{"doi":"10.1145/3583133.3596393","title":"Scikit-FIBERS: An 'OR'-Rule Discovery Evolutionary Algorithm for Risk Stratification in Right-Censored Survival Analyses","abstract":"In 'time-to-event' problems, such as survival analysis in biomedical research, investigators seek to identify the factors that impact/predict not only whether an event occurred (e.g. death), but also when. Further, in the domain of kidney transplantation, there has been recent interest in determining whether higher resolution features, i.e. amino-acid mismatches (AA-MMs) between donors and recipients, can improve risk stratification for transplant failure and identify subsets of AA-MM positions for evaluating risk in future transplants. Motivated by these problems, the FIBERS algorithm was previously developed, which applies a genetic algorithm to learn a population of feature bins (i.e. OR-rules) for predicting right-censored time-to-event survival outcomes. Here, instances are categorized as high-risk if an AA-MM is present for any AA-MM position specified in a given rule, and low-risk if not. In the present study we focus further on the FIBERS algorithm; (1) implementing it as an accessible scikit-learn compatible machine learning package, and (2) applying a variety of simulated right-censored survival datasets to validate scikit-FIBERS efficacy and examine the limitations of its performance to guide ongoing development. We present preliminary results demonstrating the efficacy of scikit-FIBERS, and the limitations of both the algorithm and survival data simulator to guide future work.","journal":null,"year":2023,"id":400186,"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":0.9529,"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":1176490,"name":"Harsh Bandhey","orcid":"0000-0002-4113-0616","position":1,"is_corresponding":false},{"id":524923,"name":"Malek Kamoun","orcid":"0000-0002-2568-1733","position":2,"is_corresponding":false},{"id":1176491,"name":"N. M. Fogarty","orcid":"0009-0003-0829-2570","position":3,"is_corresponding":false},{"id":1176492,"name":"Yi-An Hsieh","orcid":"0009-0001-2945-3826","position":4,"is_corresponding":false},{"id":14809,"name":"Ryan J. Urbanowicz","orcid":"0000-0002-0487-5555","position":0,"is_corresponding":true}],"reference_count":8,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T01:20:00.023537Z","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":[]}