{"id":"W4296466187","doi":"10.18280/mmep.090423","title":"Model Based Risk Assessment to Evaluate Lung Functionality for Early Prognosis of Asthma Using Neural Network Approach","year":2022,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Quality and Safety in Healthcare","field":"Health Professions","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Artificial neural network; Spirometry; Generalization; Computer science; Population; Radial basis function; Asthma; Machine learning; Artificial intelligence; Lung function; Support vector machine; Statistics; Medicine; Mathematics; Lung; Internal medicine","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":[0.0008529295,0.0007273351,0.0006227909,0.001001474,0.0002007971,0.0008261817,0.0006504488,0.0007055851,0.0017348],"category_scores_gemma":[0.002051536,0.0001847819,0.0006023521,0.0004199176,0.0001632086,0.000584505,0.0004507189,0.0005641952,0.0001981719],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006336243,"about_ca_system_score_gemma":0.0006346802,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008356636,"about_ca_topic_score_gemma":0.006703768,"domain_scores_codex":[0.9997479,0.00008306186,0.00001905241,0.00005716668,0.00006141665,0.00003134574],"domain_scores_gemma":[0.9994767,0.0003210941,0.00005432847,0.00001708053,0.0001117334,0.00001900835],"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":[0.0001051586,0.00009878599,0.01121389,0.00007125412,0.0001123341,0.0001020022,0.0000279267,0.9471002,0.001347917,0.001810275,0.0007139936,0.03729646],"study_design_scores_gemma":[0.000001919479,0.00001872855,0.0008874366,0.000005723603,0.00001120354,0.00001413093,0.00000487846,0.9981397,0.0001619282,0.000672257,0.00007811494,0.000003935941],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1766947,0.001745484,0.814618,0.0007687045,0.0001070613,0.0001320541,0.0007230075,0.0006388599,0.004572057],"genre_scores_gemma":[0.9682275,0.0004030006,0.02867928,0.00006467733,0.0000374748,0.0001412762,0.000412204,0.0000188621,0.002015728],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008356636,"threshold_uncertainty_score":0.01661599,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1920025869887789,"score_gpt":0.399972301151058,"score_spread":0.2079697141622791,"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."}}