{"doi":"10.1002/dad2.12276","title":"Automated text‐level semantic markers of Alzheimer's disease","abstract":"INTRODUCTION: Automated speech analysis has emerged as a scalable, cost-effective tool to identify persons with Alzheimer's disease dementia (ADD). Yet, most research is undermined by low interpretability and specificity. METHODS: Combining statistical and machine learning analyses of natural speech data, we aimed to discriminate ADD patients from healthy controls (HCs) based on automated measures of domains typically affected in ADD: semantic granularity (coarseness of concepts) and ongoing semantic variability (conceptual closeness of successive words). To test for specificity, we replicated the analyses on Parkinson's disease (PD) patients. RESULTS: Relative to controls, ADD (but not PD) patients exhibited significant differences in both measures. Also, these features robustly discriminated between ADD patients and HC, while yielding near-chance classification between PD patients and HCs. DISCUSSION: Automated discourse-level semantic analyses can reveal objective, interpretable, and specific markers of ADD, bridging well-established neuropsychological targets with digital assessment tools.","journal":"Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring","year":2022,"id":247130,"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":34,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9629,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":885173,"name":"Facundo Carrillo","orcid":null,"position":1,"is_corresponding":false},{"id":262309,"name":"Andrea Slachevsky","orcid":"0000-0001-6285-3189","position":2,"is_corresponding":false},{"id":342006,"name":"Gonzalo Forno","orcid":"0000-0003-2739-6028","position":3,"is_corresponding":false},{"id":885174,"name":"Maria Luisa Gorno Tempini","orcid":null,"position":4,"is_corresponding":false},{"id":342007,"name":"Roque Villagra","orcid":"0000-0002-4388-4833","position":5,"is_corresponding":false},{"id":282310,"name":"Agustín Ibáñez","orcid":"0000-0001-6758-5101","position":6,"is_corresponding":false},{"id":315384,"name":"Enzo Tagliazucchi","orcid":"0000-0003-0421-9993","position":7,"is_corresponding":false},{"id":262271,"name":"Adolfo M. García","orcid":"0000-0002-6936-0114","position":8,"is_corresponding":false},{"id":885172,"name":"Camila Sanz","orcid":null,"position":0,"is_corresponding":true}],"reference_count":52,"raw_metadata":null,"created_at":"2026-07-19T00:23:53.369530Z","pmid":"35059492","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":[]}