{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T03:28:53Z","timestamp":1788838133677,"version":"build-2803163510"},"reference-count":45,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2023,8,25]],"date-time":"2023-08-25T00:00:00Z","timestamp":1692921600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J Applied Clin Med Phys"],"published-print":{"date-parts":[[2023,10]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>Radiation Oncology Learning Health System (RO\u2010LHS) is a promising approach to improve the quality of care by integrating clinical, dosimetry, treatment delivery, research data in real\u2010time. This paper describes a novel set of tools to support the development of a RO\u2010LHS and the current challenges they can address.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We present a knowledge graph\u2010based approach to map radiotherapy data from clinical databases to an ontology\u2010based data repository using FAIR concepts. This strategy ensures that the data are easily discoverable, accessible, and can be used by other clinical decision support systems. It allows for visualization, presentation, and data analyses of valuable information to identify trends and patterns in patient outcomes. We designed a search engine that utilizes ontology\u2010based keyword searching, synonym\u2010based term matching that leverages the hierarchical nature of ontologies to retrieve patient records based on parent and children classes, connects to the Bioportal database for relevant clinical attributes retrieval. To identify similar patients, a method involving text corpus creation and vector embedding models (Word2Vec, Doc2Vec, GloVe, and FastText) are employed, using cosine similarity and distance metrics.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The data pipeline and tool were tested with 1660 patient clinical and dosimetry records resulting in 504\u00a0180 RDF (Resource Description Framework) tuples and visualized data relationships using graph\u2010based representations. Patient similarity analysis using embedding models showed that the Word2Vec model had the highest mean cosine similarity, while the GloVe model exhibited more compact embeddings with lower Euclidean and Manhattan distances.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The framework and tools described support the development of a RO\u2010LHS. By integrating diverse data sources and facilitating data discovery and analysis, they contribute to continuous learning and improvement in patient care. The tools enhance the quality of care by enabling the identification of cohorts, clinical decision support, and the development of clinical studies and machine learning programs in radiation oncology.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/acm2.14127","type":"journal-article","created":{"date-parts":[[2023,8,25]],"date-time":"2023-08-25T10:33:38Z","timestamp":1692959618000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Infrastructure tools to support an effective Radiation Oncology Learning Health System"],"prefix":"10.1002","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3064-4088","authenticated-orcid":false,"given":"Rishabh","family":"Kapoor","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology Virginia Commonwealth University  Richmond Virginia USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8431-8483","authenticated-orcid":false,"suffix":"IV","given":"William C","family":"Sleeman","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Virginia Commonwealth University  Richmond Virginia USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Preetam","family":"Ghosh","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Virginia Commonwealth University  Richmond Virginia USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9764-7414","authenticated-orcid":false,"given":"Jatinder","family":"Palta","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Virginia Commonwealth University  Richmond Virginia USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2023,8,25]]},"reference":[{"key":"e_1_2_8_2_1","volume-title":"The Fifth Discipline: The Art and Practice of the Learning Organization","author":"Senge PM","year":"2006"},{"key":"e_1_2_8_3_1","volume-title":"The Learning Healthcare System: Workshop Summary","author":"Olsen L","year":"2007"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2016.09.018"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1377\/hlthaff.26.2.w107"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.13136"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.adro.2016.10.001"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1200\/CCI.18.00034"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2010.07.1139"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2013.07.007"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2021.09.030"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmedinf.2017.04.015"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12875\u2010015\u20100223\u2010z"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmedinf.2014.06.009"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.2196\/35724"},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.1002\/acm2.13308"},{"key":"e_1_2_8_17_1","unstructured":"Policies and technology for interoperability and burden reduction.Centers for Medicare & Medicaid Services. [2021\u201008\u201030].https:\/\/www.cms.gov\/Regulations\u2010and\u2010Guidance\/Guidance\/Interoperability\/index"},{"key":"e_1_2_8_18_1","unstructured":"Orthanc DICOM Server.https:\/\/www.orthanc\u2010server.com\/"},{"key":"e_1_2_8_19_1","unstructured":"DICOM DIMSE.https:\/\/dicom.nema.org\/dicom\/2013\/output\/chtml\/part07\/sect_7.5.html"},{"key":"e_1_2_8_20_1","doi-asserted-by":"publisher","DOI":"10.3390\/healthcare8020120"},{"issue":"6","key":"e_1_2_8_21_1","first-page":"111","article-title":"Relabeling non\u2010standard to standard structure names using geometric and radiomic information","volume":"47","author":"Sleeman C","year":"2020","journal-title":"Med Phys"},{"key":"e_1_2_8_22_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2020.103527"},{"key":"e_1_2_8_23_1","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2016.18"},{"key":"e_1_2_8_24_1","unstructured":"Semantic Web at W3C.https:\/\/www.w3.org\/standards\/semanticweb"},{"key":"e_1_2_8_25_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.12879"},{"key":"e_1_2_8_26_1","unstructured":"Radiation Oncology Ontology.Summary | NCBO BioPortal.https:\/\/www.bioontology.org"},{"key":"e_1_2_8_27_1","article-title":"Prot\u00e9g\u00e9\u20102000: an open\u2010source ontology\u2010development and knowledge\u2010acquisition environment","volume":"953","author":"Noy NF","year":"2003","journal-title":"AMIA Annu Symp Proc AMIA Symp"},{"key":"e_1_2_8_28_1","unstructured":"National Cancer Institute Thesaurus.Summary | NCBO BioPortal.https:\/\/www.bioontology.org"},{"key":"e_1_2_8_29_1","unstructured":"International Classification of Diseases.Version 10 \u2010 Summary | NCBO BioPortal.https:\/\/www.bioontology.org"},{"key":"e_1_2_8_30_1","unstructured":"DBpedia ontology.https:\/\/www.dbpedia.org"},{"key":"e_1_2_8_31_1","doi-asserted-by":"crossref","unstructured":"UrbaniJ JacobsC.Adaptive low\u2010level storage of very large knowledge graphs WWW '20: Proceedings of The Web Conference 2020. April 2020.2020;1761\u20101772. doi:10.1145\/3366423.3380246","DOI":"10.1145\/3366423.3380246"},{"key":"e_1_2_8_32_1","unstructured":"Ontotext GraphDB.https:\/\/www.ontotext.com"},{"key":"e_1_2_8_33_1","unstructured":"MikolovT ChenK CorradoG DeanJ.\u201cEfficient Estimation of Word Representations in Vector Space\u201d. arXiv preprint arXiv:1301.3781. 2013 Jan 162013."},{"key":"e_1_2_8_34_1","unstructured":"LeQ MikolovT.Distributed representations of sentences and documents.Proceedings of the 31st International Conference on International Conference on Machine Learning.2014;32:(ICML'14). JMLR.org II\u20131188\u2013II\u20131196."},{"key":"e_1_2_8_35_1","doi-asserted-by":"crossref","unstructured":"PenningtonJ SocherR ManningC.GloVe: Global Vectors for Word Representation.Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) Doha Qatar.Association for Computational Linguistics.2014:1532\u20101543.","DOI":"10.3115\/v1\/D14-1162"},{"key":"e_1_2_8_36_1","first-page":"135","volume-title":"Enriching Word Vectors with Subword Information","author":"Bojanowski P","year":"2017"},{"key":"e_1_2_8_37_1","unstructured":"Gruff\u2014AllegroGraph software.https:\/\/allegrograph.com\/"},{"key":"e_1_2_8_38_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.12817"},{"key":"e_1_2_8_39_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.13114"},{"key":"e_1_2_8_40_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2016.10.002"},{"key":"e_1_2_8_41_1","doi-asserted-by":"publisher","DOI":"10.1200\/CCI.19.00047"},{"key":"e_1_2_8_42_1","doi-asserted-by":"publisher","DOI":"10.1002\/acm2.13875"},{"key":"e_1_2_8_43_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41746\u2010019\u20100162\u20105"},{"key":"e_1_2_8_44_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejmp.2020.03.017"},{"key":"e_1_2_8_45_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2009.05.007"},{"key":"e_1_2_8_46_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11704\u2010016\u20105228\u20109"}],"container-title":["Journal of Applied Clinical Medical Physics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/aapm.onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/acm2.14127","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,5]],"date-time":"2025-06-05T12:18:14Z","timestamp":1749125894000},"score":1,"resource":{"primary":{"URL":"https:\/\/aapm.onlinelibrary.wiley.com\/doi\/10.1002\/acm2.14127"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,25]]},"references-count":45,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2023,10]]}},"alternative-id":["10.1002\/acm2.14127"],"URL":"https:\/\/doi.org\/10.1002\/acm2.14127","archive":["Portico"],"relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2023.06.17.23290273","asserted-by":"object"}]},"ISSN":["1526-9914","1526-9914"],"issn-type":[{"value":"1526-9914","type":"print"},{"value":"1526-9914","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,25]]},"assertion":[{"value":"2023-05-18","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-07-19","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-08-25","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e14127"}}