{"doi":"10.1200/cci.23.00003","title":"Development of a Machine Learning Model to Identify Colorectal Cancer Stage in Medicare Claims","abstract":"PURPOSE: Staging information is essential for colorectal cancer research. Medicare claims are an important source of population-level data but currently lack oncologic stage. We aimed to develop a claims-based model to identify stage at diagnosis in patients with colorectal cancer. METHODS: We included patients age 66 years or older with colorectal cancer in the SEER-Medicare registry. Using patients diagnosed from 2014 to 2016, we developed models (multinomial logistic regression, elastic net regression, and random forest) to classify patients into stage I-II, III, or IV on the basis of demographics, diagnoses, and treatment utilization identified in Medicare claims. Models developed in a training cohort (2014-2016) were applied to a testing cohort (2017), and performance was evaluated using cancer stage listed in the SEER registry as the reference standard. RESULTS: The cohort of patients with 30,543 colorectal cancer included 14,935 (48.9%) patients with stage I-II, 9,203 (30.1%) with stage III, and 6,405 (21%) with stage IV disease. A claims-based model using elastic net regression had a scaled Brier score (SBS) of 0.45 (95% CI, 0.43 to 0.46). Performance was strongest for classifying stage IV (SBS, 0.62; 95% CI, 0.59 to 0.64; sensitivity, 93%; 95% CI, 91 to 94) followed by stage I-II (SBS, 0.45; 95% CI, 0.44 to 0.47; sensitivity, 86%; 95% CI, 85 to 76) and stage III (SBS, 0.32; 95% CI, 0.30 to 0.33; sensitivity, 62%; 95% CI, 61 to 64). CONCLUSION: Machine learning models effectively classified colorectal cancer stage using Medicare claims. These models extend the ability of claims-based research to risk-adjust and stratify by stage.","journal":"JCO Clinical Cancer Informatics","year":2023,"id":365593,"datarank":0.34391102268930684,"base_score":1.9459101490553132,"endowment":1.9459101490553132,"self_citation_contribution":0.29188652235829704,"citation_network_contribution":0.052024500331009825,"self_endowment_contribution":0.29188652235829704,"citer_contribution":0.052024500331009825,"corpus_percentile":49.03689951264795,"corpus_rank":6589,"citation_count":6,"citer_count":3,"citers_with_citation_signal":2,"citers_with_endowment":2,"datacite_reuse_total":0,"is_dataset":true,"is_dataset_confidence":0.6033,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":375720,"name":"James Sharpe","orcid":"0000-0002-4357-718X","position":1,"is_corresponding":false},{"id":916785,"name":"Jason Tong","orcid":"0000-0002-3351-9330","position":2,"is_corresponding":false},{"id":385870,"name":"Elinore J. Kaufman","orcid":"0000-0001-7550-0024","position":3,"is_corresponding":false},{"id":283057,"name":"Heather Wachtel","orcid":"0000-0003-3786-3638","position":4,"is_corresponding":false},{"id":463909,"name":"Cary B. Aarons","orcid":"0000-0001-9908-0255","position":5,"is_corresponding":false},{"id":15856,"name":"Gary E. Weissman","orcid":"0000-0001-9588-3819","position":6,"is_corresponding":false},{"id":455556,"name":"Rachel R. Kelz","orcid":"0000-0001-6635-5961","position":7,"is_corresponding":false},{"id":916784,"name":"Caitlin B. Finn","orcid":"0000-0002-1699-8114","position":0,"is_corresponding":true}],"reference_count":33,"raw_metadata":null,"created_at":"2026-07-19T01:14:50.885797Z","pmid":"37257142","pmcid":"PMC10530805","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":[]}