{"id":"W2949497754","doi":"10.2196/11966","title":"Deep Learning Intervention for Health Care Challenges: Some Biomedical Domain Considerations","year":2019,"lang":"en","type":"article","venue":"JMIR mhealth and uhealth","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":187,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Big data; Computer science; mHealth; Data science; Health care; Deep learning; Artificial intelligence; Domain (mathematical analysis); Categorization; Data mining","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001539187,0.0002577071,0.0005096132,0.0003083067,0.0008186239,0.0001117591,0.0003032497,0.000185588,0.00002807623],"category_scores_gemma":[0.0001467856,0.000251527,0.0001066519,0.0002129963,0.00006444148,0.00038564,0.0001579443,0.0007175223,0.00004380277],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003459971,"about_ca_system_score_gemma":0.0007789544,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002736055,"about_ca_topic_score_gemma":0.0002392991,"domain_scores_codex":[0.9962566,0.0007168003,0.0008041533,0.0008595433,0.0003944205,0.0009685099],"domain_scores_gemma":[0.997523,0.0004985519,0.0004645641,0.000524914,0.0001544326,0.000834586],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00008597907,0.0002333777,0.003439033,0.01743105,0.00002449533,0.000006230052,0.05313388,0.0001188404,0.000005151601,0.4910094,0.001430041,0.4330825],"study_design_scores_gemma":[0.02118916,0.03084571,0.1729642,0.002609554,0.00005845736,0.0005605409,0.03753103,0.2188598,0.00001100894,0.08848453,0.4239349,0.002951156],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.3038019,0.09884863,0.2361717,0.3450775,0.004559548,0.008787782,0.00005015677,0.00184957,0.0008531538],"genre_scores_gemma":[0.9551598,0.001869556,0.03609877,0.006006587,0.0003833954,0.0002779669,0.00008649583,0.00003866731,0.00007874833],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6513579,"threshold_uncertainty_score":0.9999937,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04569724367189119,"score_gpt":0.3935224048884328,"score_spread":0.3478251612165416,"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."}}