{"id":"W4413223212","doi":"10.1007/978-981-95-0568-5_1","title":"Early Prediction of Agitation in Community-Dwelling People with Dementia Using Multimodal Sensors and Machine Learning: Benchmarking of State-of-the-Art Techniques","year":2025,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Rehabilitation Institute; University of Toronto; University Health Network","funders":"","keywords":"Benchmarking; Computer science; Artificial intelligence; State (computer science); Dementia; Medicine; Programming language; Management","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008178085,0.000978632,0.0007405829,0.001076005,0.0001516812,0.001183993,0.0006657562,0.0008047018,0.001466521],"category_scores_gemma":[0.003344591,0.00016762,0.0006165669,0.0005168311,0.0001461728,0.0005900611,0.0004496951,0.0007068584,0.0007234039],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002117949,"about_ca_system_score_gemma":0.0003167735,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003794852,"about_ca_topic_score_gemma":0.006730359,"domain_scores_codex":[0.999774,0.0000631622,0.00002747745,0.00005948168,0.00005484446,0.00002095952],"domain_scores_gemma":[0.9989329,0.0006981203,0.00009544508,0.00003253037,0.0001873215,0.00005366651],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002312926,0.0005401248,0.3264066,0.0007997828,0.0004643872,0.0004517534,0.0003202176,0.004895273,0.003848824,0.0004460097,0.008663246,0.6508508],"study_design_scores_gemma":[0.0001787873,0.002141854,0.8038803,0.001078936,0.0008520205,0.002928922,0.001277873,0.1659464,0.01071157,0.004965007,0.005834111,0.0002041706],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9088093,0.03573875,0.03699406,0.001438133,0.000556133,0.0002340545,0.008011808,0.0007841298,0.007433595],"genre_scores_gemma":[0.9474184,0.01182749,0.03078401,0.0002591186,0.0003176986,0.0001363806,0.005004974,0.00005456465,0.00419732],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003794852,"threshold_uncertainty_score":0.007545531,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03678208380870259,"score_gpt":0.2808675459841234,"score_spread":0.2440854621754208,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}