{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T16:31:19Z","timestamp":1786379479894,"version":"build-2736575974"},"reference-count":48,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T00:00:00Z","timestamp":1780358400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"name":"Chongqing Talent Plan","award":["cstc2022ycjh-bgzxm0184"],"award-info":[{"award-number":["cstc2022ycjh-bgzxm0184"]}]},{"name":"Science Innovation Programs Led by the Academicians in Chongqing under Project","award":["2022YSZX-JSX0002CSTB"],"award-info":[{"award-number":["2022YSZX-JSX0002CSTB"]}]},{"name":"Key Project of Technological Innovation and Application Development of Chongqing Science & Technology Bureau","award":["CSTC2021jscxgksb-N0020"],"award-info":[{"award-number":["CSTC2021jscxgksb-N0020"]}]},{"name":"STI2030-Major Projects","award":["No. 2021ZD0201802"],"award-info":[{"award-number":["No. 2021ZD0201802"]}]},{"name":"Program for Youth Innovation in Future Medicine, Chongqing Medical University","award":["W0166"],"award-info":[{"award-number":["W0166"]}]},{"name":"the fund for top talent cultivation project of Chongqing Medical University","award":["BJRC202403"],"award-info":[{"award-number":["BJRC202403"]}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Alzheimer\u2019s Disease"],"published-print":{"date-parts":[[2026,8]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Neuropsychiatric symptoms (NPS) are common in Alzheimer's disease (AD) and mild cognitive impairment (MCI), yet their detection relies on subjective assessments. Speech features offer a promising objective biomarker for NPS, reflecting emotional and cognitive states. However, existing studies are limited in terms of scale and duration.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study aims to characterize acoustic features associated with NPS in early cognitive decline using Automated Assessment Model-Mini-Mental State Examination framework, and to evaluate machine learning classifiers for identifying indicators of NPS.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Speech data from 647 clinically diagnosed AD or MCI patients were collected and split into training and test sets in a 6:4 ratio. The training set was used for feature selection and model development, while test set was used for performance evaluation. The Synthetic Minority Over-Sampling Technique was applied to address class imbalance. Twelve machine learning models were trained to classify NPS categories. The best-performing models were evaluated, and SHapley Additive exPlanations (SHAP) were used to analyze feature importance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The ExtraTrees model outperformed the others in identifying patterns associated with NPS categories, with cross-validated AUCs ranging from 0.869 to 0.901. SHAP revealed spectral_entropy_std and kurtosis_energy as key features across multiple NPS categories.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This study demonstrates that short speech samples obtained during the MMSE can identify acoustic patterns associated with NPS in clinically diagnosed AD and MCI using machine learning. Given the single-center design and absence of external validation, model outputs should be interpreted as directional signals to raise clinical awareness rather than as definitive diagnostic determinations.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/13872877261456293","type":"journal-article","created":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T09:38:01Z","timestamp":1780393081000},"page":"1206-1218","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Speech-based machine learning for detecting neuropsychiatric symptoms in Alzheimer's disease"],"prefix":"10.1177","volume":"112","author":[{"given":"Yingxi","family":"Chen","sequence":"first","affiliation":[{"name":"Chongqing Medical University Basic Medical College"},{"name":"Chongqing Medical 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