{"id":"W2111769930","doi":"10.1016/j.compbiomed.2004.07.007","title":"Hypoplastic left heart syndrome: knowledge discovery with a data mining approach","year":2004,"lang":"en","type":"article","venue":"Computers in Biology and Medicine","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":false,"ca_institutions":"Hospital for Sick Children; University of Toronto; SickKids Foundation","funders":"","keywords":"Hypoplastic left heart syndrome; Data collection; Data mining; Computer science; Metric (unit); Measure (data warehouse); Knowledge extraction; Medicine; Data acquisition; Machine learning; Artificial intelligence; Medical physics; Heart disease; Statistics; Internal medicine; Operations management","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001453427,0.0007191927,0.001196223,0.004242947,0.0004842821,0.001811013,0.001437418,0.000858649,0.0005817869],"category_scores_gemma":[0.006083032,0.0003234975,0.001531107,0.001755203,0.0005260279,0.0009302399,0.0007436359,0.001131089,0.0002682784],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004871954,"about_ca_system_score_gemma":0.001364273,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003675167,"about_ca_topic_score_gemma":0.003793729,"domain_scores_codex":[0.9989073,0.0002379393,0.0002612427,0.0002425825,0.0002917257,0.00005915],"domain_scores_gemma":[0.9963934,0.002733143,0.0003088512,0.0001827492,0.0002626674,0.0001192604],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000817085,0.001299477,0.1419002,0.0009185551,0.001422358,0.004517365,0.0003575265,0.04845011,0.009725823,0.004686373,0.004200317,0.7817048],"study_design_scores_gemma":[0.0002304904,0.000397432,0.03825171,0.0003680719,0.001487371,0.008205882,0.0006982773,0.8769615,0.01564264,0.05127368,0.006373617,0.0001093424],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2490691,0.006708127,0.7321609,0.003212923,0.0001310742,0.0005758321,0.003584371,0.0016764,0.002881305],"genre_scores_gemma":[0.6791434,0.002512451,0.3141215,0.0003228278,0.0001203784,0.0002473114,0.0029919,0.00003210145,0.0005081297],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004242947,"threshold_uncertainty_score":0.007686555,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05144625212388019,"score_gpt":0.3126340393951114,"score_spread":0.2611877872712312,"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."}}