Sorel Muresan (435355)

September 2014

Number of annotated terms and unique terms within the harmonized set prior to disambiguation.</p

The number of regions of interest examined in the corpus callosum per group.

Shrushrita Sharma (3929930)
Yunyan Zhang (1269093)

April 2017

The number of regions of interest examined in the corpus callosum per group.</p

The 8 most common medical and surgical diagnosis categories and their relative frequency amongst all admissions.

Lynne Sykes (296298)
Philip A. Kalra (602225)
Darren Green (3020607)

April 2019

The 8 most common medical and surgical diagnosis categories and their relative frequency amongst all...

Semantic categories identified distinguishing between the number of different lexical items (types) per participant group, and the number of actual instances of relevant lexis (tokens) identified within the keyword lists in each category.

Elvira Perez (175657)
Melody Turner (625220)
Anthony Fisher (625221)
Joanna Lockwood (625222)
David Daley (625223)

September 2014

Semantic categories identified distinguishing between the number of different lexical items (type...

The number of characters (terms) present in at least 2, 10, 50, and 100 articles.

Katja C. Seltmann (187790)
Zsolt Pénzes (43582)
Matthew J. Yoder (187772)
Matthew A. Bertone (187785)
Andrew R. Deans (187801)

February 2013

The number of characters (terms) present in at least 2, 10, 50, and 100 articles.</p

1

A. J. Benham
P. Kováč
M. G. Petterson
I. Rojkovic
M. T. Styles
A. G. Gunn
J. A. Mckervey
A. Wasy

January 2015

the number of words of text – 5 264 the number of references – 52 the number of tables and figures –...

The total number of texts obtained per language and the number and percentage of texts retained in the database.

Johanna K. Kaakinen (12066085)
Egon Werlen (13924439)
Yvonne Kammerer (755309)
Cengiz Acartürk (9551952)
Xavier Aparicio (3342005)
Thierry Baccino (502753)
Ugo Ballenghein (6280079)
Per Bergamin (13924442)
Núria Castells (13924445)
Armanda Costa (13924448)
Isabel Falé (13924451)
Olga Mégalakaki (13924454)
Susana Ruiz Fernández (4244875)

October 2022

The total number of texts obtained per language and the number and percentage of texts retained in t...

Number of syndromes identified in each case using information from individual test requests.

Fernanda C. Dórea (386726)
C. Anne Muckle (386727)
David Kelton (386728)
JT. McClure (386729)
Beverly J. McEwen (386730)
W. Bruce McNab (386731)
Javier Sanchez (313760)
Crawford W. Revie (84674)

March 2013

Number of syndromes identified in each case using information from individual test requests.</p

MedNorm: A Corpus and Embeddings for Cross-terminology Medical Concept Normalisation

Belousov, M (via Mendeley Data)

June 2019

MedNorm is a corpus of 27,979 textual descriptions simultaneously mapped to both MedDRA and SNOMED-C...

Generation of silver standard concept annotations from biomedical texts with special relevance to phenotypes.

Anika Oellrich
Nigel Collier
Damian Smedley
Tudor Groza

Electronic health records and scientific articles possess differing linguistic characteristics that ...

The number of differentially expressed transcripts common and specific for each sample type.

Cristiana Paina (615997)
Stephen L. Byrne (630395)
Cristian Domnisoru (630396)
Torben Asp (34644)

September 2014

The number of differentially expressed transcripts common and specific for each sample type.</p

The relative number of documents on the web for each of the 15 fields as defined by MAS.

Madian Khabsa (562098)
C. Lee Giles (562099)

May 2014

The relative number of documents on the web for each of the 15 fields as defined by MAS.</p

The number of concepts from each semantic group that were used by each annotator and the reference standard for coding the 20 medical text snippets.

Abstract

Extracted data

The number of concepts from each semantic group that were used by each annotator and the reference standard for coding the 20 medical text snippets.

Abstract

Extracted data

Related items

Related items