{"doi":"10.1177/20552076241247963","title":"Application of machine learning in the management of lymphoma: Current practice and future prospects","abstract":"<jats:p>In the past decade, digitization of medical records and multiomics data analysis in lymphoma has led to the accessibility of high-dimensional records. The digitization of medical records, the visualization of extensive volume data extracted from medical images, and the integration of multiomics methods into clinical decision-making have produced many datasets. As a promising auxiliary tool, machine learning (ML) intends to extract homologous features in large-scale data sets and encode them into various patterns to complete complicated tasks. At present, artificial intelligence and digital mining have shown promising prospects in the field of lymphoma pathological image analysis. The paradigm shift from qualitative analysis to quantitative analysis makes the pathological diagnosis more intelligent and the results more accurate and objective. ML can promote accurate lymphoma diagnosis and provide patients with prognostic information and more individualized treatment options. Based on the above, this comprehensive review of the general workflow of ML highlights recent advances in ML techniques in the diagnosis, treatment, and prognosis of lymphoma, and clarifies the boundedness and future orientation of the ML technique in the clinical practice of lymphoma.</jats:p>","journal":"DIGITAL HEALTH","year":2024,"id":596561,"datarank":0.44166584687496613,"base_score":2.9444389791664403,"endowment":2.9444389791664403,"self_citation_contribution":0.44166584687496613,"citation_network_contribution":0.0,"self_endowment_contribution":0.44166584687496613,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":18,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1326078,"name":"Ya Zhang","orcid":"0000-0002-0291-0302","position":1,"is_corresponding":false},{"id":254301,"name":"Xin Wang","orcid":"0000-0001-8051-1481","position":2,"is_corresponding":false},{"id":1527883,"name":"Junyun Yuan","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Application of machine learning in the management of lymphoma: Current practice and future prospects.","abstract":"In the past decade, digitization of medical records and multiomics data analysis in lymphoma has led to the accessibility of high-dimensional records. The digitization of medical records, the visualization of extensive volume data extracted from medical images, and the integration of multiomics methods into clinical decision-making have produced many datasets. As a promising auxiliary tool, machine learning (ML) intends to extract homologous features in large-scale data sets and encode them into various patterns to complete complicated tasks. At present, artificial intelligence and digital mining have shown promising prospects in the field of lymphoma pathological image analysis. The paradigm shift from qualitative analysis to quantitative analysis makes the pathological diagnosis more intelligent and the results more accurate and objective. ML can promote accurate lymphoma diagnosis and provide patients with prognostic information and more individualized treatment options. Based on the above, this comprehensive review of the general workflow of ML highlights recent advances in ML techniques in the diagnosis, treatment, and prognosis of lymphoma, and clarifies the boundedness and future orientation of the ML technique in the clinical practice of lymphoma.","is_dataset_classified":null,"base_score":0.0,"endowment":0.0,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"38628632","pmcid":"PMC11020711","openalex_id":null,"authors":[],"funders":[{"funder_name":"Translational Research Grant of NCRCH","grant_id":"2020ZKMB01","title":null},{"funder_name":"National Natural Science Foundation of China","grant_id":"82070203","title":null},{"funder_name":"Translational Research Grant of NCRCH","grant_id":"2021WWB02","title":null},{"funder_name":"Natural Science Foundation of Shandong Province","grant_id":"ZR2020QH094","title":null},{"funder_name":"China Postdoctoral Science Foundation","grant_id":"2022M721981","title":null},{"funder_name":"Taishan Scholars Program of Shandong Province","grant_id":"tsqn201909184","title":null},{"funder_name":"National Natural Science Foundation of China","grant_id":"81770210","title":null},{"funder_name":"National Natural Science Foundation of China","grant_id":"82270200","title":null},{"funder_name":"Academic Promotion Programme of Shandong First Medical University","grant_id":"2019QL018","title":null},{"funder_name":"National Natural Science Foundation of China","grant_id":"82000195","title":null},{"funder_name":"Key Technology Research and Development Program of Shandong Province","grant_id":"2018CXGC1213","title":null},{"funder_name":"Taishan Scholars Program of Shandong Province","grant_id":"tspd20230610","title":null},{"funder_name":"Shandong Provincial Engineering Research Center of Lymphoma","grant_id":"","title":null}],"total_grants":13,"fwci":null,"citation_percentile":null,"influential_citations":0,"citation_trend":[],"oa_status":null,"license":null,"oa_locations":[{"url":"https://europepmc.org/articles/PMC11020711","host_type":"Europe_PMC"},{"url":"https://europepmc.org/articles/PMC11020711?pdf=render","host_type":"Europe_PMC"}],"fields_of_study":[],"mesh_terms":[],"keywords":["Artificial intelligence","Diagnosis","Prognosis","Treatment","Lymphoma","Machine Learning"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-07-28T10:38:33.658815Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}