{"doi":"10.1109/embc40787.2023.10341037","title":"Automated Knowledge Modeling for Cancer Clinical Practice Guidelines","abstract":null,"journal":"2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)","year":2023,"id":607376,"datarank":0.10397207708399181,"base_score":0.6931471805599453,"endowment":0.6931471805599453,"self_citation_contribution":0.10397207708399181,"citation_network_contribution":0.0,"self_endowment_contribution":0.10397207708399181,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":1,"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":1559569,"name":"Bhumika Gupta","orcid":null,"position":1,"is_corresponding":false},{"id":1047163,"name":"Arihant Jain","orcid":"0000-0003-1073-6153","position":2,"is_corresponding":false},{"id":1559570,"name":"Sneha Sree C","orcid":null,"position":3,"is_corresponding":false},{"id":1559571,"name":"Arunima Sarkar","orcid":null,"position":4,"is_corresponding":false},{"id":560972,"name":"Keerthi Ram","orcid":"0000-0002-0555-1923","position":5,"is_corresponding":false},{"id":560973,"name":"Mohanasankar Sivaprakasam","orcid":"0000-0002-6714-9147","position":6,"is_corresponding":false},{"id":1559568,"name":"Pralaypati Ta","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Automated Knowledge Modeling for Cancer Clinical Practice Guidelines","abstract":"Clinical Practice Guidelines (CPGs) for cancer diseases evolve rapidly due to new evidence generated by active research. Currently, CPGs are primarily published in a document format that is ill-suited for managing this developing knowledge. A knowledge model of the guidelines document suitable for programmatic interaction is required. This work proposes an automated method for extraction of knowledge from National Comprehensive Cancer Network (NCCN) CPGs in Oncology and generating a structured model containing the retrieved knowledge. The proposed method was tested using two versions of NCCN Non-Small Cell Lung Cancer (NSCLC) CPG to demonstrate the effectiveness in faithful extraction and modeling of knowledge. Three enrichment strategies using Cancer staging information, Unified Medical Language System (UMLS) Metathesaurus & National Cancer Institute thesaurus (NCIt) concepts, and Node classification are also presented to enhance the model towards enabling programmatic traversal and querying of cancer care guidelines. The Node classification was performed using a Support Vector Machine (SVM) model, achieving a classification accuracy of 0.81 with 10-fold cross-validation.","is_dataset_classified":null,"base_score":0.6931471805599453,"endowment":0.6931471805599453,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"38082992","pmcid":null,"openalex_id":"https://openalex.org/W4389542960","authors":[],"funders":[],"total_grants":0,"fwci":null,"citation_percentile":null,"influential_citations":0,"citation_trend":[{"year":2024,"count":1}],"oa_status":"closed","license":"https://doi.org/10.15223/policy-029","oa_locations":[{"url":"http://xplorestaging.ieee.org/ielx7/10339936/10339939/10341037.pdf?arnumber=10341037","host_type":"publisher"},{"url":"http://dx.doi.org/10.1109/embc40787.2023.10341037","host_type":""},{"url":"https://pubmed.ncbi.nlm.nih.gov/38082992","host_type":"repository"}],"fields_of_study":["Biomedical Text Mining and Ontologies","Semantic Web and Ontologies","Clinical practice guidelines implementation"],"mesh_terms":["Carcinoma, Non-Small-Cell Lung","Humans","Lung Neoplasms","Practice Guidelines as Topic","Unified Medical Language System","Vocabulary, Controlled"],"keywords":["Computer science","Unified Medical Language System","Support vector machine","Knowledge acquisition","Knowledge modeling","Information retrieval","Artificial intelligence","Data mining","Domain knowledge"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-07-30T06:16:47.004050Z","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":[]}