{"id":"W4410164379","doi":"10.53555/sfs.v10i3.3576","title":"Integrating AI and Big Data in Healthcare: A Scalable Approach to Personalized Medicine","year":2023,"lang":"en","type":"article","venue":"Journal of Survey in Fisheries Sciences","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Big data; Personalized medicine; Health care; Data science; Scalability; Computer science; Data mining; Database; Bioinformatics; Political science; Biology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02113337,0.0001367286,0.0004272791,0.0008079113,0.0001989597,0.0002332669,0.002160668,0.00005750893,0.00000369703],"category_scores_gemma":[0.005375263,0.00009873834,0.00001673777,0.004865013,0.0003860802,0.00104001,0.0006954089,0.0005797692,0.000001403014],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007311135,"about_ca_system_score_gemma":0.0004889257,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01585572,"about_ca_topic_score_gemma":0.007226653,"domain_scores_codex":[0.9964261,0.001088211,0.0007372053,0.0004744895,0.0008237152,0.0004502672],"domain_scores_gemma":[0.9979542,0.0009808928,0.0002793701,0.0003935018,0.0001957032,0.0001963113],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001759882,0.00001752993,0.9670411,0.00005575072,0.000002130822,0.00001993173,0.004662737,0.0001521753,0.00001401623,0.000827894,0.002284905,0.02490417],"study_design_scores_gemma":[0.0004462913,0.0005033229,0.9375263,0.0003240369,0.000001061259,0.0001019471,0.001427231,0.05640905,0.000002529003,0.001303399,0.001797076,0.0001577323],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7993229,0.001118361,0.02002134,0.176559,0.001861519,0.0003436393,0.00001738768,0.00006730598,0.0006885789],"genre_scores_gemma":[0.9685397,0.000194829,0.0292072,0.001806076,0.0001478694,0.000004998205,0.000006055277,0.000008018096,0.00008532772],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1747529,"threshold_uncertainty_score":0.9906978,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3644229481714839,"score_gpt":0.3763466992898259,"score_spread":0.01192375111834199,"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."}}