{"id":"W4383955190","doi":"10.1016/j.future.2023.07.004","title":"A heterogeneous network embedded medicine recommendation system based on LSTM","year":2023,"lang":"en","type":"article","venue":"Future Generation Computer Systems","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":51,"is_retracted":false,"has_abstract":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Computer science; Recommender system; Preprocessor; Process (computing); Data pre-processing; Machine learning; Novelty; Medical diagnosis; Artificial intelligence; Recurrent neural network; Data mining; Artificial neural network","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00140571,0.0003429435,0.0004387859,0.000365327,0.0005280981,0.0003354282,0.0007298848,0.0002054283,0.00001739785],"category_scores_gemma":[0.00002145749,0.0002941871,0.00009067774,0.001307254,0.00001691731,0.000191982,0.0001428644,0.0003110667,0.0003042362],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002328288,"about_ca_system_score_gemma":0.00009220715,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006647642,"about_ca_topic_score_gemma":0.00002047624,"domain_scores_codex":[0.9960554,0.001162985,0.0007504953,0.0008639676,0.000640612,0.000526501],"domain_scores_gemma":[0.9978819,0.0002206145,0.0003705185,0.001053546,0.0002651883,0.0002082082],"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":[0.000007186315,0.00001035066,0.0001304174,0.0001610893,0.00001665948,0.00002937057,0.0003656943,0.6380903,0.00001696291,0.009304293,0.3397461,0.01212157],"study_design_scores_gemma":[0.0004038767,0.0003138049,0.0002524381,0.0001866932,0.000005231802,0.00005498424,0.00002840765,0.8692886,0.00001193265,0.000002562051,0.1292184,0.0002331531],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.000720931,0.0001642421,0.8715481,0.007283922,0.1172863,0.0006906085,0.00000612671,0.001899028,0.0004006779],"genre_scores_gemma":[0.5150236,0.0000207881,0.0494276,0.005993912,0.4269627,0.0005071555,0.001448241,0.0001326674,0.000483253],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8221205,"threshold_uncertainty_score":0.999951,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0314724459549699,"score_gpt":0.2800330875402446,"score_spread":0.2485606415852747,"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."}}