{"id":"W2963943051","doi":"10.2196/14401","title":"Characterizing Artificial Intelligence Applications in Cancer Research: A Latent Dirichlet Allocation Analysis","year":2019,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Artificial intelligence; Latent Dirichlet allocation; Machine learning; Computer science; Precision medicine; Cancer; Applications of artificial intelligence; Jaccard index; Medicine; Data science; Topic model; Cluster analysis","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001448906,0.0001217047,0.0003258148,0.0007695327,0.00009622116,0.00004438642,0.0002010881,0.0002372537,0.001247499],"category_scores_gemma":[0.0002262243,0.0001030086,0.00009115745,0.002695737,0.0001227726,0.0002280453,0.00006075046,0.000714103,0.0006377476],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003085309,"about_ca_system_score_gemma":0.0005798708,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006882729,"about_ca_topic_score_gemma":0.0003871926,"domain_scores_codex":[0.9971926,0.00007128521,0.001110609,0.0001725946,0.001026014,0.0004268764],"domain_scores_gemma":[0.9984867,0.0002615113,0.000152174,0.0003916672,0.0003790308,0.0003289049],"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":[0.000220065,0.001547645,0.3261953,0.00140314,0.0003075907,0.000008216236,0.06898609,0.0003963772,0.0009279737,0.0173678,0.0005497772,0.58209],"study_design_scores_gemma":[0.0002479451,0.001041929,0.1908488,0.002124274,0.000554787,0.00003091457,0.1253109,0.6055037,0.02829606,0.008533832,0.03625157,0.0012552],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9868087,0.00006513272,0.005293139,0.005181261,0.0001826583,0.001361489,0.000005013352,0.00004972807,0.001052922],"genre_scores_gemma":[0.9968259,0.0003269161,0.0004977527,0.001116028,0.0002409404,0.0006552302,0.000167932,0.00001076386,0.0001585546],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6051074,"threshold_uncertainty_score":0.9996655,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2597726798504918,"score_gpt":0.5032169644861352,"score_spread":0.2434442846356433,"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."}}