A Machine-Based Prediction Model of ADHD Using CPT Data Article Swipe
Ortal Slobodin
,
Inbal Yahav
,
Itai Berger
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.3389/fnhum.2020.560021
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.3389/fnhum.2020.560021
Despite the popularity of the continuous performance test (CPT) in the diagnosis of attention-deficit/hyperactivity disorder (ADHD), its specificity, sensitivity, and ecological validity are still debated. To address some of the known shortcomings of traditional analysis and interpretation of CPT data, the present study applied a machine learning-based model (ML) using CPT indices for the Prediction of ADHD.Using a retrospective factorial fitting, followed by a bootstrap technique, we trained, cross-validated, and tested learning models on CPT performance data of 458 children aged 6-12 years (213 children with ADHD and 245 typically developed children). We used the MOXO-CPT version that included visual and auditory stimuli distractors. Results showed that the ML proposed model performed better and had a higher accuracy than the benchmark approach that used clinical data only. Using the CPT total score (that included all four indices: Attention, Timeliness, Hyperactivity, and Impulsiveness), as well as four control variables [age, gender, day of the week (DoW), time of day (ToD)], provided the most salient information for discriminating children with ADHD from their typically developed peers. This model had an accuracy rate of 87%, a sensitivity rate of 89%, and a specificity rate of 84%. This performance was 34% higher than the best-achieved accuracy of the benchmark model. The ML detection model could classify children with ADHD with high accuracy based on CPT performance. ML model of ADHD holds the promise of enhancing, perhaps complementing, behavioral assessment and may be used as a supportive measure in the evaluation of ADHD.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fnhum.2020.560021
- https://www.frontiersin.org/articles/10.3389/fnhum.2020.560021/pdf
- OA Status
- gold
- Cited By
- 71
- References
- 66
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3084498024
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3084498024Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3389/fnhum.2020.560021Digital Object Identifier
- Title
-
A Machine-Based Prediction Model of ADHD Using CPT DataWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-09-17Full publication date if available
- Authors
-
Ortal Slobodin, Inbal Yahav, Itai BergerList of authors in order
- Landing page
-
https://doi.org/10.3389/fnhum.2020.560021Publisher landing page
- PDF URL
-
https://www.frontiersin.org/articles/10.3389/fnhum.2020.560021/pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.frontiersin.org/articles/10.3389/fnhum.2020.560021/pdfDirect OA link when available
- Concepts
-
Attention deficit hyperactivity disorder, Machine learning, Artificial intelligence, Benchmark (surveying), Psychology, Contrast (vision), Computer science, Clinical psychology, Geography, GeodesyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
71Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 18, 2024: 26, 2023: 9, 2022: 13, 2021: 5Per-year citation counts (last 5 years)
- References (count)
-
66Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W3084498024 |
|---|---|
| doi | https://doi.org/10.3389/fnhum.2020.560021 |
| ids.doi | https://doi.org/10.3389/fnhum.2020.560021 |
| ids.mag | 3084498024 |
| ids.pmid | https://pubmed.ncbi.nlm.nih.gov/33093829 |
| ids.openalex | https://openalex.org/W3084498024 |
| fwci | 5.23010684 |
| type | article |
| title | A Machine-Based Prediction Model of ADHD Using CPT Data |
| biblio.issue | |
| biblio.volume | 14 |
| biblio.last_page | 560021 |
| biblio.first_page | 560021 |
| topics[0].id | https://openalex.org/T10537 |
| topics[0].field.id | https://openalex.org/fields/27 |
| topics[0].field.display_name | Medicine |
| topics[0].score | 1.0 |
| topics[0].domain.id | https://openalex.org/domains/4 |
| topics[0].domain.display_name | Health Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2738 |
| topics[0].subfield.display_name | Psychiatry and Mental health |
| topics[0].display_name | Attention Deficit Hyperactivity Disorder |
| topics[1].id | https://openalex.org/T10042 |
| topics[1].field.id | https://openalex.org/fields/28 |
| topics[1].field.display_name | Neuroscience |
| topics[1].score | 0.9624999761581421 |
| topics[1].domain.id | https://openalex.org/domains/1 |
| topics[1].domain.display_name | Life Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2805 |
| topics[1].subfield.display_name | Cognitive Neuroscience |
| topics[1].display_name | Neural and Behavioral Psychology Studies |
| topics[2].id | https://openalex.org/T10241 |
| topics[2].field.id | https://openalex.org/fields/28 |
| topics[2].field.display_name | Neuroscience |
| topics[2].score | 0.9577000141143799 |
| topics[2].domain.id | https://openalex.org/domains/1 |
| topics[2].domain.display_name | Life Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2805 |
| topics[2].subfield.display_name | Cognitive Neuroscience |
| topics[2].display_name | Functional Brain Connectivity Studies |
| is_xpac | False |
| apc_list.value | 2950 |
| apc_list.currency | USD |
| apc_list.value_usd | 2950 |
| apc_paid.value | 2950 |
| apc_paid.currency | USD |
| apc_paid.value_usd | 2950 |
| concepts[0].id | https://openalex.org/C2780783007 |
| concepts[0].level | 2 |
| concepts[0].score | 0.6768743991851807 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q181923 |
| concepts[0].display_name | Attention deficit hyperactivity disorder |
| concepts[1].id | https://openalex.org/C119857082 |
| concepts[1].level | 1 |
| concepts[1].score | 0.5562580823898315 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[1].display_name | Machine learning |
| concepts[2].id | https://openalex.org/C154945302 |
| concepts[2].level | 1 |
| concepts[2].score | 0.5369846820831299 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[2].display_name | Artificial intelligence |
| concepts[3].id | https://openalex.org/C185798385 |
| concepts[3].level | 2 |
| concepts[3].score | 0.4913081228733063 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q1161707 |
| concepts[3].display_name | Benchmark (surveying) |
| concepts[4].id | https://openalex.org/C15744967 |
| concepts[4].level | 0 |
| concepts[4].score | 0.47822636365890503 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q9418 |
| concepts[4].display_name | Psychology |
| concepts[5].id | https://openalex.org/C2776502983 |
| concepts[5].level | 2 |
| concepts[5].score | 0.42901745438575745 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q690182 |
| concepts[5].display_name | Contrast (vision) |
| concepts[6].id | https://openalex.org/C41008148 |
| concepts[6].level | 0 |
| concepts[6].score | 0.353506863117218 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[6].display_name | Computer science |
| concepts[7].id | https://openalex.org/C70410870 |
| concepts[7].level | 1 |
| concepts[7].score | 0.19441771507263184 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q199906 |
| concepts[7].display_name | Clinical psychology |
| concepts[8].id | https://openalex.org/C205649164 |
| concepts[8].level | 0 |
| concepts[8].score | 0.0 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q1071 |
| concepts[8].display_name | Geography |
| concepts[9].id | https://openalex.org/C13280743 |
| concepts[9].level | 1 |
| concepts[9].score | 0.0 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q131089 |
| concepts[9].display_name | Geodesy |
| keywords[0].id | https://openalex.org/keywords/attention-deficit-hyperactivity-disorder |
| keywords[0].score | 0.6768743991851807 |
| keywords[0].display_name | Attention deficit hyperactivity disorder |
| keywords[1].id | https://openalex.org/keywords/machine-learning |
| keywords[1].score | 0.5562580823898315 |
| keywords[1].display_name | Machine learning |
| keywords[2].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[2].score | 0.5369846820831299 |
| keywords[2].display_name | Artificial intelligence |
| keywords[3].id | https://openalex.org/keywords/benchmark |
| keywords[3].score | 0.4913081228733063 |
| keywords[3].display_name | Benchmark (surveying) |
| keywords[4].id | https://openalex.org/keywords/psychology |
| keywords[4].score | 0.47822636365890503 |
| keywords[4].display_name | Psychology |
| keywords[5].id | https://openalex.org/keywords/contrast |
| keywords[5].score | 0.42901745438575745 |
| keywords[5].display_name | Contrast (vision) |
| keywords[6].id | https://openalex.org/keywords/computer-science |
| keywords[6].score | 0.353506863117218 |
| keywords[6].display_name | Computer science |
| keywords[7].id | https://openalex.org/keywords/clinical-psychology |
| keywords[7].score | 0.19441771507263184 |
| keywords[7].display_name | Clinical psychology |
| language | en |
| locations[0].id | doi:10.3389/fnhum.2020.560021 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S146364893 |
| locations[0].source.issn | 1662-5161 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 1662-5161 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | Frontiers in Human Neuroscience |
| locations[0].source.host_organization | https://openalex.org/P4310320527 |
| locations[0].source.host_organization_name | Frontiers Media |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310320527 |
| locations[0].source.host_organization_lineage_names | Frontiers Media |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://www.frontiersin.org/articles/10.3389/fnhum.2020.560021/pdf |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Frontiers in Human Neuroscience |
| locations[0].landing_page_url | https://doi.org/10.3389/fnhum.2020.560021 |
| locations[1].id | pmid:33093829 |
| 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 | Frontiers in human neuroscience |
| locations[1].landing_page_url | https://pubmed.ncbi.nlm.nih.gov/33093829 |
| locations[2].id | pmh:oai:doaj.org/article:027ae3d2388a4e6793969b1dfd8291dd |
| locations[2].is_oa | True |
| locations[2].source.id | https://openalex.org/S4306401280 |
| 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 | DOAJ (DOAJ: Directory of Open Access Journals) |
| locations[2].source.host_organization | |
| locations[2].source.host_organization_name | |
| locations[2].license | cc-by-sa |
| locations[2].pdf_url | |
| locations[2].version | submittedVersion |
| locations[2].raw_type | article |
| locations[2].license_id | https://openalex.org/licenses/cc-by-sa |
| locations[2].is_accepted | False |
| locations[2].is_published | False |
| locations[2].raw_source_name | Frontiers in Human Neuroscience, Vol 14 (2020) |
| locations[2].landing_page_url | https://doaj.org/article/027ae3d2388a4e6793969b1dfd8291dd |
| locations[3].id | pmh:oai:pubmedcentral.nih.gov:7528635 |
| locations[3].is_oa | True |
| locations[3].source.id | https://openalex.org/S2764455111 |
| 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 | PubMed Central |
| locations[3].source.host_organization | https://openalex.org/I1299303238 |
| locations[3].source.host_organization_name | National Institutes of Health |
| locations[3].source.host_organization_lineage | https://openalex.org/I1299303238 |
| 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 | Front Hum Neurosci |
| locations[3].landing_page_url | https://www.ncbi.nlm.nih.gov/pmc/articles/7528635 |
| indexed_in | crossref, doaj, pubmed |
| authorships[0].author.id | https://openalex.org/A5072437649 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-1371-5254 |
| authorships[0].author.display_name | Ortal Slobodin |
| authorships[0].countries | IL |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I124227911 |
| authorships[0].affiliations[0].raw_affiliation_string | Department of Education, Ben-Gurion University, Beer-Sheva, Israel |
| authorships[0].institutions[0].id | https://openalex.org/I124227911 |
| authorships[0].institutions[0].ror | https://ror.org/05tkyf982 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I124227911 |
| authorships[0].institutions[0].country_code | IL |
| authorships[0].institutions[0].display_name | Ben-Gurion University of the Negev |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Ortal Slobodin |
| authorships[0].is_corresponding | True |
| authorships[0].raw_affiliation_strings | Department of Education, Ben-Gurion University, Beer-Sheva, Israel |
| authorships[1].author.id | https://openalex.org/A5070441489 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-1513-017X |
| authorships[1].author.display_name | Inbal Yahav |
| authorships[1].countries | IL |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I16391192 |
| authorships[1].affiliations[0].raw_affiliation_string | Coller School of Management, Tel Aviv University, Tel Aviv, Israel |
| authorships[1].institutions[0].id | https://openalex.org/I16391192 |
| authorships[1].institutions[0].ror | https://ror.org/04mhzgx49 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I16391192 |
| authorships[1].institutions[0].country_code | IL |
| authorships[1].institutions[0].display_name | Tel Aviv University |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Inbal Yahav |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Coller School of Management, Tel Aviv University, Tel Aviv, Israel |
| authorships[2].author.id | https://openalex.org/A5081552737 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-7577-4444 |
| authorships[2].author.display_name | Itai Berger |
| authorships[2].countries | IL |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I2800586481 |
| authorships[2].affiliations[0].raw_affiliation_string | Pediatric Neurology, Assuta Ashdod University Hospital, Ashdod, Israel |
| authorships[2].affiliations[1].institution_ids | https://openalex.org/I124227911 |
| authorships[2].affiliations[1].raw_affiliation_string | Faculty of Health Sciences, Ben-Gurion University, Beer-Sheva, Israel |
| authorships[2].institutions[0].id | https://openalex.org/I2800586481 |
| authorships[2].institutions[0].ror | https://ror.org/04qkymg17 |
| authorships[2].institutions[0].type | healthcare |
| authorships[2].institutions[0].lineage | https://openalex.org/I2800586481 |
| authorships[2].institutions[0].country_code | IL |
| authorships[2].institutions[0].display_name | Assuta Medical Center |
| authorships[2].institutions[1].id | https://openalex.org/I124227911 |
| authorships[2].institutions[1].ror | https://ror.org/05tkyf982 |
| authorships[2].institutions[1].type | education |
| authorships[2].institutions[1].lineage | https://openalex.org/I124227911 |
| authorships[2].institutions[1].country_code | IL |
| authorships[2].institutions[1].display_name | Ben-Gurion University of the Negev |
| authorships[2].author_position | last |
| authorships[2].raw_author_name | Itai Berger |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Faculty of Health Sciences, Ben-Gurion University, Beer-Sheva, Israel, Pediatric Neurology, Assuta Ashdod University Hospital, Ashdod, Israel |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://www.frontiersin.org/articles/10.3389/fnhum.2020.560021/pdf |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | A Machine-Based Prediction Model of ADHD Using CPT Data |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10537 |
| primary_topic.field.id | https://openalex.org/fields/27 |
| primary_topic.field.display_name | Medicine |
| primary_topic.score | 1.0 |
| primary_topic.domain.id | https://openalex.org/domains/4 |
| primary_topic.domain.display_name | Health Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2738 |
| primary_topic.subfield.display_name | Psychiatry and Mental health |
| primary_topic.display_name | Attention Deficit Hyperactivity Disorder |
| related_works | https://openalex.org/W2961085424, https://openalex.org/W4306674287, https://openalex.org/W4286629047, https://openalex.org/W3046775127, https://openalex.org/W3107602296, https://openalex.org/W3170094116, https://openalex.org/W4386462264, https://openalex.org/W4313488044, https://openalex.org/W3209574120, https://openalex.org/W4312192474 |
| cited_by_count | 71 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 18 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 26 |
| counts_by_year[2].year | 2023 |
| counts_by_year[2].cited_by_count | 9 |
| counts_by_year[3].year | 2022 |
| counts_by_year[3].cited_by_count | 13 |
| counts_by_year[4].year | 2021 |
| counts_by_year[4].cited_by_count | 5 |
| locations_count | 4 |
| best_oa_location.id | doi:10.3389/fnhum.2020.560021 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S146364893 |
| best_oa_location.source.issn | 1662-5161 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 1662-5161 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | Frontiers in Human Neuroscience |
| best_oa_location.source.host_organization | https://openalex.org/P4310320527 |
| best_oa_location.source.host_organization_name | Frontiers Media |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310320527 |
| best_oa_location.source.host_organization_lineage_names | Frontiers Media |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://www.frontiersin.org/articles/10.3389/fnhum.2020.560021/pdf |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Frontiers in Human Neuroscience |
| best_oa_location.landing_page_url | https://doi.org/10.3389/fnhum.2020.560021 |
| primary_location.id | doi:10.3389/fnhum.2020.560021 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S146364893 |
| primary_location.source.issn | 1662-5161 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 1662-5161 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | Frontiers in Human Neuroscience |
| primary_location.source.host_organization | https://openalex.org/P4310320527 |
| primary_location.source.host_organization_name | Frontiers Media |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310320527 |
| primary_location.source.host_organization_lineage_names | Frontiers Media |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://www.frontiersin.org/articles/10.3389/fnhum.2020.560021/pdf |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Frontiers in Human Neuroscience |
| primary_location.landing_page_url | https://doi.org/10.3389/fnhum.2020.560021 |
| publication_date | 2020-09-17 |
| publication_year | 2020 |
| referenced_works | https://openalex.org/W15153721, https://openalex.org/W6605598238, https://openalex.org/W6600642783, https://openalex.org/W2168649984, https://openalex.org/W2553179036, https://openalex.org/W2156087012, https://openalex.org/W2911964244, https://openalex.org/W2121447733, https://openalex.org/W60298810, https://openalex.org/W2148143831, https://openalex.org/W134249405, https://openalex.org/W135313329, https://openalex.org/W2789958560, https://openalex.org/W1974731011, https://openalex.org/W2263602591, https://openalex.org/W2158782477, https://openalex.org/W2058854393, https://openalex.org/W2805062029, https://openalex.org/W6734841028, https://openalex.org/W2182820749, https://openalex.org/W2587226109, https://openalex.org/W2023282360, https://openalex.org/W2052485596, https://openalex.org/W2905898683, https://openalex.org/W2118978333, https://openalex.org/W7044050609, https://openalex.org/W2800244867, https://openalex.org/W2123998733, https://openalex.org/W1996670380, https://openalex.org/W2139762462, https://openalex.org/W2357858505, https://openalex.org/W2120237358, https://openalex.org/W2014517967, https://openalex.org/W1985534419, https://openalex.org/W2611351791, https://openalex.org/W2918370085, https://openalex.org/W42779687, https://openalex.org/W1977936838, https://openalex.org/W2912587246, https://openalex.org/W6706816345, https://openalex.org/W1998663496, https://openalex.org/W2075529349, https://openalex.org/W1574068988, https://openalex.org/W6634234827, https://openalex.org/W2902620820, https://openalex.org/W2908272298, https://openalex.org/W2655824372, https://openalex.org/W2801745895, https://openalex.org/W2951936974, https://openalex.org/W2333165638, https://openalex.org/W3014324001, https://openalex.org/W1588946806, https://openalex.org/W2963548617, https://openalex.org/W2042891941, https://openalex.org/W1899193005, https://openalex.org/W2141669707, https://openalex.org/W2915607787, https://openalex.org/W2082378967, https://openalex.org/W2162012775, https://openalex.org/W3118506129, https://openalex.org/W2770084542, https://openalex.org/W2163322724, https://openalex.org/W3121452939, https://openalex.org/W2596087439, https://openalex.org/W2590957573, https://openalex.org/W2155225295 |
| referenced_works_count | 66 |
| abstract_inverted_index.a | 44, 57, 63, 115, 182, 188, 240 |
| abstract_inverted_index.ML | 108, 207, 222 |
| abstract_inverted_index.To | 25 |
| abstract_inverted_index.We | 92 |
| abstract_inverted_index.an | 177 |
| abstract_inverted_index.as | 142, 144, 239 |
| abstract_inverted_index.be | 237 |
| abstract_inverted_index.by | 62 |
| abstract_inverted_index.in | 9, 243 |
| abstract_inverted_index.of | 3, 12, 28, 32, 37, 55, 77, 151, 156, 180, 185, 191, 202, 224, 229, 246 |
| abstract_inverted_index.on | 73, 219 |
| abstract_inverted_index.we | 66 |
| abstract_inverted_index.245 | 88 |
| abstract_inverted_index.34% | 196 |
| abstract_inverted_index.458 | 78 |
| abstract_inverted_index.CPT | 38, 50, 74, 129, 220 |
| abstract_inverted_index.The | 206 |
| abstract_inverted_index.all | 134 |
| abstract_inverted_index.and | 19, 35, 69, 87, 100, 113, 140, 187, 235 |
| abstract_inverted_index.are | 22 |
| abstract_inverted_index.day | 150, 157 |
| abstract_inverted_index.for | 52, 164 |
| abstract_inverted_index.had | 114, 176 |
| abstract_inverted_index.its | 16 |
| abstract_inverted_index.may | 236 |
| abstract_inverted_index.the | 1, 4, 10, 29, 40, 53, 94, 107, 119, 128, 152, 160, 199, 203, 227, 244 |
| abstract_inverted_index.was | 195 |
| abstract_inverted_index.(213 | 83 |
| abstract_inverted_index.(ML) | 48 |
| abstract_inverted_index.6-12 | 81 |
| abstract_inverted_index.84%. | 192 |
| abstract_inverted_index.87%, | 181 |
| abstract_inverted_index.89%, | 186 |
| abstract_inverted_index.ADHD | 86, 168, 214, 225 |
| abstract_inverted_index.This | 174, 193 |
| abstract_inverted_index.aged | 80 |
| abstract_inverted_index.data | 76, 125 |
| abstract_inverted_index.four | 135, 145 |
| abstract_inverted_index.from | 169 |
| abstract_inverted_index.high | 216 |
| abstract_inverted_index.most | 161 |
| abstract_inverted_index.rate | 179, 184, 190 |
| abstract_inverted_index.some | 27 |
| abstract_inverted_index.test | 7 |
| abstract_inverted_index.than | 118, 198 |
| abstract_inverted_index.that | 97, 106, 122 |
| abstract_inverted_index.time | 155 |
| abstract_inverted_index.used | 93, 123, 238 |
| abstract_inverted_index.week | 153 |
| abstract_inverted_index.well | 143 |
| abstract_inverted_index.with | 85, 167, 213, 215 |
| abstract_inverted_index.(CPT) | 8 |
| abstract_inverted_index.(that | 132 |
| abstract_inverted_index.ADHD. | 247 |
| abstract_inverted_index.Using | 127 |
| abstract_inverted_index.[age, | 148 |
| abstract_inverted_index.based | 218 |
| abstract_inverted_index.could | 210 |
| abstract_inverted_index.data, | 39 |
| abstract_inverted_index.holds | 226 |
| abstract_inverted_index.known | 30 |
| abstract_inverted_index.model | 47, 110, 175, 209, 223 |
| abstract_inverted_index.only. | 126 |
| abstract_inverted_index.score | 131 |
| abstract_inverted_index.still | 23 |
| abstract_inverted_index.study | 42 |
| abstract_inverted_index.their | 170 |
| abstract_inverted_index.total | 130 |
| abstract_inverted_index.using | 49 |
| abstract_inverted_index.years | 82 |
| abstract_inverted_index.(DoW), | 154 |
| abstract_inverted_index.better | 112 |
| abstract_inverted_index.higher | 116, 197 |
| abstract_inverted_index.model. | 205 |
| abstract_inverted_index.models | 72 |
| abstract_inverted_index.peers. | 173 |
| abstract_inverted_index.showed | 105 |
| abstract_inverted_index.tested | 70 |
| abstract_inverted_index.visual | 99 |
| abstract_inverted_index.(ADHD), | 15 |
| abstract_inverted_index.(ToD)], | 158 |
| abstract_inverted_index.Despite | 0 |
| abstract_inverted_index.Results | 104 |
| abstract_inverted_index.address | 26 |
| abstract_inverted_index.applied | 43 |
| abstract_inverted_index.control | 146 |
| abstract_inverted_index.gender, | 149 |
| abstract_inverted_index.indices | 51 |
| abstract_inverted_index.machine | 45 |
| abstract_inverted_index.measure | 242 |
| abstract_inverted_index.perhaps | 231 |
| abstract_inverted_index.present | 41 |
| abstract_inverted_index.promise | 228 |
| abstract_inverted_index.salient | 162 |
| abstract_inverted_index.stimuli | 102 |
| abstract_inverted_index.version | 96 |
| abstract_inverted_index.MOXO-CPT | 95 |
| abstract_inverted_index.accuracy | 117, 178, 201, 217 |
| abstract_inverted_index.analysis | 34 |
| abstract_inverted_index.approach | 121 |
| abstract_inverted_index.auditory | 101 |
| abstract_inverted_index.children | 79, 84, 166, 212 |
| abstract_inverted_index.classify | 211 |
| abstract_inverted_index.clinical | 124 |
| abstract_inverted_index.debated. | 24 |
| abstract_inverted_index.disorder | 14 |
| abstract_inverted_index.fitting, | 60 |
| abstract_inverted_index.followed | 61 |
| abstract_inverted_index.included | 98, 133 |
| abstract_inverted_index.indices: | 136 |
| abstract_inverted_index.learning | 71 |
| abstract_inverted_index.proposed | 109 |
| abstract_inverted_index.provided | 159 |
| abstract_inverted_index.trained, | 67 |
| abstract_inverted_index.validity | 21 |
| abstract_inverted_index.benchmark | 120, 204 |
| abstract_inverted_index.bootstrap | 64 |
| abstract_inverted_index.detection | 208 |
| abstract_inverted_index.developed | 90, 172 |
| abstract_inverted_index.diagnosis | 11 |
| abstract_inverted_index.factorial | 59 |
| abstract_inverted_index.performed | 111 |
| abstract_inverted_index.typically | 89, 171 |
| abstract_inverted_index.variables | 147 |
| abstract_inverted_index.ADHD.Using | 56 |
| abstract_inverted_index.Attention, | 137 |
| abstract_inverted_index.Prediction | 54 |
| abstract_inverted_index.assessment | 234 |
| abstract_inverted_index.behavioral | 233 |
| abstract_inverted_index.children). | 91 |
| abstract_inverted_index.continuous | 5 |
| abstract_inverted_index.ecological | 20 |
| abstract_inverted_index.enhancing, | 230 |
| abstract_inverted_index.evaluation | 245 |
| abstract_inverted_index.popularity | 2 |
| abstract_inverted_index.supportive | 241 |
| abstract_inverted_index.technique, | 65 |
| abstract_inverted_index.Timeliness, | 138 |
| abstract_inverted_index.information | 163 |
| abstract_inverted_index.performance | 6, 75, 194 |
| abstract_inverted_index.sensitivity | 183 |
| abstract_inverted_index.specificity | 189 |
| abstract_inverted_index.traditional | 33 |
| abstract_inverted_index.distractors. | 103 |
| abstract_inverted_index.performance. | 221 |
| abstract_inverted_index.sensitivity, | 18 |
| abstract_inverted_index.shortcomings | 31 |
| abstract_inverted_index.specificity, | 17 |
| abstract_inverted_index.best-achieved | 200 |
| abstract_inverted_index.retrospective | 58 |
| abstract_inverted_index.Hyperactivity, | 139 |
| abstract_inverted_index.complementing, | 232 |
| abstract_inverted_index.discriminating | 165 |
| abstract_inverted_index.interpretation | 36 |
| abstract_inverted_index.learning-based | 46 |
| abstract_inverted_index.Impulsiveness), | 141 |
| abstract_inverted_index.cross-validated, | 68 |
| abstract_inverted_index.attention-deficit/hyperactivity | 13 |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 97 |
| corresponding_author_ids | https://openalex.org/A5072437649 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| corresponding_institution_ids | https://openalex.org/I124227911 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/10 |
| sustainable_development_goals[0].score | 0.49000000953674316 |
| sustainable_development_goals[0].display_name | Reduced inequalities |
| citation_normalized_percentile.value | 0.95276281 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |