{"id":"W4321225553","doi":"10.1038/s41598-023-29334-0","title":"Hybrid feature engineering of medical data via variational autoencoders with triplet loss: a COVID-19 prognosis study","year":2023,"lang":"en","type":"article","venue":"Scientific Reports","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University; McGill University","funders":"","keywords":"Autoencoder; Artificial intelligence; Machine learning; Feature engineering; Computer science; Dimensionality reduction; Random forest; Feature (linguistics); Logistic regression; Feature learning; Overfitting; Regularization (linguistics); Artificial neural network; Deep learning","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":[0.003674554,0.0008036248,0.0009109558,0.0005339116,0.0001637767,0.0007306145,0.0007089434,0.0008479381,0.0004701001],"category_scores_gemma":[0.005619866,0.0003209117,0.0008926607,0.0004290164,0.0004668926,0.001081301,0.0008866134,0.001317465,0.0001155318],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005585856,"about_ca_system_score_gemma":0.0006973735,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003594325,"about_ca_topic_score_gemma":0.002198061,"domain_scores_codex":[0.9991805,0.0003932815,0.00004706273,0.0001529951,0.0001507309,0.00007551334],"domain_scores_gemma":[0.9976591,0.001586529,0.0001844395,0.0001606253,0.0003078371,0.0001015178],"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":[0.0002928281,0.0002287163,0.01454157,0.00007367236,0.000263723,0.0002077883,0.00008384891,0.8932348,0.003029329,0.005098116,0.001100968,0.08184458],"study_design_scores_gemma":[0.000002393423,0.0000324776,0.0004628423,0.000002186931,0.000005771289,0.00001807294,0.000004164498,0.9986537,0.0002445324,0.0005118386,0.00005850168,0.000003581172],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3223761,0.001609181,0.6732872,0.001087039,0.0000903439,0.00005572699,0.0001885928,0.0003394829,0.0009662372],"genre_scores_gemma":[0.9535656,0.000451389,0.04430689,0.0001363937,0.0000691716,0.00003313788,0.0003740903,0.0000297829,0.001033509],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003674554,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04674180753237518,"score_gpt":0.3413057240569369,"score_spread":0.2945639165245618,"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."}}