{"doi":"10.1002/alz.70539","title":"Identifying dementia neuropathology using low‐burden clinical data","abstract":"INTRODUCTION: Identifying dementia neuropathology is critical for guiding effective therapies and clinical trials. To tackle this, we developed semi-supervised models for identifying neuropathology using low-burden data to improve generalizability. METHODS: We defined low-burden data as being reasonably obtainable at a primary care setting. By using a semi-supervised learning paradigm, we can amplify the utility of low-burden data. We trained a clustering and a semi-supervised prediction model to yield clustering and prediction results for different neuropathology lesion types. RESULTS: Our clustering model identified two clinically meaningful outlier groups that were either neuropathology-enriched or -scarce. We predicted neuropathology burden across different pathology types and found that using low-burden data over multiple clinical visits can predict neuropathology on par with using higher-burden data. DISCUSSION: This work fills a critical gap in the field by using low-burden clinical data to predict neuropathology, thereby improving dementia screening, therapy, and targeted clinical trials. HIGHLIGHTS: Clinical data are useful for neuropathology screening in future clinical trials. Novel application of semi-supervised learning for identifying neuropathology. Clustering model found groups with highly different neuropathology prevalence. Low-burden data can provide relatively accurate predictions of pathology load. Higher-burden, longitudinal data are most helpful for predicting vascular lesions.","journal":"Alzheimer s & Dementia","year":2025,"id":550872,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":2,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9214,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":466113,"name":"Babak Shahbaba","orcid":"0000-0002-8102-1609","position":1,"is_corresponding":false},{"id":260105,"name":"Craig E.L. Stark","orcid":"0000-0002-9334-8502","position":2,"is_corresponding":false},{"id":676597,"name":"Yueqi Ren","orcid":"0000-0003-2936-6009","position":0,"is_corresponding":true}],"reference_count":61,"raw_metadata":null,"created_at":"2026-07-19T02:54:20.915388Z","pmid":"40760979","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":[]}