{"id":"W2963128167","doi":"10.17760/d20291511","title":"Leveraging big data to forecast short-term hospital resource demand","year":2018,"lang":"en","type":"dissertation","venue":"","topic":"Healthcare Policy and Management","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Staffing; Liberian dollar; Per capita; Health care; Resource (disambiguation); Business; Value (mathematics); Variable (mathematics); Demand patterns; Environmental economics; Operations management; Economics; Demand management; Computer science; Finance; Medicine; Economic growth; Environmental health","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.003166926,0.001161806,0.0007362935,0.002439974,0.0003587696,0.002299144,0.0008985671,0.001406941,0.001688996],"category_scores_gemma":[0.02057936,0.0005655885,0.000701844,0.002702297,0.0003757043,0.002795844,0.001072905,0.002298362,0.000730465],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001499576,"about_ca_system_score_gemma":0.001169001,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01897622,"about_ca_topic_score_gemma":0.02041738,"domain_scores_codex":[0.9990069,0.0003655497,0.0001043241,0.000180209,0.0002052403,0.0001378634],"domain_scores_gemma":[0.9900192,0.006737707,0.0009264936,0.0005829976,0.001217162,0.0005164888],"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":[0.0007533808,0.0005161444,0.2261262,0.0003271033,0.0005912451,0.0003756117,0.0002218492,0.6213011,0.0005745933,0.009320803,0.03394704,0.105945],"study_design_scores_gemma":[0.00002515935,0.00005445371,0.02605695,0.00007289855,0.00003348365,0.00003023917,0.0001906252,0.9518016,0.0003130393,0.01767804,0.003707165,0.00003634817],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8159535,0.007126855,0.08189083,0.03285325,0.001379743,0.0002604897,0.04583033,0.001146203,0.01355889],"genre_scores_gemma":[0.9576774,0.002306317,0.01540916,0.0006509385,0.0004356909,0.00009815703,0.02200491,0.00008527299,0.001332338],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01897622,"threshold_uncertainty_score":0.03773153,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1652446369150849,"score_gpt":0.3145999533632299,"score_spread":0.149355316448145,"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."}}