SALTClass: classifying clinical short notes using background knowledge from unlabeled data Article Swipe
Ayoub Bagheri
,
Daniel L. Oberski
,
Arjan Sammani
,
P.G.M. van der Heijden
,
Folkert W. Asselbergs
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1101/801944
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1101/801944
Background With the increasing use of unstructured text in electronic health records, extracting useful related information has become a necessity. Text classification can be applied to extract patients’ medical history from clinical notes. However, the sparsity in clinical short notes, that is, excessively small word counts in the text, can lead to large classification errors. Previous studies demonstrated that natural language processing (NLP) can be useful in the text classification of clinical outcomes. We propose incorporating the knowledge from unlabeled data, as this may alleviate the problem of short noisy sparse text. Results The software package SALTClass (short and long text classifier) is a machine learning NLP toolkit. It uses seven clustering algorithms, namely, latent Dirichlet allocation, K-Means, MiniBatchK-Means, BIRCH, MeanShift, DBScan, and GMM. Smoothing methods are applied to the resulting cluster information to enrich the representation of sparse text. For the subsequent prediction step, SALTClass can be used on either the original document-term matrix or in an enrichment pipeline. To this end, ten different supervised classifiers have also been integrated into SALTClass. We demonstrate the effectiveness of the SALTClass NLP toolkit in the identification of patients’ family history in a Dutch clinical cardiovascular text corpus from University Medical Center Utrecht, the Netherlands. Conclusions The considerable amount of unstructured short text in healthcare applications, particularly in clinical cardiovascular notes, has created an urgent need for tools that can parse specific information from text reports. Using machine learning algorithms for enriching short text can improve the representation for further applications. Availability SALTClass can be downloaded as a Python package from Python Package Index (PyPI) website at https://pypi.org/project/saltclass and from GitHub at https://github.com/bagheria/saltclass .
Related Topics
Concepts
Computer science
Artificial intelligence
Latent Dirichlet allocation
Natural language processing
Machine learning
Parsing
Cluster analysis
Smoothing
Text mining
Classifier (UML)
Topic model
Pipeline (software)
Unstructured data
Information retrieval
Data mining
Big data
Programming language
Computer vision
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/801944
- https://www.biorxiv.org/content/biorxiv/early/2019/10/13/801944.full.pdf
- OA Status
- green
- Cited By
- 2
- References
- 26
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2979865607
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W2979865607Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1101/801944Digital Object Identifier
- Title
-
SALTClass: classifying clinical short notes using background knowledge from unlabeled dataWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2019Year of publication
- Publication date
-
2019-10-13Full publication date if available
- Authors
-
Ayoub Bagheri, Daniel L. Oberski, Arjan Sammani, P.G.M. van der Heijden, Folkert W. AsselbergsList of authors in order
- Landing page
-
https://doi.org/10.1101/801944Publisher landing page
- PDF URL
-
https://www.biorxiv.org/content/biorxiv/early/2019/10/13/801944.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.biorxiv.org/content/biorxiv/early/2019/10/13/801944.full.pdfDirect OA link when available
- Concepts
-
Computer science, Artificial intelligence, Latent Dirichlet allocation, Natural language processing, Machine learning, Parsing, Cluster analysis, Smoothing, Text mining, Classifier (UML), Topic model, Pipeline (software), Unstructured data, Information retrieval, Data mining, Big data, Programming language, Computer visionTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
-
2022: 1, 2021: 1Per-year citation counts (last 5 years)
- References (count)
-
26Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W2979865607 |
|---|---|
| doi | https://doi.org/10.1101/801944 |
| ids.doi | https://doi.org/10.1101/801944 |
| ids.mag | 2979865607 |
| ids.openalex | https://openalex.org/W2979865607 |
| fwci | 0.30723549 |
| type | preprint |
| title | SALTClass: classifying clinical short notes using background knowledge from unlabeled data |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T10028 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.9984999895095825 |
| 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 | Topic Modeling |
| topics[1].id | https://openalex.org/T11710 |
| topics[1].field.id | https://openalex.org/fields/13 |
| topics[1].field.display_name | Biochemistry, Genetics and Molecular Biology |
| topics[1].score | 0.9983999729156494 |
| topics[1].domain.id | https://openalex.org/domains/1 |
| topics[1].domain.display_name | Life Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1312 |
| topics[1].subfield.display_name | Molecular Biology |
| topics[1].display_name | Biomedical Text Mining and Ontologies |
| topics[2].id | https://openalex.org/T13702 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.9958999752998352 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1702 |
| topics[2].subfield.display_name | Artificial Intelligence |
| topics[2].display_name | Machine Learning in Healthcare |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C41008148 |
| concepts[0].level | 0 |
| concepts[0].score | 0.7574083805084229 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[0].display_name | Computer science |
| concepts[1].id | https://openalex.org/C154945302 |
| concepts[1].level | 1 |
| concepts[1].score | 0.7319802641868591 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[1].display_name | Artificial intelligence |
| concepts[2].id | https://openalex.org/C500882744 |
| concepts[2].level | 3 |
| concepts[2].score | 0.5858718752861023 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q269236 |
| concepts[2].display_name | Latent Dirichlet allocation |
| concepts[3].id | https://openalex.org/C204321447 |
| concepts[3].level | 1 |
| concepts[3].score | 0.5481869578361511 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q30642 |
| concepts[3].display_name | Natural language processing |
| concepts[4].id | https://openalex.org/C119857082 |
| concepts[4].level | 1 |
| concepts[4].score | 0.5466884970664978 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[4].display_name | Machine learning |
| concepts[5].id | https://openalex.org/C186644900 |
| concepts[5].level | 2 |
| concepts[5].score | 0.5318825840950012 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q194152 |
| concepts[5].display_name | Parsing |
| concepts[6].id | https://openalex.org/C73555534 |
| concepts[6].level | 2 |
| concepts[6].score | 0.5180121064186096 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q622825 |
| concepts[6].display_name | Cluster analysis |
| concepts[7].id | https://openalex.org/C3770464 |
| concepts[7].level | 2 |
| concepts[7].score | 0.5052927136421204 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q775963 |
| concepts[7].display_name | Smoothing |
| concepts[8].id | https://openalex.org/C71472368 |
| concepts[8].level | 2 |
| concepts[8].score | 0.49259620904922485 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q676880 |
| concepts[8].display_name | Text mining |
| concepts[9].id | https://openalex.org/C95623464 |
| concepts[9].level | 2 |
| concepts[9].score | 0.47386980056762695 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q1096149 |
| concepts[9].display_name | Classifier (UML) |
| concepts[10].id | https://openalex.org/C171686336 |
| concepts[10].level | 2 |
| concepts[10].score | 0.4486515522003174 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q3532085 |
| concepts[10].display_name | Topic model |
| concepts[11].id | https://openalex.org/C43521106 |
| concepts[11].level | 2 |
| concepts[11].score | 0.4394330084323883 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q2165493 |
| concepts[11].display_name | Pipeline (software) |
| concepts[12].id | https://openalex.org/C2781252014 |
| concepts[12].level | 3 |
| concepts[12].score | 0.433121919631958 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q1141900 |
| concepts[12].display_name | Unstructured data |
| concepts[13].id | https://openalex.org/C23123220 |
| concepts[13].level | 1 |
| concepts[13].score | 0.3542391359806061 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q816826 |
| concepts[13].display_name | Information retrieval |
| concepts[14].id | https://openalex.org/C124101348 |
| concepts[14].level | 1 |
| concepts[14].score | 0.2513120174407959 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q172491 |
| concepts[14].display_name | Data mining |
| concepts[15].id | https://openalex.org/C75684735 |
| concepts[15].level | 2 |
| concepts[15].score | 0.1486443281173706 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q858810 |
| concepts[15].display_name | Big data |
| concepts[16].id | https://openalex.org/C199360897 |
| concepts[16].level | 1 |
| concepts[16].score | 0.0 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q9143 |
| concepts[16].display_name | Programming language |
| concepts[17].id | https://openalex.org/C31972630 |
| concepts[17].level | 1 |
| concepts[17].score | 0.0 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q844240 |
| concepts[17].display_name | Computer vision |
| keywords[0].id | https://openalex.org/keywords/computer-science |
| keywords[0].score | 0.7574083805084229 |
| keywords[0].display_name | Computer science |
| keywords[1].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[1].score | 0.7319802641868591 |
| keywords[1].display_name | Artificial intelligence |
| keywords[2].id | https://openalex.org/keywords/latent-dirichlet-allocation |
| keywords[2].score | 0.5858718752861023 |
| keywords[2].display_name | Latent Dirichlet allocation |
| keywords[3].id | https://openalex.org/keywords/natural-language-processing |
| keywords[3].score | 0.5481869578361511 |
| keywords[3].display_name | Natural language processing |
| keywords[4].id | https://openalex.org/keywords/machine-learning |
| keywords[4].score | 0.5466884970664978 |
| keywords[4].display_name | Machine learning |
| keywords[5].id | https://openalex.org/keywords/parsing |
| keywords[5].score | 0.5318825840950012 |
| keywords[5].display_name | Parsing |
| keywords[6].id | https://openalex.org/keywords/cluster-analysis |
| keywords[6].score | 0.5180121064186096 |
| keywords[6].display_name | Cluster analysis |
| keywords[7].id | https://openalex.org/keywords/smoothing |
| keywords[7].score | 0.5052927136421204 |
| keywords[7].display_name | Smoothing |
| keywords[8].id | https://openalex.org/keywords/text-mining |
| keywords[8].score | 0.49259620904922485 |
| keywords[8].display_name | Text mining |
| keywords[9].id | https://openalex.org/keywords/classifier |
| keywords[9].score | 0.47386980056762695 |
| keywords[9].display_name | Classifier (UML) |
| keywords[10].id | https://openalex.org/keywords/topic-model |
| keywords[10].score | 0.4486515522003174 |
| keywords[10].display_name | Topic model |
| keywords[11].id | https://openalex.org/keywords/pipeline |
| keywords[11].score | 0.4394330084323883 |
| keywords[11].display_name | Pipeline (software) |
| keywords[12].id | https://openalex.org/keywords/unstructured-data |
| keywords[12].score | 0.433121919631958 |
| keywords[12].display_name | Unstructured data |
| keywords[13].id | https://openalex.org/keywords/information-retrieval |
| keywords[13].score | 0.3542391359806061 |
| keywords[13].display_name | Information retrieval |
| keywords[14].id | https://openalex.org/keywords/data-mining |
| keywords[14].score | 0.2513120174407959 |
| keywords[14].display_name | Data mining |
| keywords[15].id | https://openalex.org/keywords/big-data |
| keywords[15].score | 0.1486443281173706 |
| keywords[15].display_name | Big data |
| language | en |
| locations[0].id | doi:10.1101/801944 |
| 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.biorxiv.org/content/biorxiv/early/2019/10/13/801944.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/10.1101/801944 |
| locations[1].id | pmh:uu:oai:dspace.library.uu.nl:1874/390877 |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S4306401843 |
| 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 | Data Archiving and Networked Services (DANS) |
| locations[1].source.host_organization | https://openalex.org/I1322597698 |
| locations[1].source.host_organization_name | Royal Netherlands Academy of Arts and Sciences |
| locations[1].source.host_organization_lineage | https://openalex.org/I1322597698 |
| locations[1].license | other-oa |
| locations[1].pdf_url | |
| locations[1].version | submittedVersion |
| locations[1].raw_type | info:eu-repo/semantics/lecture |
| locations[1].license_id | https://openalex.org/licenses/other-oa |
| locations[1].is_accepted | False |
| locations[1].is_published | False |
| locations[1].raw_source_name | |
| locations[1].landing_page_url | https://dspace.library.uu.nl/handle/1874/390877 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5047736253 |
| authorships[0].author.orcid | https://orcid.org/0000-0001-6366-2173 |
| authorships[0].author.display_name | Ayoub Bagheri |
| authorships[0].countries | NL |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I193662353, https://openalex.org/I3018483916 |
| authorships[0].affiliations[0].raw_affiliation_string | Department of Cardiology, Division of Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands |
| authorships[0].affiliations[1].institution_ids | https://openalex.org/I193662353 |
| authorships[0].affiliations[1].raw_affiliation_string | Department of Methodology and Statistics, Faculty of Social Sciences, Utrecht University, Utrecht, the Netherlands |
| authorships[0].institutions[0].id | https://openalex.org/I3018483916 |
| authorships[0].institutions[0].ror | https://ror.org/0575yy874 |
| authorships[0].institutions[0].type | healthcare |
| authorships[0].institutions[0].lineage | https://openalex.org/I3018483916 |
| authorships[0].institutions[0].country_code | NL |
| authorships[0].institutions[0].display_name | University Medical Center Utrecht |
| authorships[0].institutions[1].id | https://openalex.org/I193662353 |
| authorships[0].institutions[1].ror | https://ror.org/04pp8hn57 |
| authorships[0].institutions[1].type | education |
| authorships[0].institutions[1].lineage | https://openalex.org/I193662353 |
| authorships[0].institutions[1].country_code | NL |
| authorships[0].institutions[1].display_name | Utrecht University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Ayoub Bagheri |
| authorships[0].is_corresponding | True |
| authorships[0].raw_affiliation_strings | Department of Cardiology, Division of Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands, Department of Methodology and Statistics, Faculty of Social Sciences, Utrecht University, Utrecht, the Netherlands |
| authorships[1].author.id | https://openalex.org/A5061913766 |
| authorships[1].author.orcid | https://orcid.org/0000-0001-7467-2297 |
| authorships[1].author.display_name | Daniel L. Oberski |
| authorships[1].countries | NL |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I193662353 |
| authorships[1].affiliations[0].raw_affiliation_string | Department of Methodology and Statistics, Faculty of Social Sciences, Utrecht University, Utrecht, the Netherlands |
| authorships[1].institutions[0].id | https://openalex.org/I193662353 |
| authorships[1].institutions[0].ror | https://ror.org/04pp8hn57 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I193662353 |
| authorships[1].institutions[0].country_code | NL |
| authorships[1].institutions[0].display_name | Utrecht University |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Daniel Oberski |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Department of Methodology and Statistics, Faculty of Social Sciences, Utrecht University, Utrecht, the Netherlands |
| authorships[2].author.id | https://openalex.org/A5056798683 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-5557-9342 |
| authorships[2].author.display_name | Arjan Sammani |
| authorships[2].countries | NL |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I193662353, https://openalex.org/I3018483916 |
| authorships[2].affiliations[0].raw_affiliation_string | Department of Cardiology, Division of Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands |
| authorships[2].institutions[0].id | https://openalex.org/I3018483916 |
| authorships[2].institutions[0].ror | https://ror.org/0575yy874 |
| authorships[2].institutions[0].type | healthcare |
| authorships[2].institutions[0].lineage | https://openalex.org/I3018483916 |
| authorships[2].institutions[0].country_code | NL |
| authorships[2].institutions[0].display_name | University Medical Center Utrecht |
| authorships[2].institutions[1].id | https://openalex.org/I193662353 |
| authorships[2].institutions[1].ror | https://ror.org/04pp8hn57 |
| authorships[2].institutions[1].type | education |
| authorships[2].institutions[1].lineage | https://openalex.org/I193662353 |
| authorships[2].institutions[1].country_code | NL |
| authorships[2].institutions[1].display_name | Utrecht University |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Arjan Sammani |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Department of Cardiology, Division of Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands |
| authorships[3].author.id | https://openalex.org/A5090384758 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-3345-096X |
| authorships[3].author.display_name | P.G.M. van der Heijden |
| authorships[3].countries | GB, NL |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I43439940 |
| authorships[3].affiliations[0].raw_affiliation_string | Southampton Statistical Sciences Research Institute, University of Southampton, Southampton, United Kingdom |
| authorships[3].affiliations[1].institution_ids | https://openalex.org/I193662353 |
| authorships[3].affiliations[1].raw_affiliation_string | Department of Methodology and Statistics, Faculty of Social Sciences, Utrecht University, Utrecht, the Netherlands |
| authorships[3].institutions[0].id | https://openalex.org/I43439940 |
| authorships[3].institutions[0].ror | https://ror.org/01ryk1543 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I43439940 |
| authorships[3].institutions[0].country_code | GB |
| authorships[3].institutions[0].display_name | University of Southampton |
| authorships[3].institutions[1].id | https://openalex.org/I193662353 |
| authorships[3].institutions[1].ror | https://ror.org/04pp8hn57 |
| authorships[3].institutions[1].type | education |
| authorships[3].institutions[1].lineage | https://openalex.org/I193662353 |
| authorships[3].institutions[1].country_code | NL |
| authorships[3].institutions[1].display_name | Utrecht University |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Peter G.M. van der Heijden |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Department of Methodology and Statistics, Faculty of Social Sciences, Utrecht University, Utrecht, the Netherlands, Southampton Statistical Sciences Research Institute, University of Southampton, Southampton, United Kingdom |
| authorships[4].author.id | https://openalex.org/A5039782389 |
| authorships[4].author.orcid | https://orcid.org/0000-0002-1692-8669 |
| authorships[4].author.display_name | Folkert W. Asselbergs |
| authorships[4].countries | GB, NL |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I193662353, https://openalex.org/I3018483916 |
| authorships[4].affiliations[0].raw_affiliation_string | Department of Cardiology, Division of Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands |
| authorships[4].affiliations[1].institution_ids | https://openalex.org/I45129253 |
| authorships[4].affiliations[1].raw_affiliation_string | Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, United Kingdom |
| authorships[4].affiliations[2].institution_ids | https://openalex.org/I4210142469, https://openalex.org/I45129253 |
| authorships[4].affiliations[2].raw_affiliation_string | Health Data Research UK and Institute of Health Informatics, University College London, London, United Kingdom |
| authorships[4].institutions[0].id | https://openalex.org/I4210142469 |
| authorships[4].institutions[0].ror | https://ror.org/04rtjaj74 |
| authorships[4].institutions[0].type | nonprofit |
| authorships[4].institutions[0].lineage | https://openalex.org/I4210142469 |
| authorships[4].institutions[0].country_code | GB |
| authorships[4].institutions[0].display_name | Health Data Research UK |
| authorships[4].institutions[1].id | https://openalex.org/I45129253 |
| authorships[4].institutions[1].ror | https://ror.org/02jx3x895 |
| authorships[4].institutions[1].type | education |
| authorships[4].institutions[1].lineage | https://openalex.org/I124357947, https://openalex.org/I45129253 |
| authorships[4].institutions[1].country_code | GB |
| authorships[4].institutions[1].display_name | University College London |
| authorships[4].institutions[2].id | https://openalex.org/I3018483916 |
| authorships[4].institutions[2].ror | https://ror.org/0575yy874 |
| authorships[4].institutions[2].type | healthcare |
| authorships[4].institutions[2].lineage | https://openalex.org/I3018483916 |
| authorships[4].institutions[2].country_code | NL |
| authorships[4].institutions[2].display_name | University Medical Center Utrecht |
| authorships[4].institutions[3].id | https://openalex.org/I193662353 |
| authorships[4].institutions[3].ror | https://ror.org/04pp8hn57 |
| authorships[4].institutions[3].type | education |
| authorships[4].institutions[3].lineage | https://openalex.org/I193662353 |
| authorships[4].institutions[3].country_code | NL |
| authorships[4].institutions[3].display_name | Utrecht University |
| authorships[4].author_position | last |
| authorships[4].raw_author_name | Folkert W. Asselbergs |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | Department of Cardiology, Division of Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands, Health Data Research UK and Institute of Health Informatics, University College London, London, United Kingdom, Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, United Kingdom |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://www.biorxiv.org/content/biorxiv/early/2019/10/13/801944.full.pdf |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | SALTClass: classifying clinical short notes using background knowledge from unlabeled data |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10028 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.9984999895095825 |
| 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 | Topic Modeling |
| related_works | https://openalex.org/W2769501189, https://openalex.org/W4315588616, https://openalex.org/W4312773271, https://openalex.org/W2888805565, https://openalex.org/W2962686197, https://openalex.org/W2207653751, https://openalex.org/W3005513013, https://openalex.org/W2611137333, https://openalex.org/W4291700620, https://openalex.org/W4317422773 |
| cited_by_count | 2 |
| counts_by_year[0].year | 2022 |
| counts_by_year[0].cited_by_count | 1 |
| counts_by_year[1].year | 2021 |
| counts_by_year[1].cited_by_count | 1 |
| locations_count | 2 |
| best_oa_location.id | doi:10.1101/801944 |
| 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.biorxiv.org/content/biorxiv/early/2019/10/13/801944.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/10.1101/801944 |
| primary_location.id | doi:10.1101/801944 |
| 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.biorxiv.org/content/biorxiv/early/2019/10/13/801944.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/10.1101/801944 |
| publication_date | 2019-10-13 |
| publication_year | 2019 |
| referenced_works | https://openalex.org/W2793682084, https://openalex.org/W2002514548, https://openalex.org/W2623759943, https://openalex.org/W2778088240, https://openalex.org/W2048195127, https://openalex.org/W2053968437, https://openalex.org/W2939054980, https://openalex.org/W1871067837, https://openalex.org/W2146089916, https://openalex.org/W2605328938, https://openalex.org/W1126062418, https://openalex.org/W1148992798, https://openalex.org/W2041789624, https://openalex.org/W2989047019, https://openalex.org/W2769851464, https://openalex.org/W2107556918, https://openalex.org/W2612554835, https://openalex.org/W2232290562, https://openalex.org/W2797076554, https://openalex.org/W2293543073, https://openalex.org/W3020920087, https://openalex.org/W27263903, https://openalex.org/W1880262756, https://openalex.org/W2890394888, https://openalex.org/W1579838312, https://openalex.org/W2949308662 |
| referenced_works_count | 26 |
| abstract_inverted_index.. | 272 |
| abstract_inverted_index.a | 19, 104, 191, 256 |
| abstract_inverted_index.It | 109 |
| abstract_inverted_index.To | 161 |
| abstract_inverted_index.We | 74, 174 |
| abstract_inverted_index.an | 158, 222 |
| abstract_inverted_index.as | 82, 255 |
| abstract_inverted_index.at | 265, 270 |
| abstract_inverted_index.be | 24, 65, 148, 253 |
| abstract_inverted_index.in | 9, 37, 47, 67, 157, 183, 190, 212, 216 |
| abstract_inverted_index.is | 103 |
| abstract_inverted_index.of | 6, 71, 88, 138, 178, 186, 208 |
| abstract_inverted_index.on | 150 |
| abstract_inverted_index.or | 156 |
| abstract_inverted_index.to | 26, 52, 129, 134 |
| abstract_inverted_index.For | 141 |
| abstract_inverted_index.NLP | 107, 181 |
| abstract_inverted_index.The | 94, 205 |
| abstract_inverted_index.and | 99, 123, 267 |
| abstract_inverted_index.are | 127 |
| abstract_inverted_index.can | 23, 50, 64, 147, 228, 243, 252 |
| abstract_inverted_index.for | 225, 239, 247 |
| abstract_inverted_index.has | 17, 220 |
| abstract_inverted_index.is, | 42 |
| abstract_inverted_index.may | 84 |
| abstract_inverted_index.ten | 164 |
| abstract_inverted_index.the | 3, 35, 48, 68, 77, 86, 130, 136, 142, 152, 176, 179, 184, 202, 245 |
| abstract_inverted_index.use | 5 |
| abstract_inverted_index.GMM. | 124 |
| abstract_inverted_index.Text | 21 |
| abstract_inverted_index.With | 2 |
| abstract_inverted_index.also | 169 |
| abstract_inverted_index.been | 170 |
| abstract_inverted_index.end, | 163 |
| abstract_inverted_index.from | 31, 79, 197, 232, 259, 268 |
| abstract_inverted_index.have | 168 |
| abstract_inverted_index.into | 172 |
| abstract_inverted_index.lead | 51 |
| abstract_inverted_index.long | 100 |
| abstract_inverted_index.need | 224 |
| abstract_inverted_index.text | 8, 69, 101, 195, 211, 233, 242 |
| abstract_inverted_index.that | 41, 59, 227 |
| abstract_inverted_index.this | 83, 162 |
| abstract_inverted_index.used | 149 |
| abstract_inverted_index.uses | 110 |
| abstract_inverted_index.word | 45 |
| abstract_inverted_index.(NLP) | 63 |
| abstract_inverted_index.Dutch | 192 |
| abstract_inverted_index.Index | 262 |
| abstract_inverted_index.Using | 235 |
| abstract_inverted_index.data, | 81 |
| abstract_inverted_index.large | 53 |
| abstract_inverted_index.noisy | 90 |
| abstract_inverted_index.parse | 229 |
| abstract_inverted_index.seven | 111 |
| abstract_inverted_index.short | 39, 89, 210, 241 |
| abstract_inverted_index.small | 44 |
| abstract_inverted_index.step, | 145 |
| abstract_inverted_index.text, | 49 |
| abstract_inverted_index.text. | 92, 140 |
| abstract_inverted_index.tools | 226 |
| abstract_inverted_index.(PyPI) | 263 |
| abstract_inverted_index.(short | 98 |
| abstract_inverted_index.BIRCH, | 120 |
| abstract_inverted_index.Center | 200 |
| abstract_inverted_index.GitHub | 269 |
| abstract_inverted_index.Python | 257, 260 |
| abstract_inverted_index.amount | 207 |
| abstract_inverted_index.become | 18 |
| abstract_inverted_index.corpus | 196 |
| abstract_inverted_index.counts | 46 |
| abstract_inverted_index.either | 151 |
| abstract_inverted_index.enrich | 135 |
| abstract_inverted_index.family | 188 |
| abstract_inverted_index.health | 11 |
| abstract_inverted_index.latent | 115 |
| abstract_inverted_index.matrix | 155 |
| abstract_inverted_index.notes, | 40, 219 |
| abstract_inverted_index.notes. | 33 |
| abstract_inverted_index.sparse | 91, 139 |
| abstract_inverted_index.urgent | 223 |
| abstract_inverted_index.useful | 14, 66 |
| abstract_inverted_index.DBScan, | 122 |
| abstract_inverted_index.Medical | 199 |
| abstract_inverted_index.Package | 261 |
| abstract_inverted_index.Results | 93 |
| abstract_inverted_index.applied | 25, 128 |
| abstract_inverted_index.cluster | 132 |
| abstract_inverted_index.created | 221 |
| abstract_inverted_index.errors. | 55 |
| abstract_inverted_index.extract | 27 |
| abstract_inverted_index.further | 248 |
| abstract_inverted_index.history | 30, 189 |
| abstract_inverted_index.improve | 244 |
| abstract_inverted_index.machine | 105, 236 |
| abstract_inverted_index.medical | 29 |
| abstract_inverted_index.methods | 126 |
| abstract_inverted_index.namely, | 114 |
| abstract_inverted_index.natural | 60 |
| abstract_inverted_index.package | 96, 258 |
| abstract_inverted_index.problem | 87 |
| abstract_inverted_index.propose | 75 |
| abstract_inverted_index.related | 15 |
| abstract_inverted_index.studies | 57 |
| abstract_inverted_index.toolkit | 182 |
| abstract_inverted_index.website | 264 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.However, | 34 |
| abstract_inverted_index.K-Means, | 118 |
| abstract_inverted_index.Previous | 56 |
| abstract_inverted_index.Utrecht, | 201 |
| abstract_inverted_index.clinical | 32, 38, 72, 193, 217 |
| abstract_inverted_index.language | 61 |
| abstract_inverted_index.learning | 106, 237 |
| abstract_inverted_index.original | 153 |
| abstract_inverted_index.records, | 12 |
| abstract_inverted_index.reports. | 234 |
| abstract_inverted_index.software | 95 |
| abstract_inverted_index.sparsity | 36 |
| abstract_inverted_index.specific | 230 |
| abstract_inverted_index.toolkit. | 108 |
| abstract_inverted_index.Dirichlet | 116 |
| abstract_inverted_index.SALTClass | 97, 146, 180, 251 |
| abstract_inverted_index.Smoothing | 125 |
| abstract_inverted_index.alleviate | 85 |
| abstract_inverted_index.different | 165 |
| abstract_inverted_index.enriching | 240 |
| abstract_inverted_index.knowledge | 78 |
| abstract_inverted_index.outcomes. | 73 |
| abstract_inverted_index.pipeline. | 160 |
| abstract_inverted_index.resulting | 131 |
| abstract_inverted_index.unlabeled | 80 |
| abstract_inverted_index.Background | 1 |
| abstract_inverted_index.MeanShift, | 121 |
| abstract_inverted_index.SALTClass. | 173 |
| abstract_inverted_index.University | 198 |
| abstract_inverted_index.algorithms | 238 |
| abstract_inverted_index.clustering | 112 |
| abstract_inverted_index.downloaded | 254 |
| abstract_inverted_index.electronic | 10 |
| abstract_inverted_index.enrichment | 159 |
| abstract_inverted_index.extracting | 13 |
| abstract_inverted_index.healthcare | 213 |
| abstract_inverted_index.increasing | 4 |
| abstract_inverted_index.integrated | 171 |
| abstract_inverted_index.necessity. | 20 |
| abstract_inverted_index.prediction | 144 |
| abstract_inverted_index.processing | 62 |
| abstract_inverted_index.subsequent | 143 |
| abstract_inverted_index.supervised | 166 |
| abstract_inverted_index.Conclusions | 204 |
| abstract_inverted_index.algorithms, | 113 |
| abstract_inverted_index.allocation, | 117 |
| abstract_inverted_index.classifier) | 102 |
| abstract_inverted_index.classifiers | 167 |
| abstract_inverted_index.demonstrate | 175 |
| abstract_inverted_index.excessively | 43 |
| abstract_inverted_index.information | 16, 133, 231 |
| abstract_inverted_index.patients’ | 28, 187 |
| abstract_inverted_index.Availability | 250 |
| abstract_inverted_index.Netherlands. | 203 |
| abstract_inverted_index.considerable | 206 |
| abstract_inverted_index.demonstrated | 58 |
| abstract_inverted_index.particularly | 215 |
| abstract_inverted_index.unstructured | 7, 209 |
| abstract_inverted_index.applications, | 214 |
| abstract_inverted_index.applications. | 249 |
| abstract_inverted_index.document-term | 154 |
| abstract_inverted_index.effectiveness | 177 |
| abstract_inverted_index.incorporating | 76 |
| abstract_inverted_index.cardiovascular | 194, 218 |
| abstract_inverted_index.classification | 22, 54, 70 |
| abstract_inverted_index.identification | 185 |
| abstract_inverted_index.representation | 137, 246 |
| abstract_inverted_index.MiniBatchK-Means, | 119 |
| abstract_inverted_index.https://pypi.org/project/saltclass | 266 |
| abstract_inverted_index.https://github.com/bagheria/saltclass | 271 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5047736253 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 5 |
| corresponding_institution_ids | https://openalex.org/I193662353, https://openalex.org/I3018483916 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.8199999928474426 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile.value | 0.66952123 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |