{"id":"W3216052844","doi":"10.2196/29978","title":"Reduction of Platelet Outdating and Shortage by Forecasting Demand With Statistical Learning and Deep Neural Networks: Modeling Study","year":2021,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Blood donation and transfusion practices","field":"Business, Management and Accounting","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Obsolescence; Economic shortage; Computer science; Task (project management); Artificial neural network; Demand forecasting; Platelet; Product (mathematics); Operations management; Business; Artificial intelligence; Operations research; Engineering; Marketing; Medicine; Systems engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005536815,0.0001192848,0.000206894,0.00006847992,0.0001986269,0.000212191,0.00005036761,0.00007019605,0.0001003427],"category_scores_gemma":[0.0002802038,0.00009666089,0.00001210755,0.0002076514,0.00006582313,0.001162622,0.00009819752,0.0003566506,7.382802e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003909089,"about_ca_system_score_gemma":0.00001429481,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002989021,"about_ca_topic_score_gemma":0.00003864842,"domain_scores_codex":[0.998745,0.00002400723,0.0004998116,0.0001161082,0.0004491844,0.000165845],"domain_scores_gemma":[0.9993926,0.0001462273,0.0002243921,0.00005833917,0.0001363005,0.00004213687],"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":[0.0004241608,0.001043342,0.1285083,0.002644123,0.0004366067,0.0001920432,0.01288043,0.03252517,0.00006136975,0.004184959,0.0003081993,0.8167912],"study_design_scores_gemma":[0.0009509493,0.00004285631,0.0002255076,0.0000635388,0.0000616236,0.00005577721,0.02080262,0.977363,0.000003701911,0.0000383826,0.0002799128,0.0001120705],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9148468,0.0001003893,0.08378529,0.0001431398,0.00005440642,0.000191472,8.836847e-7,0.00004095195,0.0008366196],"genre_scores_gemma":[0.9978234,0.00003928694,0.001742226,0.0002096425,0.0001052275,0.00001088847,0.00004914791,0.00001131308,0.000008824147],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9448379,"threshold_uncertainty_score":0.3941717,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01837060497455158,"score_gpt":0.2646819152784936,"score_spread":0.246311310303942,"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."}}