{"id":"W2979284462","doi":"10.2196/15980","title":"Cohort Selection for Clinical Trials From Longitudinal Patient Records: Text Mining Approach","year":2019,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Engineering and Physical Sciences Research Council","keywords":"Computer science; Context (archaeology); Clinical trial; Task (project management); Medical record; Executable; Natural language processing; Artificial intelligence; Selection (genetic algorithm); Psychological intervention; Medicine; Information retrieval; Data mining; Nursing","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":[0.03546555,0.001460782,0.002770684,0.01512125,0.001438825,0.004406599,0.003726609,0.00223701,0.002101103],"category_scores_gemma":[0.08979616,0.0008760443,0.003291545,0.008596106,0.0007282787,0.00359376,0.002645954,0.002766869,0.001816217],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001690203,"about_ca_system_score_gemma":0.005724872,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00360621,"about_ca_topic_score_gemma":0.006282179,"domain_scores_codex":[0.9793539,0.008109737,0.004830861,0.0043526,0.002868934,0.0004838457],"domain_scores_gemma":[0.8724797,0.1013939,0.01009307,0.006623361,0.007649088,0.001760879],"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":[0.001564929,0.001202801,0.06489646,0.004670399,0.001230773,0.002276865,0.002503265,0.01766011,0.01142127,0.005471845,0.04346815,0.8436331],"study_design_scores_gemma":[0.001116623,0.001148301,0.03735505,0.002570153,0.001831544,0.003207512,0.003583665,0.7439412,0.03020915,0.08389845,0.09076866,0.0003696172],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05251045,0.004084679,0.8848511,0.005537502,0.0004307641,0.007729622,0.03107607,0.01224205,0.001537747],"genre_scores_gemma":[0.1014386,0.0009481632,0.8683665,0.0008280507,0.0004162877,0.00281796,0.02433522,0.0002283342,0.0006210153],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03546555,"threshold_uncertainty_score":0.187562,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1131650521275603,"score_gpt":0.4321035775214888,"score_spread":0.3189385253939285,"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."}}