{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007984032,0.001130364,0.001981756,0.00291752,0.001264553,0.006156188,0.003581628,0.001910042,0.004506692],"category_scores_gemma":[0.01734407,0.0009855429,0.001952053,0.004947265,0.001902339,0.007952073,0.007251733,0.004345397,0.001462345],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001982367,"about_ca_system_score_gemma":0.003601991,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005133354,"about_ca_topic_score_gemma":0.006024053,"domain_scores_codex":[0.9948155,0.002163643,0.0004723048,0.000812192,0.001469217,0.0002669912],"domain_scores_gemma":[0.9891993,0.005539277,0.000541329,0.002458745,0.001479653,0.0007815935],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005861651,0.0003570432,0.00712154,0.00161232,0.0009664435,0.0008153431,0.001098764,0.1100505,0.004478229,0.1593261,0.08738635,0.6262012],"study_design_scores_gemma":[0.0001081342,0.00009772557,0.001709109,0.000382269,0.0002021371,0.0002951301,0.0006501892,0.5186307,0.002015722,0.4069213,0.0688865,0.0001010729],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00868288,0.01707228,0.91102,0.03562456,0.001363295,0.0006983883,0.002722031,0.005929497,0.01688702],"genre_scores_gemma":[0.2527579,0.0112597,0.7204788,0.004489157,0.0021373,0.0007175041,0.004022334,0.0004169626,0.003720398],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007984032,"threshold_uncertainty_score":0.04222405,"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."}}