On diagnostic accuracy measure with cut-points criterion for ordinal disease classification based on concordance and discordance Article Swipe
Jing Kersey
,
Hani M. Samawi
,
Jingjing Yin
,
Haresh Rochani
,
Xinyan Zhang
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1080/02664763.2022.2041567
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1080/02664763.2022.2041567
The accuracy of a diagnostic test has always been essential in detecting disease staging. Many diagnostic tests of accuracy measures are used in binary diagnosis tests. Some measures apply to multi-stage diagnosis. Yet, there are limitations to the implementation, and the performance highly depends on the distribution of diagnostic outcomes. Another essential aspect of medical diagnostic testing using biomarkers is to find an optimal cut-point that categorizes a patient as diseased or healthy. This aspect was extended to the diseases with more than two stages. We propose a diagnostic accuracy measure and optimal cut-points selection (CD), using concordance and discordance for k-stages diseases. The CD measure uses the classification agreement and disagreement between tests outcomes and diseases stages. Simulations for power studies suggest that CD can detect the differences between the null and alternative hypotheses that other methods cannot for some scenarios. Simulation results indicate that using CD measures to select optimal cut-points can provide relatively high correct classification rates than the existing measures and more balanced accurate classification rates than the generalized Youden Index (GYI). An illustration is provided using the ANDI data to choose biomarkers for diagnosing Alzheimer's Disease (AD) and select optimal cut-points for the chosen biomarkers.
Related Topics
Concepts
Concordance
Youden's J statistic
Cut-point
Measure (data warehouse)
Diagnostic test
Computer science
Statistics
Diagnostic accuracy
Selection (genetic algorithm)
Null (SQL)
Artificial intelligence
Pattern recognition (psychology)
Data mining
Mathematics
Medicine
Receiver operating characteristic
Internal medicine
Radiology
Emergency medicine
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1080/02664763.2022.2041567
- OA Status
- green
- Cited By
- 1
- References
- 22
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4213199326
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4213199326Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1080/02664763.2022.2041567Digital Object Identifier
- Title
-
On diagnostic accuracy measure with cut-points criterion for ordinal disease classification based on concordance and discordanceWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-02-21Full publication date if available
- Authors
-
Jing Kersey, Hani M. Samawi, Jingjing Yin, Haresh Rochani, Xinyan ZhangList of authors in order
- Landing page
-
https://doi.org/10.1080/02664763.2022.2041567Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://pmc.ncbi.nlm.nih.gov/articles/PMC10228310/pdf/CJAS_50_2041567.pdfDirect OA link when available
- Concepts
-
Concordance, Youden's J statistic, Cut-point, Measure (data warehouse), Diagnostic test, Computer science, Statistics, Diagnostic accuracy, Selection (genetic algorithm), Null (SQL), Artificial intelligence, Pattern recognition (psychology), Data mining, Mathematics, Medicine, Receiver operating characteristic, Internal medicine, Radiology, Emergency medicineTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 1Per-year citation counts (last 5 years)
- References (count)
-
22Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4213199326 |
|---|---|
| doi | https://doi.org/10.1080/02664763.2022.2041567 |
| ids.doi | https://doi.org/10.1080/02664763.2022.2041567 |
| ids.pmid | https://pubmed.ncbi.nlm.nih.gov/37260473 |
| ids.openalex | https://openalex.org/W4213199326 |
| fwci | 0.19579882 |
| type | article |
| title | On diagnostic accuracy measure with cut-points criterion for ordinal disease classification based on concordance and discordance |
| biblio.issue | 8 |
| biblio.volume | 50 |
| biblio.last_page | 1789 |
| biblio.first_page | 1772 |
| topics[0].id | https://openalex.org/T13702 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.97079998254776 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1702 |
| topics[0].subfield.display_name | Artificial Intelligence |
| topics[0].display_name | Machine Learning in Healthcare |
| topics[1].id | https://openalex.org/T10136 |
| topics[1].field.id | https://openalex.org/fields/26 |
| topics[1].field.display_name | Mathematics |
| topics[1].score | 0.968500018119812 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2613 |
| topics[1].subfield.display_name | Statistics and Probability |
| topics[1].display_name | Statistical Methods and Inference |
| topics[2].id | https://openalex.org/T10804 |
| topics[2].field.id | https://openalex.org/fields/20 |
| topics[2].field.display_name | Economics, Econometrics and Finance |
| topics[2].score | 0.9599999785423279 |
| topics[2].domain.id | https://openalex.org/domains/2 |
| topics[2].domain.display_name | Social Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2002 |
| topics[2].subfield.display_name | Economics and Econometrics |
| topics[2].display_name | Health Systems, Economic Evaluations, Quality of Life |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C160798450 |
| concepts[0].level | 2 |
| concepts[0].score | 0.8299154043197632 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q4230870 |
| concepts[0].display_name | Concordance |
| concepts[1].id | https://openalex.org/C43346845 |
| concepts[1].level | 3 |
| concepts[1].score | 0.7188500165939331 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q8057732 |
| concepts[1].display_name | Youden's J statistic |
| concepts[2].id | https://openalex.org/C2781415353 |
| concepts[2].level | 2 |
| concepts[2].score | 0.7071825265884399 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q5196602 |
| concepts[2].display_name | Cut-point |
| concepts[3].id | https://openalex.org/C2780009758 |
| concepts[3].level | 2 |
| concepts[3].score | 0.574732780456543 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q6804172 |
| concepts[3].display_name | Measure (data warehouse) |
| concepts[4].id | https://openalex.org/C82157600 |
| concepts[4].level | 2 |
| concepts[4].score | 0.5575070381164551 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q2671652 |
| concepts[4].display_name | Diagnostic test |
| concepts[5].id | https://openalex.org/C41008148 |
| concepts[5].level | 0 |
| concepts[5].score | 0.5249433517456055 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[5].display_name | Computer science |
| concepts[6].id | https://openalex.org/C105795698 |
| concepts[6].level | 1 |
| concepts[6].score | 0.5037772059440613 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q12483 |
| concepts[6].display_name | Statistics |
| concepts[7].id | https://openalex.org/C3020132585 |
| concepts[7].level | 2 |
| concepts[7].score | 0.4385959208011627 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q2671652 |
| concepts[7].display_name | Diagnostic accuracy |
| concepts[8].id | https://openalex.org/C81917197 |
| concepts[8].level | 2 |
| concepts[8].score | 0.43641820549964905 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q628760 |
| concepts[8].display_name | Selection (genetic algorithm) |
| concepts[9].id | https://openalex.org/C203763787 |
| concepts[9].level | 2 |
| concepts[9].score | 0.43125247955322266 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q371029 |
| concepts[9].display_name | Null (SQL) |
| concepts[10].id | https://openalex.org/C154945302 |
| concepts[10].level | 1 |
| concepts[10].score | 0.4031512141227722 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[10].display_name | Artificial intelligence |
| concepts[11].id | https://openalex.org/C153180895 |
| concepts[11].level | 2 |
| concepts[11].score | 0.3813019096851349 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q7148389 |
| concepts[11].display_name | Pattern recognition (psychology) |
| concepts[12].id | https://openalex.org/C124101348 |
| concepts[12].level | 1 |
| concepts[12].score | 0.37879788875579834 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q172491 |
| concepts[12].display_name | Data mining |
| concepts[13].id | https://openalex.org/C33923547 |
| concepts[13].level | 0 |
| concepts[13].score | 0.3658650517463684 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[13].display_name | Mathematics |
| concepts[14].id | https://openalex.org/C71924100 |
| concepts[14].level | 0 |
| concepts[14].score | 0.3203892111778259 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q11190 |
| concepts[14].display_name | Medicine |
| concepts[15].id | https://openalex.org/C58471807 |
| concepts[15].level | 2 |
| concepts[15].score | 0.21115919947624207 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q327120 |
| concepts[15].display_name | Receiver operating characteristic |
| concepts[16].id | https://openalex.org/C126322002 |
| concepts[16].level | 1 |
| concepts[16].score | 0.0 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q11180 |
| concepts[16].display_name | Internal medicine |
| concepts[17].id | https://openalex.org/C126838900 |
| concepts[17].level | 1 |
| concepts[17].score | 0.0 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q77604 |
| concepts[17].display_name | Radiology |
| concepts[18].id | https://openalex.org/C194828623 |
| concepts[18].level | 1 |
| concepts[18].score | 0.0 |
| concepts[18].wikidata | https://www.wikidata.org/wiki/Q2861470 |
| concepts[18].display_name | Emergency medicine |
| keywords[0].id | https://openalex.org/keywords/concordance |
| keywords[0].score | 0.8299154043197632 |
| keywords[0].display_name | Concordance |
| keywords[1].id | https://openalex.org/keywords/youdens-j-statistic |
| keywords[1].score | 0.7188500165939331 |
| keywords[1].display_name | Youden's J statistic |
| keywords[2].id | https://openalex.org/keywords/cut-point |
| keywords[2].score | 0.7071825265884399 |
| keywords[2].display_name | Cut-point |
| keywords[3].id | https://openalex.org/keywords/measure |
| keywords[3].score | 0.574732780456543 |
| keywords[3].display_name | Measure (data warehouse) |
| keywords[4].id | https://openalex.org/keywords/diagnostic-test |
| keywords[4].score | 0.5575070381164551 |
| keywords[4].display_name | Diagnostic test |
| keywords[5].id | https://openalex.org/keywords/computer-science |
| keywords[5].score | 0.5249433517456055 |
| keywords[5].display_name | Computer science |
| keywords[6].id | https://openalex.org/keywords/statistics |
| keywords[6].score | 0.5037772059440613 |
| keywords[6].display_name | Statistics |
| keywords[7].id | https://openalex.org/keywords/diagnostic-accuracy |
| keywords[7].score | 0.4385959208011627 |
| keywords[7].display_name | Diagnostic accuracy |
| keywords[8].id | https://openalex.org/keywords/selection |
| keywords[8].score | 0.43641820549964905 |
| keywords[8].display_name | Selection (genetic algorithm) |
| keywords[9].id | https://openalex.org/keywords/null |
| keywords[9].score | 0.43125247955322266 |
| keywords[9].display_name | Null (SQL) |
| keywords[10].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[10].score | 0.4031512141227722 |
| keywords[10].display_name | Artificial intelligence |
| keywords[11].id | https://openalex.org/keywords/pattern-recognition |
| keywords[11].score | 0.3813019096851349 |
| keywords[11].display_name | Pattern recognition (psychology) |
| keywords[12].id | https://openalex.org/keywords/data-mining |
| keywords[12].score | 0.37879788875579834 |
| keywords[12].display_name | Data mining |
| keywords[13].id | https://openalex.org/keywords/mathematics |
| keywords[13].score | 0.3658650517463684 |
| keywords[13].display_name | Mathematics |
| keywords[14].id | https://openalex.org/keywords/medicine |
| keywords[14].score | 0.3203892111778259 |
| keywords[14].display_name | Medicine |
| keywords[15].id | https://openalex.org/keywords/receiver-operating-characteristic |
| keywords[15].score | 0.21115919947624207 |
| keywords[15].display_name | Receiver operating characteristic |
| language | en |
| locations[0].id | doi:10.1080/02664763.2022.2041567 |
| locations[0].is_oa | False |
| locations[0].source.id | https://openalex.org/S167117542 |
| locations[0].source.issn | 0266-4763, 1360-0532 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | 0266-4763 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | Journal of Applied Statistics |
| locations[0].source.host_organization | https://openalex.org/P4310320547 |
| locations[0].source.host_organization_name | Taylor & Francis |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310320547 |
| locations[0].license | |
| locations[0].pdf_url | |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Journal of Applied Statistics |
| locations[0].landing_page_url | https://doi.org/10.1080/02664763.2022.2041567 |
| locations[1].id | pmid:37260473 |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306525036 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | PubMed |
| locations[1].source.host_organization | https://openalex.org/I1299303238 |
| locations[1].source.host_organization_name | National Institutes of Health |
| locations[1].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | publishedVersion |
| locations[1].raw_type | |
| locations[1].license_id | |
| locations[1].is_accepted | True |
| locations[1].is_published | True |
| locations[1].raw_source_name | Journal of applied statistics |
| locations[1].landing_page_url | https://pubmed.ncbi.nlm.nih.gov/37260473 |
| locations[2].id | pmh:oai:pubmedcentral.nih.gov:10228310 |
| locations[2].is_oa | True |
| locations[2].source.id | https://openalex.org/S2764455111 |
| locations[2].source.issn | |
| locations[2].source.type | repository |
| locations[2].source.is_oa | False |
| locations[2].source.issn_l | |
| locations[2].source.is_core | False |
| locations[2].source.is_in_doaj | False |
| locations[2].source.display_name | PubMed Central |
| locations[2].source.host_organization | https://openalex.org/I1299303238 |
| locations[2].source.host_organization_name | National Institutes of Health |
| locations[2].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[2].license | |
| locations[2].pdf_url | https://pmc.ncbi.nlm.nih.gov/articles/PMC10228310/pdf/CJAS_50_2041567.pdf |
| locations[2].version | submittedVersion |
| locations[2].raw_type | Text |
| locations[2].license_id | |
| locations[2].is_accepted | False |
| locations[2].is_published | False |
| locations[2].raw_source_name | J Appl Stat |
| locations[2].landing_page_url | https://www.ncbi.nlm.nih.gov/pmc/articles/10228310 |
| locations[3].id | pmh:oai:figshare.com:article/19208477 |
| locations[3].is_oa | True |
| locations[3].source.id | https://openalex.org/S4306400572 |
| locations[3].source.issn | |
| locations[3].source.type | repository |
| locations[3].source.is_oa | False |
| locations[3].source.issn_l | |
| locations[3].source.is_core | False |
| locations[3].source.is_in_doaj | False |
| locations[3].source.display_name | OPAL (Open@LaTrobe) (La Trobe University) |
| locations[3].source.host_organization | https://openalex.org/I196829312 |
| locations[3].source.host_organization_name | La Trobe University |
| locations[3].source.host_organization_lineage | https://openalex.org/I196829312 |
| locations[3].license | cc-by |
| locations[3].pdf_url | |
| locations[3].version | submittedVersion |
| locations[3].raw_type | Text |
| locations[3].license_id | https://openalex.org/licenses/cc-by |
| locations[3].is_accepted | False |
| locations[3].is_published | False |
| locations[3].raw_source_name | |
| locations[3].landing_page_url | https://figshare.com/articles/journal_contribution/On_diagnostic_accuracy_measure_with_cut-points_criterion_for_ordinal_disease_classification_based_on_concordance_and_discordance/19208477 |
| locations[4].id | doi:10.6084/m9.figshare.19208477.v1 |
| locations[4].is_oa | True |
| locations[4].source | |
| locations[4].license | cc-by |
| locations[4].pdf_url | |
| locations[4].version | |
| locations[4].raw_type | article-journal |
| locations[4].license_id | https://openalex.org/licenses/cc-by |
| locations[4].is_accepted | False |
| locations[4].is_published | |
| locations[4].raw_source_name | |
| locations[4].landing_page_url | https://doi.org/10.6084/m9.figshare.19208477.v1 |
| indexed_in | crossref, datacite, pubmed |
| authorships[0].author.id | https://openalex.org/A5039872744 |
| authorships[0].author.orcid | https://orcid.org/0000-0001-7256-3978 |
| authorships[0].author.display_name | Jing Kersey |
| authorships[0].countries | US |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I39815113 |
| authorships[0].affiliations[0].raw_affiliation_string | Department of Biostatistics, Epidemiology and Environmental Sciences, Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro |
| authorships[0].institutions[0].id | https://openalex.org/I39815113 |
| authorships[0].institutions[0].ror | https://ror.org/04agmb972 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I39815113 |
| authorships[0].institutions[0].country_code | US |
| authorships[0].institutions[0].display_name | Georgia Southern University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Jing Kersey |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Department of Biostatistics, Epidemiology and Environmental Sciences, Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro |
| authorships[1].author.id | https://openalex.org/A5058158732 |
| authorships[1].author.orcid | https://orcid.org/0000-0003-3817-2249 |
| authorships[1].author.display_name | Hani M. Samawi |
| authorships[1].countries | US |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I39815113 |
| authorships[1].affiliations[0].raw_affiliation_string | Department of Biostatistics, Epidemiology and Environmental Sciences, Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro |
| authorships[1].institutions[0].id | https://openalex.org/I39815113 |
| authorships[1].institutions[0].ror | https://ror.org/04agmb972 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I39815113 |
| authorships[1].institutions[0].country_code | US |
| authorships[1].institutions[0].display_name | Georgia Southern University |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Hani Samawi |
| authorships[1].is_corresponding | True |
| authorships[1].raw_affiliation_strings | Department of Biostatistics, Epidemiology and Environmental Sciences, Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro |
| authorships[2].author.id | https://openalex.org/A5004857861 |
| authorships[2].author.orcid | https://orcid.org/0000-0003-4843-613X |
| authorships[2].author.display_name | Jingjing Yin |
| authorships[2].countries | US |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I39815113 |
| authorships[2].affiliations[0].raw_affiliation_string | Department of Biostatistics, Epidemiology and Environmental Sciences, Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro |
| authorships[2].institutions[0].id | https://openalex.org/I39815113 |
| authorships[2].institutions[0].ror | https://ror.org/04agmb972 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I39815113 |
| authorships[2].institutions[0].country_code | US |
| authorships[2].institutions[0].display_name | Georgia Southern University |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Jingjing Yin |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Department of Biostatistics, Epidemiology and Environmental Sciences, Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro |
| authorships[3].author.id | https://openalex.org/A5004566515 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-3056-6035 |
| authorships[3].author.display_name | Haresh Rochani |
| authorships[3].countries | US |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I39815113 |
| authorships[3].affiliations[0].raw_affiliation_string | Department of Biostatistics, Epidemiology and Environmental Sciences, Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro |
| authorships[3].institutions[0].id | https://openalex.org/I39815113 |
| authorships[3].institutions[0].ror | https://ror.org/04agmb972 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I39815113 |
| authorships[3].institutions[0].country_code | US |
| authorships[3].institutions[0].display_name | Georgia Southern University |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Haresh Rochani |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Department of Biostatistics, Epidemiology and Environmental Sciences, Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro |
| authorships[4].author.id | https://openalex.org/A5100393363 |
| authorships[4].author.orcid | https://orcid.org/0000-0002-8274-8711 |
| authorships[4].author.display_name | Xinyan Zhang |
| authorships[4].countries | US |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I39815113 |
| authorships[4].affiliations[0].raw_affiliation_string | Department of Biostatistics, Epidemiology and Environmental Sciences, Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro |
| authorships[4].institutions[0].id | https://openalex.org/I39815113 |
| authorships[4].institutions[0].ror | https://ror.org/04agmb972 |
| authorships[4].institutions[0].type | education |
| authorships[4].institutions[0].lineage | https://openalex.org/I39815113 |
| authorships[4].institutions[0].country_code | US |
| authorships[4].institutions[0].display_name | Georgia Southern University |
| authorships[4].author_position | last |
| authorships[4].raw_author_name | Xinyan Zhang |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | Department of Biostatistics, Epidemiology and Environmental Sciences, Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro |
| has_content.pdf | True |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://pmc.ncbi.nlm.nih.gov/articles/PMC10228310/pdf/CJAS_50_2041567.pdf |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | On diagnostic accuracy measure with cut-points criterion for ordinal disease classification based on concordance and discordance |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T13702 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.97079998254776 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1702 |
| primary_topic.subfield.display_name | Artificial Intelligence |
| primary_topic.display_name | Machine Learning in Healthcare |
| related_works | https://openalex.org/W2055472916, https://openalex.org/W2061902340, https://openalex.org/W2989173305, https://openalex.org/W3112502628, https://openalex.org/W2564086294, https://openalex.org/W4296182679, https://openalex.org/W4283207213, https://openalex.org/W4213199326, https://openalex.org/W2073887351, https://openalex.org/W4292801092 |
| cited_by_count | 1 |
| counts_by_year[0].year | 2024 |
| counts_by_year[0].cited_by_count | 1 |
| locations_count | 5 |
| best_oa_location.id | pmh:oai:pubmedcentral.nih.gov:10228310 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S2764455111 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | False |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | PubMed Central |
| best_oa_location.source.host_organization | https://openalex.org/I1299303238 |
| best_oa_location.source.host_organization_name | National Institutes of Health |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I1299303238 |
| best_oa_location.license | |
| best_oa_location.pdf_url | https://pmc.ncbi.nlm.nih.gov/articles/PMC10228310/pdf/CJAS_50_2041567.pdf |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | Text |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | J Appl Stat |
| best_oa_location.landing_page_url | https://www.ncbi.nlm.nih.gov/pmc/articles/10228310 |
| primary_location.id | doi:10.1080/02664763.2022.2041567 |
| primary_location.is_oa | False |
| primary_location.source.id | https://openalex.org/S167117542 |
| primary_location.source.issn | 0266-4763, 1360-0532 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | 0266-4763 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | Journal of Applied Statistics |
| primary_location.source.host_organization | https://openalex.org/P4310320547 |
| primary_location.source.host_organization_name | Taylor & Francis |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310320547 |
| primary_location.license | |
| primary_location.pdf_url | |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Journal of Applied Statistics |
| primary_location.landing_page_url | https://doi.org/10.1080/02664763.2022.2041567 |
| publication_date | 2022-02-21 |
| publication_year | 2022 |
| referenced_works | https://openalex.org/W1509445416, https://openalex.org/W1969758592, https://openalex.org/W2112670427, https://openalex.org/W1974625384, https://openalex.org/W2048537487, https://openalex.org/W2168366176, https://openalex.org/W2011152350, https://openalex.org/W1981744949, https://openalex.org/W1920596803, https://openalex.org/W2124216274, https://openalex.org/W2143225045, https://openalex.org/W2111732446, https://openalex.org/W2122860580, https://openalex.org/W2345969707, https://openalex.org/W2134268004, https://openalex.org/W1968114652, https://openalex.org/W4211050998, https://openalex.org/W2129497119, https://openalex.org/W2090237611, https://openalex.org/W2791954221, https://openalex.org/W1987938319, https://openalex.org/W4236554388 |
| referenced_works_count | 22 |
| abstract_inverted_index.a | 3, 67, 87 |
| abstract_inverted_index.An | 176 |
| abstract_inverted_index.CD | 104, 124, 147 |
| abstract_inverted_index.We | 85 |
| abstract_inverted_index.an | 62 |
| abstract_inverted_index.as | 69 |
| abstract_inverted_index.in | 10, 22 |
| abstract_inverted_index.is | 59, 178 |
| abstract_inverted_index.of | 2, 17, 47, 53 |
| abstract_inverted_index.on | 44 |
| abstract_inverted_index.or | 71 |
| abstract_inverted_index.to | 29, 36, 60, 77, 149, 184 |
| abstract_inverted_index.The | 0, 103 |
| abstract_inverted_index.and | 39, 91, 98, 110, 115, 132, 164, 192 |
| abstract_inverted_index.are | 20, 34 |
| abstract_inverted_index.can | 125, 153 |
| abstract_inverted_index.for | 100, 119, 139, 187, 196 |
| abstract_inverted_index.has | 6 |
| abstract_inverted_index.the | 37, 40, 45, 78, 107, 127, 130, 161, 171, 181, 197 |
| abstract_inverted_index.two | 83 |
| abstract_inverted_index.was | 75 |
| abstract_inverted_index.(AD) | 191 |
| abstract_inverted_index.ANDI | 182 |
| abstract_inverted_index.Many | 14 |
| abstract_inverted_index.Some | 26 |
| abstract_inverted_index.This | 73 |
| abstract_inverted_index.Yet, | 32 |
| abstract_inverted_index.been | 8 |
| abstract_inverted_index.data | 183 |
| abstract_inverted_index.find | 61 |
| abstract_inverted_index.high | 156 |
| abstract_inverted_index.more | 81, 165 |
| abstract_inverted_index.null | 131 |
| abstract_inverted_index.some | 140 |
| abstract_inverted_index.test | 5 |
| abstract_inverted_index.than | 82, 160, 170 |
| abstract_inverted_index.that | 65, 123, 135, 145 |
| abstract_inverted_index.used | 21 |
| abstract_inverted_index.uses | 106 |
| abstract_inverted_index.with | 80 |
| abstract_inverted_index.(CD), | 95 |
| abstract_inverted_index.Index | 174 |
| abstract_inverted_index.apply | 28 |
| abstract_inverted_index.other | 136 |
| abstract_inverted_index.power | 120 |
| abstract_inverted_index.rates | 159, 169 |
| abstract_inverted_index.tests | 16, 113 |
| abstract_inverted_index.there | 33 |
| abstract_inverted_index.using | 57, 96, 146, 180 |
| abstract_inverted_index.(GYI). | 175 |
| abstract_inverted_index.Youden | 173 |
| abstract_inverted_index.always | 7 |
| abstract_inverted_index.aspect | 52, 74 |
| abstract_inverted_index.binary | 23 |
| abstract_inverted_index.cannot | 138 |
| abstract_inverted_index.choose | 185 |
| abstract_inverted_index.chosen | 198 |
| abstract_inverted_index.detect | 126 |
| abstract_inverted_index.highly | 42 |
| abstract_inverted_index.select | 150, 193 |
| abstract_inverted_index.tests. | 25 |
| abstract_inverted_index.Another | 50 |
| abstract_inverted_index.Disease | 190 |
| abstract_inverted_index.between | 112, 129 |
| abstract_inverted_index.correct | 157 |
| abstract_inverted_index.depends | 43 |
| abstract_inverted_index.disease | 12 |
| abstract_inverted_index.measure | 90, 105 |
| abstract_inverted_index.medical | 54 |
| abstract_inverted_index.methods | 137 |
| abstract_inverted_index.optimal | 63, 92, 151, 194 |
| abstract_inverted_index.patient | 68 |
| abstract_inverted_index.propose | 86 |
| abstract_inverted_index.provide | 154 |
| abstract_inverted_index.results | 143 |
| abstract_inverted_index.stages. | 84, 117 |
| abstract_inverted_index.studies | 121 |
| abstract_inverted_index.suggest | 122 |
| abstract_inverted_index.testing | 56 |
| abstract_inverted_index.accuracy | 1, 18, 89 |
| abstract_inverted_index.accurate | 167 |
| abstract_inverted_index.balanced | 166 |
| abstract_inverted_index.diseased | 70 |
| abstract_inverted_index.diseases | 79, 116 |
| abstract_inverted_index.existing | 162 |
| abstract_inverted_index.extended | 76 |
| abstract_inverted_index.healthy. | 72 |
| abstract_inverted_index.indicate | 144 |
| abstract_inverted_index.k-stages | 101 |
| abstract_inverted_index.measures | 19, 27, 148, 163 |
| abstract_inverted_index.outcomes | 114 |
| abstract_inverted_index.provided | 179 |
| abstract_inverted_index.staging. | 13 |
| abstract_inverted_index.agreement | 109 |
| abstract_inverted_index.cut-point | 64 |
| abstract_inverted_index.detecting | 11 |
| abstract_inverted_index.diagnosis | 24 |
| abstract_inverted_index.diseases. | 102 |
| abstract_inverted_index.essential | 9, 51 |
| abstract_inverted_index.outcomes. | 49 |
| abstract_inverted_index.selection | 94 |
| abstract_inverted_index.Simulation | 142 |
| abstract_inverted_index.biomarkers | 58, 186 |
| abstract_inverted_index.cut-points | 93, 152, 195 |
| abstract_inverted_index.diagnosing | 188 |
| abstract_inverted_index.diagnosis. | 31 |
| abstract_inverted_index.diagnostic | 4, 15, 48, 55, 88 |
| abstract_inverted_index.hypotheses | 134 |
| abstract_inverted_index.relatively | 155 |
| abstract_inverted_index.scenarios. | 141 |
| abstract_inverted_index.Alzheimer's | 189 |
| abstract_inverted_index.Simulations | 118 |
| abstract_inverted_index.alternative | 133 |
| abstract_inverted_index.biomarkers. | 199 |
| abstract_inverted_index.categorizes | 66 |
| abstract_inverted_index.concordance | 97 |
| abstract_inverted_index.differences | 128 |
| abstract_inverted_index.discordance | 99 |
| abstract_inverted_index.generalized | 172 |
| abstract_inverted_index.limitations | 35 |
| abstract_inverted_index.multi-stage | 30 |
| abstract_inverted_index.performance | 41 |
| abstract_inverted_index.disagreement | 111 |
| abstract_inverted_index.distribution | 46 |
| abstract_inverted_index.illustration | 177 |
| abstract_inverted_index.classification | 108, 158, 168 |
| abstract_inverted_index.implementation, | 38 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 90 |
| corresponding_author_ids | https://openalex.org/A5058158732 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| corresponding_institution_ids | https://openalex.org/I39815113 |
| citation_normalized_percentile.value | 0.49213042 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |