Heart Block Identification from 12-Lead ECG: Exploring the Generalizability of Self-Supervised AI Article Swipe
Teya S. Bergamaschi
,
Collin M. Stultz
,
Ridwan Alam
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1101/2024.10.15.24315546
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1101/2024.10.15.24315546
Timely diagnosis and treatment of heart blocks are critical for preventing fatal outcomes in patients with cardiac conduction disorders. Expert analysis of clinical 12-lead electro-cardiograms (ECG) remains the standard diagnosis apparatus. In this study, we propose a self-supervised deep learning model that can detect evidence of heart blocks in ECG signals. We build a multichannel residual neural network (KRes) and train this model in a self-supervised contrastive fashion leveraging 3.6 million ECG from patients at the Massachusetts General Hospital (MGH) to learn robust ECG representations. We evaluate the utility of such representations on a large public dataset, PTB-XL, through fine-tuning and linear probing for identifying heart blocks. To compare, we also build a baseline supervised model by adapting KRes. The training data contains a subset of 10.6 thousand PTB-XL ECG from patients with and without heart block patterns, and a holdout set of 1319 ECG is used for evaluation. We compare the performances of the finetuning and the linear probing of the self-supervised representations with that of the supervised model using the area under the receiver-operating curve (AUC), sensitivity, specificity, and predictive values, and found those performing equally well. Moreover, we repeat the training cycles of the three pipelines while reducing the number of training samples and demonstrate that the performance of the self-supervised representations remain steady even when labeled data is limited. This research underscores the potential of self-supervised learning in cardiac diagnostics, emphasizing generalizability and performance across diverse datasets and clinical settings, especially in data-scarce paradigms.
Related Topics
Concepts
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2024.10.15.24315546
- https://www.medrxiv.org/content/medrxiv/early/2024/10/17/2024.10.15.24315546.full.pdf
- OA Status
- green
- References
- 16
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4403552331
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4403552331Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1101/2024.10.15.24315546Digital Object Identifier
- Title
-
Heart Block Identification from 12-Lead ECG: Exploring the Generalizability of Self-Supervised AIWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-10-17Full publication date if available
- Authors
-
Teya S. Bergamaschi, Collin M. Stultz, Ridwan AlamList of authors in order
- Landing page
-
https://doi.org/10.1101/2024.10.15.24315546Publisher landing page
- PDF URL
-
https://www.medrxiv.org/content/medrxiv/early/2024/10/17/2024.10.15.24315546.full.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.medrxiv.org/content/medrxiv/early/2024/10/17/2024.10.15.24315546.full.pdfDirect OA link when available
- Concepts
-
Generalizability theory, Identification (biology), Lead (geology), Block (permutation group theory), Computer science, Artificial intelligence, Psychology, Mathematics, Biology, Developmental psychology, Paleontology, Botany, GeometryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
16Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4403552331 |
|---|---|
| doi | https://doi.org/10.1101/2024.10.15.24315546 |
| ids.doi | https://doi.org/10.1101/2024.10.15.24315546 |
| ids.openalex | https://openalex.org/W4403552331 |
| fwci | 0.0 |
| type | preprint |
| title | Heart Block Identification from 12-Lead ECG: Exploring the Generalizability of Self-Supervised AI |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T12702 |
| topics[0].field.id | https://openalex.org/fields/28 |
| topics[0].field.display_name | Neuroscience |
| topics[0].score | 0.4339999854564667 |
| topics[0].domain.id | https://openalex.org/domains/1 |
| topics[0].domain.display_name | Life Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2808 |
| topics[0].subfield.display_name | Neurology |
| topics[0].display_name | Brain Tumor Detection and Classification |
| topics[1].id | https://openalex.org/T11021 |
| topics[1].field.id | https://openalex.org/fields/27 |
| topics[1].field.display_name | Medicine |
| topics[1].score | 0.4268999993801117 |
| topics[1].domain.id | https://openalex.org/domains/4 |
| topics[1].domain.display_name | Health Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2705 |
| topics[1].subfield.display_name | Cardiology and Cardiovascular Medicine |
| topics[1].display_name | ECG Monitoring and Analysis |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C27158222 |
| concepts[0].level | 2 |
| concepts[0].score | 0.8350719213485718 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q5532422 |
| concepts[0].display_name | Generalizability theory |
| concepts[1].id | https://openalex.org/C116834253 |
| concepts[1].level | 2 |
| concepts[1].score | 0.7455959320068359 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q2039217 |
| concepts[1].display_name | Identification (biology) |
| concepts[2].id | https://openalex.org/C2777093003 |
| concepts[2].level | 2 |
| concepts[2].score | 0.7334508895874023 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q6508345 |
| concepts[2].display_name | Lead (geology) |
| concepts[3].id | https://openalex.org/C2777210771 |
| concepts[3].level | 2 |
| concepts[3].score | 0.5657399892807007 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q4927124 |
| concepts[3].display_name | Block (permutation group theory) |
| concepts[4].id | https://openalex.org/C41008148 |
| concepts[4].level | 0 |
| concepts[4].score | 0.4092258810997009 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[4].display_name | Computer science |
| concepts[5].id | https://openalex.org/C154945302 |
| concepts[5].level | 1 |
| concepts[5].score | 0.4090760350227356 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[5].display_name | Artificial intelligence |
| concepts[6].id | https://openalex.org/C15744967 |
| concepts[6].level | 0 |
| concepts[6].score | 0.4017086625099182 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q9418 |
| concepts[6].display_name | Psychology |
| concepts[7].id | https://openalex.org/C33923547 |
| concepts[7].level | 0 |
| concepts[7].score | 0.15125256776809692 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[7].display_name | Mathematics |
| concepts[8].id | https://openalex.org/C86803240 |
| concepts[8].level | 0 |
| concepts[8].score | 0.07867541909217834 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q420 |
| concepts[8].display_name | Biology |
| concepts[9].id | https://openalex.org/C138496976 |
| concepts[9].level | 1 |
| concepts[9].score | 0.06957241892814636 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q175002 |
| concepts[9].display_name | Developmental psychology |
| concepts[10].id | https://openalex.org/C151730666 |
| concepts[10].level | 1 |
| concepts[10].score | 0.0 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q7205 |
| concepts[10].display_name | Paleontology |
| concepts[11].id | https://openalex.org/C59822182 |
| concepts[11].level | 1 |
| concepts[11].score | 0.0 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q441 |
| concepts[11].display_name | Botany |
| concepts[12].id | https://openalex.org/C2524010 |
| concepts[12].level | 1 |
| concepts[12].score | 0.0 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q8087 |
| concepts[12].display_name | Geometry |
| keywords[0].id | https://openalex.org/keywords/generalizability-theory |
| keywords[0].score | 0.8350719213485718 |
| keywords[0].display_name | Generalizability theory |
| keywords[1].id | https://openalex.org/keywords/identification |
| keywords[1].score | 0.7455959320068359 |
| keywords[1].display_name | Identification (biology) |
| keywords[2].id | https://openalex.org/keywords/lead |
| keywords[2].score | 0.7334508895874023 |
| keywords[2].display_name | Lead (geology) |
| keywords[3].id | https://openalex.org/keywords/block |
| keywords[3].score | 0.5657399892807007 |
| keywords[3].display_name | Block (permutation group theory) |
| keywords[4].id | https://openalex.org/keywords/computer-science |
| keywords[4].score | 0.4092258810997009 |
| keywords[4].display_name | Computer science |
| keywords[5].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[5].score | 0.4090760350227356 |
| keywords[5].display_name | Artificial intelligence |
| keywords[6].id | https://openalex.org/keywords/psychology |
| keywords[6].score | 0.4017086625099182 |
| keywords[6].display_name | Psychology |
| keywords[7].id | https://openalex.org/keywords/mathematics |
| keywords[7].score | 0.15125256776809692 |
| keywords[7].display_name | Mathematics |
| keywords[8].id | https://openalex.org/keywords/biology |
| keywords[8].score | 0.07867541909217834 |
| keywords[8].display_name | Biology |
| keywords[9].id | https://openalex.org/keywords/developmental-psychology |
| keywords[9].score | 0.06957241892814636 |
| keywords[9].display_name | Developmental psychology |
| language | en |
| locations[0].id | doi:10.1101/2024.10.15.24315546 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4306402567 |
| locations[0].source.issn | |
| locations[0].source.type | repository |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | bioRxiv (Cold Spring Harbor Laboratory) |
| locations[0].source.host_organization | https://openalex.org/I2750212522 |
| locations[0].source.host_organization_name | Cold Spring Harbor Laboratory |
| locations[0].source.host_organization_lineage | https://openalex.org/I2750212522 |
| locations[0].license | cc-by-nc-nd |
| locations[0].pdf_url | https://www.medrxiv.org/content/medrxiv/early/2024/10/17/2024.10.15.24315546.full.pdf |
| locations[0].version | acceptedVersion |
| locations[0].raw_type | posted-content |
| locations[0].license_id | https://openalex.org/licenses/cc-by-nc-nd |
| locations[0].is_accepted | True |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | https://doi.org/10.1101/2024.10.15.24315546 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5040571795 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-1988-3478 |
| authorships[0].author.display_name | Teya S. Bergamaschi |
| authorships[0].countries | US |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I63966007 |
| authorships[0].affiliations[0].raw_affiliation_string | Massachusetts Institute of Technology |
| authorships[0].institutions[0].id | https://openalex.org/I63966007 |
| authorships[0].institutions[0].ror | https://ror.org/042nb2s44 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I63966007 |
| authorships[0].institutions[0].country_code | US |
| authorships[0].institutions[0].display_name | Massachusetts Institute of Technology |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Teya S. Bergamaschi |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Massachusetts Institute of Technology |
| authorships[1].author.id | https://openalex.org/A5024941370 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-3415-242X |
| authorships[1].author.display_name | Collin M. Stultz |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Collin M. Stultz |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5034393434 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-4332-4051 |
| authorships[2].author.display_name | Ridwan Alam |
| authorships[2].countries | US |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I63966007 |
| authorships[2].affiliations[0].raw_affiliation_string | Massachusetts Institute of Technology |
| authorships[2].institutions[0].id | https://openalex.org/I63966007 |
| authorships[2].institutions[0].ror | https://ror.org/042nb2s44 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I63966007 |
| authorships[2].institutions[0].country_code | US |
| authorships[2].institutions[0].display_name | Massachusetts Institute of Technology |
| authorships[2].author_position | last |
| authorships[2].raw_author_name | Ridwan Alam |
| authorships[2].is_corresponding | True |
| authorships[2].raw_affiliation_strings | Massachusetts Institute of Technology |
| has_content.pdf | True |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://www.medrxiv.org/content/medrxiv/early/2024/10/17/2024.10.15.24315546.full.pdf |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Heart Block Identification from 12-Lead ECG: Exploring the Generalizability of Self-Supervised AI |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T06:51:31.235846 |
| primary_topic.id | https://openalex.org/T12702 |
| primary_topic.field.id | https://openalex.org/fields/28 |
| primary_topic.field.display_name | Neuroscience |
| primary_topic.score | 0.4339999854564667 |
| primary_topic.domain.id | https://openalex.org/domains/1 |
| primary_topic.domain.display_name | Life Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2808 |
| primary_topic.subfield.display_name | Neurology |
| primary_topic.display_name | Brain Tumor Detection and Classification |
| related_works | https://openalex.org/W2118717649, https://openalex.org/W2413243053, https://openalex.org/W410723623, https://openalex.org/W2015341305, https://openalex.org/W2035068594, https://openalex.org/W4225593417, https://openalex.org/W2573498121, https://openalex.org/W3022298670, https://openalex.org/W3160494304, https://openalex.org/W3006162251 |
| cited_by_count | 0 |
| locations_count | 1 |
| best_oa_location.id | doi:10.1101/2024.10.15.24315546 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306402567 |
| 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 | bioRxiv (Cold Spring Harbor Laboratory) |
| best_oa_location.source.host_organization | https://openalex.org/I2750212522 |
| best_oa_location.source.host_organization_name | Cold Spring Harbor Laboratory |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I2750212522 |
| best_oa_location.license | cc-by-nc-nd |
| best_oa_location.pdf_url | https://www.medrxiv.org/content/medrxiv/early/2024/10/17/2024.10.15.24315546.full.pdf |
| best_oa_location.version | acceptedVersion |
| best_oa_location.raw_type | posted-content |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by-nc-nd |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | https://doi.org/10.1101/2024.10.15.24315546 |
| primary_location.id | doi:10.1101/2024.10.15.24315546 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4306402567 |
| primary_location.source.issn | |
| primary_location.source.type | repository |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | bioRxiv (Cold Spring Harbor Laboratory) |
| primary_location.source.host_organization | https://openalex.org/I2750212522 |
| primary_location.source.host_organization_name | Cold Spring Harbor Laboratory |
| primary_location.source.host_organization_lineage | https://openalex.org/I2750212522 |
| primary_location.license | cc-by-nc-nd |
| primary_location.pdf_url | https://www.medrxiv.org/content/medrxiv/early/2024/10/17/2024.10.15.24315546.full.pdf |
| primary_location.version | acceptedVersion |
| primary_location.raw_type | posted-content |
| primary_location.license_id | https://openalex.org/licenses/cc-by-nc-nd |
| primary_location.is_accepted | True |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | https://doi.org/10.1101/2024.10.15.24315546 |
| publication_date | 2024-10-17 |
| publication_year | 2024 |
| referenced_works | https://openalex.org/W4321367639, https://openalex.org/W7054953960, https://openalex.org/W2156272049, https://openalex.org/W3012063106, https://openalex.org/W2902644322, https://openalex.org/W3021394814, https://openalex.org/W4391149661, https://openalex.org/W2114043028, https://openalex.org/W3027572331, https://openalex.org/W3008117014, https://openalex.org/W4399997619, https://openalex.org/W3015226328, https://openalex.org/W3107490265, https://openalex.org/W3138270768, https://openalex.org/W3005680577, https://openalex.org/W3099085560 |
| referenced_works_count | 16 |
| abstract_inverted_index.a | 37, 54, 65, 94, 113, 124, 140 |
| abstract_inverted_index.In | 32 |
| abstract_inverted_index.To | 108 |
| abstract_inverted_index.We | 52, 86, 150 |
| abstract_inverted_index.at | 75 |
| abstract_inverted_index.by | 117 |
| abstract_inverted_index.in | 14, 49, 64, 232, 246 |
| abstract_inverted_index.is | 146, 222 |
| abstract_inverted_index.of | 5, 22, 46, 90, 126, 143, 154, 161, 167, 196, 204, 212, 229 |
| abstract_inverted_index.on | 93 |
| abstract_inverted_index.to | 81 |
| abstract_inverted_index.we | 35, 110, 191 |
| abstract_inverted_index.3.6 | 70 |
| abstract_inverted_index.ECG | 50, 72, 84, 130, 145 |
| abstract_inverted_index.The | 120 |
| abstract_inverted_index.and | 3, 60, 101, 134, 139, 157, 181, 184, 207, 237, 242 |
| abstract_inverted_index.are | 8 |
| abstract_inverted_index.can | 43 |
| abstract_inverted_index.for | 10, 104, 148 |
| abstract_inverted_index.set | 142 |
| abstract_inverted_index.the | 28, 76, 88, 152, 155, 158, 162, 168, 172, 175, 193, 197, 202, 210, 213, 227 |
| abstract_inverted_index.10.6 | 127 |
| abstract_inverted_index.1319 | 144 |
| abstract_inverted_index.This | 224 |
| abstract_inverted_index.also | 111 |
| abstract_inverted_index.area | 173 |
| abstract_inverted_index.data | 122, 221 |
| abstract_inverted_index.deep | 39 |
| abstract_inverted_index.even | 218 |
| abstract_inverted_index.from | 73, 131 |
| abstract_inverted_index.such | 91 |
| abstract_inverted_index.that | 42, 166, 209 |
| abstract_inverted_index.this | 33, 62 |
| abstract_inverted_index.used | 147 |
| abstract_inverted_index.when | 219 |
| abstract_inverted_index.with | 16, 133, 165 |
| abstract_inverted_index.(ECG) | 26 |
| abstract_inverted_index.(MGH) | 80 |
| abstract_inverted_index.KRes. | 119 |
| abstract_inverted_index.block | 137 |
| abstract_inverted_index.build | 53, 112 |
| abstract_inverted_index.curve | 177 |
| abstract_inverted_index.fatal | 12 |
| abstract_inverted_index.found | 185 |
| abstract_inverted_index.heart | 6, 47, 106, 136 |
| abstract_inverted_index.large | 95 |
| abstract_inverted_index.learn | 82 |
| abstract_inverted_index.model | 41, 63, 116, 170 |
| abstract_inverted_index.those | 186 |
| abstract_inverted_index.three | 198 |
| abstract_inverted_index.train | 61 |
| abstract_inverted_index.under | 174 |
| abstract_inverted_index.using | 171 |
| abstract_inverted_index.well. | 189 |
| abstract_inverted_index.while | 200 |
| abstract_inverted_index.(AUC), | 178 |
| abstract_inverted_index.(KRes) | 59 |
| abstract_inverted_index.Expert | 20 |
| abstract_inverted_index.PTB-XL | 129 |
| abstract_inverted_index.Timely | 1 |
| abstract_inverted_index.across | 239 |
| abstract_inverted_index.blocks | 7, 48 |
| abstract_inverted_index.cycles | 195 |
| abstract_inverted_index.detect | 44 |
| abstract_inverted_index.linear | 102, 159 |
| abstract_inverted_index.neural | 57 |
| abstract_inverted_index.number | 203 |
| abstract_inverted_index.public | 96 |
| abstract_inverted_index.remain | 216 |
| abstract_inverted_index.repeat | 192 |
| abstract_inverted_index.robust | 83 |
| abstract_inverted_index.steady | 217 |
| abstract_inverted_index.study, | 34 |
| abstract_inverted_index.subset | 125 |
| abstract_inverted_index.12-lead | 24 |
| abstract_inverted_index.General | 78 |
| abstract_inverted_index.PTB-XL, | 98 |
| abstract_inverted_index.blocks. | 107 |
| abstract_inverted_index.cardiac | 17, 233 |
| abstract_inverted_index.compare | 151 |
| abstract_inverted_index.diverse | 240 |
| abstract_inverted_index.equally | 188 |
| abstract_inverted_index.fashion | 68 |
| abstract_inverted_index.holdout | 141 |
| abstract_inverted_index.labeled | 220 |
| abstract_inverted_index.million | 71 |
| abstract_inverted_index.network | 58 |
| abstract_inverted_index.probing | 103, 160 |
| abstract_inverted_index.propose | 36 |
| abstract_inverted_index.remains | 27 |
| abstract_inverted_index.samples | 206 |
| abstract_inverted_index.through | 99 |
| abstract_inverted_index.utility | 89 |
| abstract_inverted_index.values, | 183 |
| abstract_inverted_index.without | 135 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Hospital | 79 |
| abstract_inverted_index.adapting | 118 |
| abstract_inverted_index.analysis | 21 |
| abstract_inverted_index.baseline | 114 |
| abstract_inverted_index.clinical | 23, 243 |
| abstract_inverted_index.compare, | 109 |
| abstract_inverted_index.contains | 123 |
| abstract_inverted_index.critical | 9 |
| abstract_inverted_index.dataset, | 97 |
| abstract_inverted_index.datasets | 241 |
| abstract_inverted_index.evaluate | 87 |
| abstract_inverted_index.evidence | 45 |
| abstract_inverted_index.learning | 40, 231 |
| abstract_inverted_index.limited. | 223 |
| abstract_inverted_index.outcomes | 13 |
| abstract_inverted_index.patients | 15, 74, 132 |
| abstract_inverted_index.reducing | 201 |
| abstract_inverted_index.research | 225 |
| abstract_inverted_index.residual | 56 |
| abstract_inverted_index.signals. | 51 |
| abstract_inverted_index.standard | 29 |
| abstract_inverted_index.thousand | 128 |
| abstract_inverted_index.training | 121, 194, 205 |
| abstract_inverted_index.Moreover, | 190 |
| abstract_inverted_index.diagnosis | 2, 30 |
| abstract_inverted_index.patterns, | 138 |
| abstract_inverted_index.pipelines | 199 |
| abstract_inverted_index.potential | 228 |
| abstract_inverted_index.settings, | 244 |
| abstract_inverted_index.treatment | 4 |
| abstract_inverted_index.apparatus. | 31 |
| abstract_inverted_index.conduction | 18 |
| abstract_inverted_index.disorders. | 19 |
| abstract_inverted_index.especially | 245 |
| abstract_inverted_index.finetuning | 156 |
| abstract_inverted_index.leveraging | 69 |
| abstract_inverted_index.paradigms. | 248 |
| abstract_inverted_index.performing | 187 |
| abstract_inverted_index.predictive | 182 |
| abstract_inverted_index.preventing | 11 |
| abstract_inverted_index.supervised | 115, 169 |
| abstract_inverted_index.contrastive | 67 |
| abstract_inverted_index.data-scarce | 247 |
| abstract_inverted_index.demonstrate | 208 |
| abstract_inverted_index.emphasizing | 235 |
| abstract_inverted_index.evaluation. | 149 |
| abstract_inverted_index.fine-tuning | 100 |
| abstract_inverted_index.identifying | 105 |
| abstract_inverted_index.performance | 211, 238 |
| abstract_inverted_index.underscores | 226 |
| abstract_inverted_index.diagnostics, | 234 |
| abstract_inverted_index.multichannel | 55 |
| abstract_inverted_index.performances | 153 |
| abstract_inverted_index.sensitivity, | 179 |
| abstract_inverted_index.specificity, | 180 |
| abstract_inverted_index.Massachusetts | 77 |
| abstract_inverted_index.representations | 92, 164, 215 |
| abstract_inverted_index.self-supervised | 38, 66, 163, 214, 230 |
| abstract_inverted_index.generalizability | 236 |
| abstract_inverted_index.representations. | 85 |
| abstract_inverted_index.receiver-operating | 176 |
| abstract_inverted_index.electro-cardiograms | 25 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5034393434 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| corresponding_institution_ids | https://openalex.org/I63966007 |
| citation_normalized_percentile.value | 0.23633024 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |