{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003473842,0.0007000833,0.0009200721,0.0009311878,0.0004075555,0.0005540634,0.001088148,0.0008884434,0.003887503],"category_scores_gemma":[0.0005961887,0.0002855307,0.000449673,0.0008561021,0.00009351528,0.0008669395,0.0005329562,0.0005987218,0.002233188],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005269626,"about_ca_system_score_gemma":0.0006680593,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01219014,"about_ca_topic_score_gemma":0.02037453,"domain_scores_codex":[0.9997934,0.00001870368,0.00001964381,0.00008666197,0.00005127853,0.00003041698],"domain_scores_gemma":[0.9997508,0.00004991948,0.00001842136,0.00003666299,0.0001098737,0.0000343065],"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":[0.001462981,0.001003426,0.01032698,0.0002976132,0.0005530759,0.0006900847,0.00007879523,0.03619884,0.06801678,0.001046841,0.0308259,0.8494987],"study_design_scores_gemma":[0.0001108445,0.0002988851,0.004936828,0.00002454323,0.0002721977,0.0004761417,0.00003249069,0.9624316,0.02320988,0.001327046,0.006817746,0.00006190631],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1884199,0.003504082,0.7548872,0.001444085,0.001147916,0.0003524326,0.005492973,0.03528342,0.009467968],"genre_scores_gemma":[0.7574471,0.0009568341,0.2188266,0.001208066,0.0003603714,0.0002004683,0.005247768,0.0002108034,0.01554196],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01219014,"threshold_uncertainty_score":0.02423835,"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."}}