{"id":"W4381715639","doi":"10.1109/tnsre.2023.3288835","title":"Early Detection of Alzheimer’s Disease From Cortical and Hippocampal Local Field Potentials Using an Ensembled Machine Learning Model","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ministero dell’Istruzione, dell’Università e della Ricerca; Università degli Studi di Padova; Ministero della Salute; Trent University; Nottingham Trent University","keywords":"Local field potential; Artificial intelligence; Computer science; Pattern recognition (psychology); Machine learning; Masking (illustration); Hippocampal formation; Decoding methods; Neuroscience; Psychology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.000403542,0.0004804133,0.0004441318,0.0003839212,0.0001451281,0.0004240903,0.0004357118,0.000494169,0.0003913961],"category_scores_gemma":[0.0009373302,0.0001740144,0.000613616,0.000228799,0.0001424411,0.0003938342,0.0003227062,0.0005980383,0.0001279251],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002963838,"about_ca_system_score_gemma":0.0002811186,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003512792,"about_ca_topic_score_gemma":0.004366945,"domain_scores_codex":[0.9999052,0.00002269178,0.000005096416,0.00003260718,0.00001967306,0.00001468449],"domain_scores_gemma":[0.9997593,0.0001276303,0.00003303629,0.00002343323,0.00004467295,0.00001195439],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001021025,0.00007330689,0.006780239,0.00002485382,0.0001143974,0.0001154552,0.00005244026,0.8904275,0.008667059,0.00106363,0.0004656176,0.09211341],"study_design_scores_gemma":[9.723942e-7,0.00001135074,0.0005186828,0.000001186587,0.000005254293,0.00001158889,0.000001866626,0.9987834,0.0003372389,0.0002794521,0.00004684865,0.000002130902],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3724135,0.0005876285,0.6246546,0.0002741354,0.00004823449,0.0000342512,0.0002090094,0.0005428078,0.001235824],"genre_scores_gemma":[0.967725,0.0001799646,0.03086371,0.00004382709,0.00001699109,0.00003886073,0.0001682886,0.00001385446,0.000949536],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003512792,"threshold_uncertainty_score":0.006984711,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02797058286036633,"score_gpt":0.2585745639688526,"score_spread":0.2306039811084863,"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."}}