{"doi":"10.1038/s41598-024-68481-w","title":"Developing a fair and interpretable representation of the clock drawing test for mitigating low education and racial bias","abstract":"The clock drawing test (CDT) is a neuropsychological assessment tool to screen an individual's cognitive ability. In this study, we developed a Fair and Interpretable Representation of Clock drawing test (FaIRClocks) to evaluate and mitigate classification bias against people with less than 8 years of education, while screening their cognitive function using an array of neuropsychological measures. In this study, we represented clock drawings by a priorly published 10-dimensional deep learning feature set trained on publicly available data from the National Health and Aging Trends Study (NHATS). These embeddings were further fine-tuned with clocks from a preoperative cognitive screening program at the University of Florida to predict three cognitive scores: the Mini-Mental State Examination (MMSE) total score, an attention composite z-score (ATT-C), and a memory composite z-score (MEM-C). ATT-C and MEM-C scores were developed by averaging z-scores based on normative references. The cognitive screening classifiers were initially tested to see their relative performance in patients with low years of education (< = 8 years) versus patients with higher education (> 8 years) and race. Results indicated that the initial unweighted classifiers confounded lower education with cognitive compromise resulting in a 100% type I error rate for this group. Thereby, the samples were re-weighted using multiple fairness metrics to achieve sensitivity/specificity and positive/negative predictive value (PPV/NPV) balance across groups. In summary, we report the FaIRClocks model, with promise to help identify and mitigate bias against people with less than 8 years of education during preoperative cognitive screening.","journal":"Scientific Reports","year":2024,"id":468704,"datarank":0.20794415416798362,"base_score":1.3862943611198906,"endowment":1.3862943611198906,"self_citation_contribution":0.20794415416798362,"citation_network_contribution":0.0,"self_endowment_contribution":0.20794415416798362,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":3,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9615,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":939279,"name":"Sabyasachi Bandyopadhyay","orcid":"0000-0003-4617-7832","position":1,"is_corresponding":false},{"id":1170496,"name":"Faith Kimmet","orcid":null,"position":2,"is_corresponding":false},{"id":1108869,"name":"Jack Wittmayer","orcid":null,"position":3,"is_corresponding":false},{"id":37846,"name":"Kia Khezeli","orcid":"0000-0002-1982-9391","position":4,"is_corresponding":false},{"id":352828,"name":"David J. Libon","orcid":"0000-0002-0456-8600","position":5,"is_corresponding":false},{"id":352830,"name":"Catherine C. Price","orcid":"0000-0001-5994-0644","position":6,"is_corresponding":false},{"id":231724,"name":"Parisa Rashidi","orcid":"0000-0003-4530-2048","position":7,"is_corresponding":false},{"id":1166965,"name":"Jiaqing Zhang","orcid":"0000-0002-6773-0046","position":0,"is_corresponding":true}],"reference_count":57,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T02:05:23.500722Z","pmid":"39075127","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":[]}