{"id":"W2264121265","doi":"10.1176/appi.pn.2016.1b4","title":"Automated Speech Analysis May Identify People With Alzheimer’s Disease","year":2016,"lang":"en","type":"article","venue":"Psychiatric News","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Disease; Psychology; Natural language processing; Computer science; Speech recognition; Linguistics; Medicine; Pathology; Philosophy","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002893605,0.0002502648,0.0003275222,0.0006186541,0.0001992927,0.0001658636,0.001176406,0.0000631424,0.0001835417],"category_scores_gemma":[0.00005897161,0.0001565748,0.0001896379,0.003983618,0.00002956513,0.0003853816,0.0001848157,0.0001623887,0.0004809334],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004867226,"about_ca_system_score_gemma":0.0002110368,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00135656,"about_ca_topic_score_gemma":0.00104567,"domain_scores_codex":[0.9974403,0.000241569,0.000385331,0.0008220715,0.000599845,0.0005109236],"domain_scores_gemma":[0.9975122,0.000151941,0.0002559678,0.001501705,0.0001242792,0.0004538744],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002642146,0.00007079174,0.96064,0.00002003532,0.000238314,0.00002921441,0.000123682,0.0001910568,0.00000461046,0.001999061,0.008341908,0.02831489],"study_design_scores_gemma":[0.0005960484,0.00009786628,0.9526231,0.00001691798,0.00055331,0.00001531635,0.00001070311,0.04019364,0.000008277308,0.0007730042,0.004721133,0.0003906991],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3288867,0.003220794,0.5683117,0.0866145,0.002480157,0.001196973,0.00004104579,0.005855751,0.003392324],"genre_scores_gemma":[0.9502729,0.00005081182,0.04831797,0.0005567216,0.000196322,0.00003694417,0.000007626684,0.00002623216,0.0005344466],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6213862,"threshold_uncertainty_score":0.6384935,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01659916162657053,"score_gpt":0.3164263300236065,"score_spread":0.299827168397036,"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."}}