{"id":"W2081596682","doi":"10.1142/s0129065709001987","title":"TREATING EPILEPSY VIA ADAPTIVE NEUROSTIMULATION: A REINFORCEMENT LEARNING APPROACH","year":2009,"lang":"en","type":"article","venue":"International Journal of Neural Systems","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":93,"is_retracted":false,"has_abstract":true,"ca_institutions":"Montreal Neurological Institute and Hospital; McGill University","funders":"Canadian Institutes of Health Research","keywords":"Neurostimulation; Reinforcement learning; Computer science; Epilepsy; Artificial intelligence; Brain stimulation; Duration (music); Electroencephalography; Machine learning; Stimulation; Neuroscience; Psychology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0006674818,0.000528819,0.0006047296,0.0002719715,0.0002024,0.0003311593,0.0008666926,0.0007142295,0.001078176],"category_scores_gemma":[0.001501232,0.0002240311,0.0003749513,0.0001504225,0.0007726829,0.0004429387,0.0004528018,0.0006835115,0.0001409661],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004565165,"about_ca_system_score_gemma":0.0005226631,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001064099,"about_ca_topic_score_gemma":0.001146594,"domain_scores_codex":[0.9997543,0.00009123044,0.00001462779,0.00005157426,0.00006572762,0.00002257823],"domain_scores_gemma":[0.999468,0.0003529943,0.00007816304,0.000026233,0.00005366567,0.00002103238],"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":[0.00009002935,0.0001404925,0.0006279738,0.0001140839,0.00007489778,0.0001331667,0.00007794578,0.8516074,0.008560528,0.01205901,0.0007815253,0.1257329],"study_design_scores_gemma":[0.00002661758,0.00007482634,0.0000940528,0.000008354263,0.00001142658,0.00004201438,0.000005147791,0.9925719,0.00147098,0.004995943,0.0006898613,0.000008897283],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008388068,0.0002165549,0.9895864,0.0002465572,0.00002121492,0.00004490385,0.000008486717,0.0001561697,0.001331642],"genre_scores_gemma":[0.6929939,0.000422471,0.3036118,0.0002748127,0.00008000689,0.0002787257,0.00003039802,0.00006088019,0.002246989],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001078176,"threshold_uncertainty_score":0.003606915,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04415346886069817,"score_gpt":0.2944864131664126,"score_spread":0.2503329443057144,"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."}}