{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001768203,0.0006693081,0.0003481532,0.003929414,0.0004068119,0.00229893,0.0003397381,0.001088236,0.02201332],"category_scores_gemma":[0.008491624,0.0002191184,0.0004531861,0.001116303,0.0002338716,0.00119782,0.0007144838,0.0006354041,0.01754116],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003482937,"about_ca_system_score_gemma":0.0003171131,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00249424,"about_ca_topic_score_gemma":0.004030592,"domain_scores_codex":[0.9992774,0.000199718,0.000126447,0.000141272,0.0001932547,0.00006198893],"domain_scores_gemma":[0.9950203,0.002312891,0.0006340262,0.0002563831,0.001613597,0.0001628319],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0007702864,0.0002834344,0.2221443,0.0005852437,0.0001430555,0.0005703907,0.0007962927,0.0006079455,0.005237922,0.0009334498,0.1444985,0.6234292],"study_design_scores_gemma":[0.0001854459,0.0008446066,0.7889462,0.001771024,0.0003350583,0.00375283,0.002495044,0.009011855,0.01044739,0.01111663,0.1708779,0.000215996],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.639367,0.04171986,0.04535452,0.03209657,0.00794139,0.0009020079,0.05028144,0.007330049,0.1750072],"genre_scores_gemma":[0.8533853,0.01848291,0.04822273,0.005314295,0.003687946,0.0005592824,0.02111129,0.0005348821,0.04870125],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02201332,"threshold_uncertainty_score":0.07364184,"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."}}