Exploring Pattern of Relapse in Pediatric Patients with Acute Lymphocytic Leukemia and Acute Myeloid Leukemia Undergoing Stem Cell Transplant Using Machine Learning Methods Article Swipe
David C. Shyr
,
Bing Zhang
,
Gopin Saini
,
Simon Brewer
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/jcm13144021
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/jcm13144021
Background. Leukemic relapse remains the primary cause of treatment failure and death after allogeneic hematopoietic stem cell transplant. Changes in post-transplant donor chimerism have been identified as a predictor of relapse. A better predictive model of relapse incorporating donor chimerism has the potential to improve leukemia-free survival by allowing earlier initiation of post-transplant treatment on individual patients. We explored the use of machine learning, a suite of analytical methods focusing on pattern recognition, to improve post-transplant relapse prediction. Methods. Using a cohort of 63 pediatric patients with acute lymphocytic leukemia (ALL) and 46 patients with acute myeloid leukemia (AML) who underwent stem cell transplant at a single institution, we built predictive models of leukemic relapse with both pre-transplant and post-transplant patient variables (specifically lineage-specific chimerism) using the random forest classifier. Local Interpretable Model-Agnostic Explanations, an interpretable machine learning tool was used to confirm our random forest classification result. Results. Our analysis showed that a random forest model using these hyperparameter values achieved 85% accuracy, 85% sensitivity, 89% specificity for ALL, while for AML 81% accuracy, 75% sensitivity, and 100% specificity at predicting relapses within 24 months post-HSCT in cross validation. The Local Interpretable Model-Agnostic Explanations tool was able to confirm many variables that the random forest classifier identified as important for the relapse prediction. Conclusions. Machine learning methods can reveal the interaction of different risk factors of post-transplant leukemic relapse and robust predictions can be obtained even with a modest clinical dataset. The random forest classifier distinguished different important predictive factors between ALL and AML in our relapse models, consistent with previous knowledge, lending increased confidence to adopting machine learning prediction to clinical management.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/jcm13144021
- https://www.mdpi.com/2077-0383/13/14/4021/pdf?version=1720595984
- OA Status
- gold
- Cited By
- 1
- References
- 37
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4400493486
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4400493486Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/jcm13144021Digital Object Identifier
- Title
-
Exploring Pattern of Relapse in Pediatric Patients with Acute Lymphocytic Leukemia and Acute Myeloid Leukemia Undergoing Stem Cell Transplant Using Machine Learning MethodsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-07-10Full publication date if available
- Authors
-
David C. Shyr, Bing Zhang, Gopin Saini, Simon BrewerList of authors in order
- Landing page
-
https://doi.org/10.3390/jcm13144021Publisher landing page
- PDF URL
-
https://www.mdpi.com/2077-0383/13/14/4021/pdf?version=1720595984Direct 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.mdpi.com/2077-0383/13/14/4021/pdf?version=1720595984Direct OA link when available
- Concepts
-
Medicine, Random forest, Oncology, Leukemia, Internal medicine, Hematopoietic stem cell transplantation, Transplantation, Myeloid leukemia, Recursive partitioning, Machine learning, Acute lymphocytic leukemia, Stem cell, Lymphoblastic Leukemia, Biology, Genetics, Computer scienceTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1Per-year citation counts (last 5 years)
- References (count)
-
37Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4400493486 |
|---|---|
| doi | https://doi.org/10.3390/jcm13144021 |
| ids.doi | https://doi.org/10.3390/jcm13144021 |
| ids.pmid | https://pubmed.ncbi.nlm.nih.gov/39064061 |
| ids.openalex | https://openalex.org/W4400493486 |
| fwci | 0.56791475 |
| type | article |
| title | Exploring Pattern of Relapse in Pediatric Patients with Acute Lymphocytic Leukemia and Acute Myeloid Leukemia Undergoing Stem Cell Transplant Using Machine Learning Methods |
| biblio.issue | 14 |
| biblio.volume | 13 |
| biblio.last_page | 4021 |
| biblio.first_page | 4021 |
| topics[0].id | https://openalex.org/T11287 |
| topics[0].field.id | https://openalex.org/fields/13 |
| topics[0].field.display_name | Biochemistry, Genetics and Molecular Biology |
| topics[0].score | 0.9933000206947327 |
| topics[0].domain.id | https://openalex.org/domains/1 |
| topics[0].domain.display_name | Life Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1306 |
| topics[0].subfield.display_name | Cancer Research |
| topics[0].display_name | Cancer Genomics and Diagnostics |
| topics[1].id | https://openalex.org/T10345 |
| topics[1].field.id | https://openalex.org/fields/27 |
| topics[1].field.display_name | Medicine |
| topics[1].score | 0.9927999973297119 |
| topics[1].domain.id | https://openalex.org/domains/4 |
| topics[1].domain.display_name | Health Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2720 |
| topics[1].subfield.display_name | Hematology |
| topics[1].display_name | Hematopoietic Stem Cell Transplantation |
| topics[2].id | https://openalex.org/T10309 |
| topics[2].field.id | https://openalex.org/fields/27 |
| topics[2].field.display_name | Medicine |
| topics[2].score | 0.9925000071525574 |
| topics[2].domain.id | https://openalex.org/domains/4 |
| topics[2].domain.display_name | Health Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2720 |
| topics[2].subfield.display_name | Hematology |
| topics[2].display_name | Acute Myeloid Leukemia Research |
| is_xpac | False |
| apc_list.value | 2400 |
| apc_list.currency | CHF |
| apc_list.value_usd | 2598 |
| apc_paid.value | 2400 |
| apc_paid.currency | CHF |
| apc_paid.value_usd | 2598 |
| concepts[0].id | https://openalex.org/C71924100 |
| concepts[0].level | 0 |
| concepts[0].score | 0.87427818775177 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q11190 |
| concepts[0].display_name | Medicine |
| concepts[1].id | https://openalex.org/C169258074 |
| concepts[1].level | 2 |
| concepts[1].score | 0.7120380401611328 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q245748 |
| concepts[1].display_name | Random forest |
| concepts[2].id | https://openalex.org/C143998085 |
| concepts[2].level | 1 |
| concepts[2].score | 0.5757785439491272 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q162555 |
| concepts[2].display_name | Oncology |
| concepts[3].id | https://openalex.org/C2778461978 |
| concepts[3].level | 2 |
| concepts[3].score | 0.5085760354995728 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q29496 |
| concepts[3].display_name | Leukemia |
| concepts[4].id | https://openalex.org/C126322002 |
| concepts[4].level | 1 |
| concepts[4].score | 0.49518337845802307 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q11180 |
| concepts[4].display_name | Internal medicine |
| concepts[5].id | https://openalex.org/C2777408962 |
| concepts[5].level | 3 |
| concepts[5].score | 0.4764564335346222 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q1128050 |
| concepts[5].display_name | Hematopoietic stem cell transplantation |
| concepts[6].id | https://openalex.org/C2911091166 |
| concepts[6].level | 2 |
| concepts[6].score | 0.469791054725647 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q106419912 |
| concepts[6].display_name | Transplantation |
| concepts[7].id | https://openalex.org/C2778729363 |
| concepts[7].level | 2 |
| concepts[7].score | 0.4599247872829437 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q11688946 |
| concepts[7].display_name | Myeloid leukemia |
| concepts[8].id | https://openalex.org/C137345334 |
| concepts[8].level | 2 |
| concepts[8].score | 0.44010522961616516 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q7303350 |
| concepts[8].display_name | Recursive partitioning |
| concepts[9].id | https://openalex.org/C119857082 |
| concepts[9].level | 1 |
| concepts[9].score | 0.4362734854221344 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[9].display_name | Machine learning |
| concepts[10].id | https://openalex.org/C2781107101 |
| concepts[10].level | 4 |
| concepts[10].score | 0.4294382631778717 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q180664 |
| concepts[10].display_name | Acute lymphocytic leukemia |
| concepts[11].id | https://openalex.org/C28328180 |
| concepts[11].level | 2 |
| concepts[11].score | 0.42668431997299194 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q48196 |
| concepts[11].display_name | Stem cell |
| concepts[12].id | https://openalex.org/C2909962599 |
| concepts[12].level | 3 |
| concepts[12].score | 0.29236501455307007 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q180664 |
| concepts[12].display_name | Lymphoblastic Leukemia |
| concepts[13].id | https://openalex.org/C86803240 |
| concepts[13].level | 0 |
| concepts[13].score | 0.0 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q420 |
| concepts[13].display_name | Biology |
| concepts[14].id | https://openalex.org/C54355233 |
| concepts[14].level | 1 |
| concepts[14].score | 0.0 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q7162 |
| concepts[14].display_name | Genetics |
| concepts[15].id | https://openalex.org/C41008148 |
| concepts[15].level | 0 |
| concepts[15].score | 0.0 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[15].display_name | Computer science |
| keywords[0].id | https://openalex.org/keywords/medicine |
| keywords[0].score | 0.87427818775177 |
| keywords[0].display_name | Medicine |
| keywords[1].id | https://openalex.org/keywords/random-forest |
| keywords[1].score | 0.7120380401611328 |
| keywords[1].display_name | Random forest |
| keywords[2].id | https://openalex.org/keywords/oncology |
| keywords[2].score | 0.5757785439491272 |
| keywords[2].display_name | Oncology |
| keywords[3].id | https://openalex.org/keywords/leukemia |
| keywords[3].score | 0.5085760354995728 |
| keywords[3].display_name | Leukemia |
| keywords[4].id | https://openalex.org/keywords/internal-medicine |
| keywords[4].score | 0.49518337845802307 |
| keywords[4].display_name | Internal medicine |
| keywords[5].id | https://openalex.org/keywords/hematopoietic-stem-cell-transplantation |
| keywords[5].score | 0.4764564335346222 |
| keywords[5].display_name | Hematopoietic stem cell transplantation |
| keywords[6].id | https://openalex.org/keywords/transplantation |
| keywords[6].score | 0.469791054725647 |
| keywords[6].display_name | Transplantation |
| keywords[7].id | https://openalex.org/keywords/myeloid-leukemia |
| keywords[7].score | 0.4599247872829437 |
| keywords[7].display_name | Myeloid leukemia |
| keywords[8].id | https://openalex.org/keywords/recursive-partitioning |
| keywords[8].score | 0.44010522961616516 |
| keywords[8].display_name | Recursive partitioning |
| keywords[9].id | https://openalex.org/keywords/machine-learning |
| keywords[9].score | 0.4362734854221344 |
| keywords[9].display_name | Machine learning |
| keywords[10].id | https://openalex.org/keywords/acute-lymphocytic-leukemia |
| keywords[10].score | 0.4294382631778717 |
| keywords[10].display_name | Acute lymphocytic leukemia |
| keywords[11].id | https://openalex.org/keywords/stem-cell |
| keywords[11].score | 0.42668431997299194 |
| keywords[11].display_name | Stem cell |
| keywords[12].id | https://openalex.org/keywords/lymphoblastic-leukemia |
| keywords[12].score | 0.29236501455307007 |
| keywords[12].display_name | Lymphoblastic Leukemia |
| language | en |
| locations[0].id | doi:10.3390/jcm13144021 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S2737969411 |
| locations[0].source.issn | 2077-0383 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 2077-0383 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | Journal of Clinical Medicine |
| locations[0].source.host_organization | https://openalex.org/P4310310987 |
| locations[0].source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310310987 |
| locations[0].source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://www.mdpi.com/2077-0383/13/14/4021/pdf?version=1720595984 |
| 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 | Journal of Clinical Medicine |
| locations[0].landing_page_url | https://doi.org/10.3390/jcm13144021 |
| locations[1].id | pmid:39064061 |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306525036 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | PubMed |
| locations[1].source.host_organization | https://openalex.org/I1299303238 |
| locations[1].source.host_organization_name | National Institutes of Health |
| locations[1].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | publishedVersion |
| locations[1].raw_type | |
| locations[1].license_id | |
| locations[1].is_accepted | True |
| locations[1].is_published | True |
| locations[1].raw_source_name | Journal of clinical medicine |
| locations[1].landing_page_url | https://pubmed.ncbi.nlm.nih.gov/39064061 |
| locations[2].id | pmh:oai:pubmedcentral.nih.gov:11277799 |
| locations[2].is_oa | True |
| locations[2].source.id | https://openalex.org/S2764455111 |
| locations[2].source.issn | |
| locations[2].source.type | repository |
| locations[2].source.is_oa | False |
| locations[2].source.issn_l | |
| locations[2].source.is_core | False |
| locations[2].source.is_in_doaj | False |
| locations[2].source.display_name | PubMed Central |
| locations[2].source.host_organization | https://openalex.org/I1299303238 |
| locations[2].source.host_organization_name | National Institutes of Health |
| locations[2].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[2].license | other-oa |
| locations[2].pdf_url | |
| locations[2].version | submittedVersion |
| locations[2].raw_type | Text |
| locations[2].license_id | https://openalex.org/licenses/other-oa |
| locations[2].is_accepted | False |
| locations[2].is_published | False |
| locations[2].raw_source_name | J Clin Med |
| locations[2].landing_page_url | https://www.ncbi.nlm.nih.gov/pmc/articles/11277799 |
| indexed_in | crossref, pubmed |
| authorships[0].author.id | https://openalex.org/A5021803974 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-1111-5335 |
| authorships[0].author.display_name | David C. Shyr |
| authorships[0].countries | US |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I97018004 |
| authorships[0].affiliations[0].raw_affiliation_string | Department of Pediatrics, Division of Pediatric Hematology/Oncology, Section of Stem Cell Transplant, Stanford University, Stanford, CA 94305, USA |
| authorships[0].institutions[0].id | https://openalex.org/I97018004 |
| authorships[0].institutions[0].ror | https://ror.org/00f54p054 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I97018004 |
| authorships[0].institutions[0].country_code | US |
| authorships[0].institutions[0].display_name | Stanford University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | David Shyr |
| authorships[0].is_corresponding | True |
| authorships[0].raw_affiliation_strings | Department of Pediatrics, Division of Pediatric Hematology/Oncology, Section of Stem Cell Transplant, Stanford University, Stanford, CA 94305, USA |
| authorships[1].author.id | https://openalex.org/A5100389678 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-1750-1659 |
| authorships[1].author.display_name | Bing Zhang |
| authorships[1].countries | US |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I97018004 |
| authorships[1].affiliations[0].raw_affiliation_string | Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA |
| authorships[1].institutions[0].id | https://openalex.org/I97018004 |
| authorships[1].institutions[0].ror | https://ror.org/00f54p054 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I97018004 |
| authorships[1].institutions[0].country_code | US |
| authorships[1].institutions[0].display_name | Stanford University |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Bing M. Zhang |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA |
| authorships[2].author.id | https://openalex.org/A5069819595 |
| authorships[2].author.orcid | https://orcid.org/0000-0001-7842-1241 |
| authorships[2].author.display_name | Gopin Saini |
| authorships[2].countries | US |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I97018004 |
| authorships[2].affiliations[0].raw_affiliation_string | Stem Cell and Gene Therapy Clinical Trial Program, Department of Pediatrics, Stanford University, Stanford, CA 94305, USA |
| authorships[2].institutions[0].id | https://openalex.org/I97018004 |
| authorships[2].institutions[0].ror | https://ror.org/00f54p054 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I97018004 |
| authorships[2].institutions[0].country_code | US |
| authorships[2].institutions[0].display_name | Stanford University |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Gopin Saini |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Stem Cell and Gene Therapy Clinical Trial Program, Department of Pediatrics, Stanford University, Stanford, CA 94305, USA |
| authorships[3].author.id | https://openalex.org/A5067431267 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-6810-1911 |
| authorships[3].author.display_name | Simon Brewer |
| authorships[3].countries | US |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I223532165 |
| authorships[3].affiliations[0].raw_affiliation_string | Department of Geography, University of Utah, Salt Lake City, UT 84112, USA |
| authorships[3].institutions[0].id | https://openalex.org/I223532165 |
| authorships[3].institutions[0].ror | https://ror.org/03r0ha626 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I223532165 |
| authorships[3].institutions[0].country_code | US |
| authorships[3].institutions[0].display_name | University of Utah |
| authorships[3].author_position | last |
| authorships[3].raw_author_name | Simon C. Brewer |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Department of Geography, University of Utah, Salt Lake City, UT 84112, USA |
| has_content.pdf | True |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://www.mdpi.com/2077-0383/13/14/4021/pdf?version=1720595984 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Exploring Pattern of Relapse in Pediatric Patients with Acute Lymphocytic Leukemia and Acute Myeloid Leukemia Undergoing Stem Cell Transplant Using Machine Learning Methods |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T11287 |
| primary_topic.field.id | https://openalex.org/fields/13 |
| primary_topic.field.display_name | Biochemistry, Genetics and Molecular Biology |
| primary_topic.score | 0.9933000206947327 |
| primary_topic.domain.id | https://openalex.org/domains/1 |
| primary_topic.domain.display_name | Life Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1306 |
| primary_topic.subfield.display_name | Cancer Research |
| primary_topic.display_name | Cancer Genomics and Diagnostics |
| related_works | https://openalex.org/W2523220573, https://openalex.org/W3193043704, https://openalex.org/W4386259002, https://openalex.org/W1546989560, https://openalex.org/W3171520305, https://openalex.org/W1924178503, https://openalex.org/W3135126032, https://openalex.org/W4308716060, https://openalex.org/W4280648719, https://openalex.org/W4380150137 |
| cited_by_count | 1 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 1 |
| locations_count | 3 |
| best_oa_location.id | doi:10.3390/jcm13144021 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S2737969411 |
| best_oa_location.source.issn | 2077-0383 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 2077-0383 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | Journal of Clinical Medicine |
| best_oa_location.source.host_organization | https://openalex.org/P4310310987 |
| best_oa_location.source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310310987 |
| best_oa_location.source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://www.mdpi.com/2077-0383/13/14/4021/pdf?version=1720595984 |
| 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 | Journal of Clinical Medicine |
| best_oa_location.landing_page_url | https://doi.org/10.3390/jcm13144021 |
| primary_location.id | doi:10.3390/jcm13144021 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S2737969411 |
| primary_location.source.issn | 2077-0383 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 2077-0383 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | Journal of Clinical Medicine |
| primary_location.source.host_organization | https://openalex.org/P4310310987 |
| primary_location.source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310310987 |
| primary_location.source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://www.mdpi.com/2077-0383/13/14/4021/pdf?version=1720595984 |
| 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 | Journal of Clinical Medicine |
| primary_location.landing_page_url | https://doi.org/10.3390/jcm13144021 |
| publication_date | 2024-07-10 |
| publication_year | 2024 |
| referenced_works | https://openalex.org/W2002187983, https://openalex.org/W1558704041, https://openalex.org/W2970182194, https://openalex.org/W2594644326, https://openalex.org/W2952113860, https://openalex.org/W2114072750, https://openalex.org/W2285753642, https://openalex.org/W2598496003, https://openalex.org/W2887716600, https://openalex.org/W2346004326, https://openalex.org/W2610209779, https://openalex.org/W2035642890, https://openalex.org/W4248191069, https://openalex.org/W3038147963, https://openalex.org/W3115735942, https://openalex.org/W4388906328, https://openalex.org/W2963371984, https://openalex.org/W2961296555, https://openalex.org/W2289411369, https://openalex.org/W2800722845, https://openalex.org/W3161430037, https://openalex.org/W3042578835, https://openalex.org/W2226832866, https://openalex.org/W2910705748, https://openalex.org/W2945976633, https://openalex.org/W3082965341, https://openalex.org/W4322102106, https://openalex.org/W3120671187, https://openalex.org/W4386879759, https://openalex.org/W2769214064, https://openalex.org/W1831050183, https://openalex.org/W3170940710, https://openalex.org/W2048231652, https://openalex.org/W2595371676, https://openalex.org/W3214897392, https://openalex.org/W2792919287, https://openalex.org/W4225887547 |
| referenced_works_count | 37 |
| abstract_inverted_index.A | 31 |
| abstract_inverted_index.a | 27, 64, 80, 105, 153, 238 |
| abstract_inverted_index.24 | 184 |
| abstract_inverted_index.46 | 92 |
| abstract_inverted_index.63 | 83 |
| abstract_inverted_index.We | 57 |
| abstract_inverted_index.an | 134 |
| abstract_inverted_index.as | 26, 208 |
| abstract_inverted_index.at | 104, 180 |
| abstract_inverted_index.be | 234 |
| abstract_inverted_index.by | 47 |
| abstract_inverted_index.in | 19, 187, 255 |
| abstract_inverted_index.of | 7, 29, 35, 51, 61, 66, 82, 112, 222, 226 |
| abstract_inverted_index.on | 54, 70 |
| abstract_inverted_index.to | 43, 73, 141, 198, 266, 271 |
| abstract_inverted_index.we | 108 |
| abstract_inverted_index.75% | 175 |
| abstract_inverted_index.81% | 173 |
| abstract_inverted_index.85% | 162, 164 |
| abstract_inverted_index.89% | 166 |
| abstract_inverted_index.ALL | 252 |
| abstract_inverted_index.AML | 172, 254 |
| abstract_inverted_index.Our | 149 |
| abstract_inverted_index.The | 190, 242 |
| abstract_inverted_index.and | 10, 91, 118, 177, 230, 253 |
| abstract_inverted_index.can | 218, 233 |
| abstract_inverted_index.for | 168, 171, 210 |
| abstract_inverted_index.has | 40 |
| abstract_inverted_index.our | 143, 256 |
| abstract_inverted_index.the | 4, 41, 59, 126, 203, 211, 220 |
| abstract_inverted_index.use | 60 |
| abstract_inverted_index.was | 139, 196 |
| abstract_inverted_index.who | 99 |
| abstract_inverted_index.100% | 178 |
| abstract_inverted_index.ALL, | 169 |
| abstract_inverted_index.able | 197 |
| abstract_inverted_index.been | 24 |
| abstract_inverted_index.both | 116 |
| abstract_inverted_index.cell | 16, 102 |
| abstract_inverted_index.even | 236 |
| abstract_inverted_index.have | 23 |
| abstract_inverted_index.many | 200 |
| abstract_inverted_index.risk | 224 |
| abstract_inverted_index.stem | 15, 101 |
| abstract_inverted_index.that | 152, 202 |
| abstract_inverted_index.tool | 138, 195 |
| abstract_inverted_index.used | 140 |
| abstract_inverted_index.with | 86, 94, 115, 237, 260 |
| abstract_inverted_index.(ALL) | 90 |
| abstract_inverted_index.(AML) | 98 |
| abstract_inverted_index.Local | 130, 191 |
| abstract_inverted_index.Using | 79 |
| abstract_inverted_index.acute | 87, 95 |
| abstract_inverted_index.after | 12 |
| abstract_inverted_index.built | 109 |
| abstract_inverted_index.cause | 6 |
| abstract_inverted_index.cross | 188 |
| abstract_inverted_index.death | 11 |
| abstract_inverted_index.donor | 21, 38 |
| abstract_inverted_index.model | 34, 156 |
| abstract_inverted_index.suite | 65 |
| abstract_inverted_index.these | 158 |
| abstract_inverted_index.using | 125, 157 |
| abstract_inverted_index.while | 170 |
| abstract_inverted_index.better | 32 |
| abstract_inverted_index.cohort | 81 |
| abstract_inverted_index.forest | 128, 145, 155, 205, 244 |
| abstract_inverted_index.models | 111 |
| abstract_inverted_index.modest | 239 |
| abstract_inverted_index.months | 185 |
| abstract_inverted_index.random | 127, 144, 154, 204, 243 |
| abstract_inverted_index.reveal | 219 |
| abstract_inverted_index.robust | 231 |
| abstract_inverted_index.showed | 151 |
| abstract_inverted_index.single | 106 |
| abstract_inverted_index.values | 160 |
| abstract_inverted_index.within | 183 |
| abstract_inverted_index.Changes | 18 |
| abstract_inverted_index.Machine | 215 |
| abstract_inverted_index.between | 251 |
| abstract_inverted_index.confirm | 142, 199 |
| abstract_inverted_index.earlier | 49 |
| abstract_inverted_index.factors | 225, 250 |
| abstract_inverted_index.failure | 9 |
| abstract_inverted_index.improve | 44, 74 |
| abstract_inverted_index.lending | 263 |
| abstract_inverted_index.machine | 62, 136, 268 |
| abstract_inverted_index.methods | 68, 217 |
| abstract_inverted_index.models, | 258 |
| abstract_inverted_index.myeloid | 96 |
| abstract_inverted_index.patient | 120 |
| abstract_inverted_index.pattern | 71 |
| abstract_inverted_index.primary | 5 |
| abstract_inverted_index.relapse | 2, 36, 76, 114, 212, 229, 257 |
| abstract_inverted_index.remains | 3 |
| abstract_inverted_index.result. | 147 |
| abstract_inverted_index.Leukemic | 1 |
| abstract_inverted_index.Methods. | 78 |
| abstract_inverted_index.Results. | 148 |
| abstract_inverted_index.achieved | 161 |
| abstract_inverted_index.adopting | 267 |
| abstract_inverted_index.allowing | 48 |
| abstract_inverted_index.analysis | 150 |
| abstract_inverted_index.clinical | 240, 272 |
| abstract_inverted_index.dataset. | 241 |
| abstract_inverted_index.explored | 58 |
| abstract_inverted_index.focusing | 69 |
| abstract_inverted_index.learning | 137, 216, 269 |
| abstract_inverted_index.leukemia | 89, 97 |
| abstract_inverted_index.leukemic | 113, 228 |
| abstract_inverted_index.obtained | 235 |
| abstract_inverted_index.patients | 85, 93 |
| abstract_inverted_index.previous | 261 |
| abstract_inverted_index.relapse. | 30 |
| abstract_inverted_index.relapses | 182 |
| abstract_inverted_index.survival | 46 |
| abstract_inverted_index.accuracy, | 163, 174 |
| abstract_inverted_index.chimerism | 22, 39 |
| abstract_inverted_index.different | 223, 247 |
| abstract_inverted_index.important | 209, 248 |
| abstract_inverted_index.increased | 264 |
| abstract_inverted_index.learning, | 63 |
| abstract_inverted_index.patients. | 56 |
| abstract_inverted_index.pediatric | 84 |
| abstract_inverted_index.post-HSCT | 186 |
| abstract_inverted_index.potential | 42 |
| abstract_inverted_index.predictor | 28 |
| abstract_inverted_index.treatment | 8, 53 |
| abstract_inverted_index.underwent | 100 |
| abstract_inverted_index.variables | 121, 201 |
| abstract_inverted_index.allogeneic | 13 |
| abstract_inverted_index.analytical | 67 |
| abstract_inverted_index.chimerism) | 124 |
| abstract_inverted_index.classifier | 206, 245 |
| abstract_inverted_index.confidence | 265 |
| abstract_inverted_index.consistent | 259 |
| abstract_inverted_index.identified | 25, 207 |
| abstract_inverted_index.individual | 55 |
| abstract_inverted_index.initiation | 50 |
| abstract_inverted_index.knowledge, | 262 |
| abstract_inverted_index.predicting | 181 |
| abstract_inverted_index.prediction | 270 |
| abstract_inverted_index.predictive | 33, 110, 249 |
| abstract_inverted_index.transplant | 103 |
| abstract_inverted_index.Background. | 0 |
| abstract_inverted_index.classifier. | 129 |
| abstract_inverted_index.interaction | 221 |
| abstract_inverted_index.lymphocytic | 88 |
| abstract_inverted_index.management. | 273 |
| abstract_inverted_index.prediction. | 77, 213 |
| abstract_inverted_index.predictions | 232 |
| abstract_inverted_index.specificity | 167, 179 |
| abstract_inverted_index.transplant. | 17 |
| abstract_inverted_index.validation. | 189 |
| abstract_inverted_index.Conclusions. | 214 |
| abstract_inverted_index.Explanations | 194 |
| abstract_inverted_index.institution, | 107 |
| abstract_inverted_index.recognition, | 72 |
| abstract_inverted_index.sensitivity, | 165, 176 |
| abstract_inverted_index.(specifically | 122 |
| abstract_inverted_index.Explanations, | 133 |
| abstract_inverted_index.Interpretable | 131, 192 |
| abstract_inverted_index.distinguished | 246 |
| abstract_inverted_index.hematopoietic | 14 |
| abstract_inverted_index.incorporating | 37 |
| abstract_inverted_index.interpretable | 135 |
| abstract_inverted_index.leukemia-free | 45 |
| abstract_inverted_index.Model-Agnostic | 132, 193 |
| abstract_inverted_index.classification | 146 |
| abstract_inverted_index.hyperparameter | 159 |
| abstract_inverted_index.pre-transplant | 117 |
| abstract_inverted_index.post-transplant | 20, 52, 75, 119, 227 |
| abstract_inverted_index.lineage-specific | 123 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| corresponding_author_ids | https://openalex.org/A5021803974 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| corresponding_institution_ids | https://openalex.org/I97018004 |
| citation_normalized_percentile.value | 0.63424829 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |