{"id":"W4391041530","doi":"10.11591/ijeecs.v33.i2.pp1196-1204","title":"Multimodal approach for early prediction of COVID-19 disease using convolutional neural network","year":2024,"lang":"en","type":"article","venue":"Indonesian Journal of Electrical Engineering and Computer Science","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Trinity College","funders":"","keywords":"Convolutional neural network; Artificial intelligence; Random forest; Coronavirus disease 2019 (COVID-19); Computer science; Classifier (UML); Feature selection; Pattern recognition (psychology); Decision tree; Machine learning; Medicine; Disease; Pathology; Infectious disease (medical specialty)","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005984995,0.0001103425,0.0002318943,0.0003940174,0.00009266342,0.00007370345,0.0001328632,0.00003953723,6.662329e-7],"category_scores_gemma":[0.0002221539,0.0000940295,0.0001122378,0.0008734032,0.0001326976,0.0002068116,0.00003166922,0.0002231234,5.890112e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001941769,"about_ca_system_score_gemma":0.0009098018,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005597853,"about_ca_topic_score_gemma":1.486849e-8,"domain_scores_codex":[0.9987606,0.00001702697,0.0003394838,0.0002216608,0.0003937947,0.0002673706],"domain_scores_gemma":[0.998879,0.0002845849,0.00008050407,0.00008796644,0.0001669357,0.0005010672],"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":[0.0002106472,0.0001160306,0.009620795,0.0004219872,0.00005396015,0.00007753546,0.0001336628,0.9802865,0.002413777,0.0008485954,0.0002507117,0.005565788],"study_design_scores_gemma":[0.0006101265,0.0004360958,0.0380878,0.0001637142,0.00008047648,0.0004249182,9.72689e-7,0.9598266,0.00006534415,0.00004730946,0.0001856983,0.00007088242],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3034317,0.000938062,0.6945249,0.0006527635,0.0003036939,0.0001172437,0.000003656301,0.00002763502,2.986235e-7],"genre_scores_gemma":[0.9501889,0.00001034976,0.04877267,0.0003226093,0.0006893852,0.000003090066,0.00000113152,0.00001054505,0.000001271135],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6467572,"threshold_uncertainty_score":0.3834412,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0236569689388738,"score_gpt":0.2762220253018829,"score_spread":0.2525650563630091,"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."}}