{"id":"W4229456659","doi":"10.3390/jpm12050768","title":"A Novel Patient Similarity Network (PSN) Framework Based on Multi-Model Deep Learning for Precision Medicine","year":2022,"lang":"en","type":"article","venue":"Journal of Personalized Medicine","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Concordia University","funders":"","keywords":"Computer science; Autoencoder; Deep learning; Artificial intelligence; Convolutional neural network; Encoder; Similarity (geometry); Machine learning; Curse of dimensionality; Word embedding; Feature learning; Embedding; Data mining","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":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.004723245,0.0003134064,0.0008308428,0.0004094771,0.0008051415,0.00002335987,0.001084575,0.0001225752,0.0003652],"category_scores_gemma":[0.005600026,0.0002327218,0.0002348431,0.0007635649,0.0001358893,0.0001474478,0.0002271548,0.002420441,0.000001075887],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004244386,"about_ca_system_score_gemma":0.0003186289,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000424382,"about_ca_topic_score_gemma":0.000005070248,"domain_scores_codex":[0.9951352,0.0005939738,0.001121172,0.0004795945,0.002117005,0.0005529861],"domain_scores_gemma":[0.9941917,0.002957554,0.001372694,0.0004688109,0.0005996921,0.0004095617],"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":[0.001030702,0.0002901823,0.001204806,0.000107794,0.00004856372,0.00005687075,0.009234145,0.9395632,0.0002417308,0.003676858,0.005106531,0.03943864],"study_design_scores_gemma":[0.00543464,0.006592144,0.0006704099,0.0008040589,0.00005727575,0.000127907,0.0005334113,0.9500612,0.000003444988,0.001831883,0.03368368,0.0001999265],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002318146,0.001890986,0.9584435,0.03490843,0.001768568,0.0004825481,0.000003409722,0.00006335462,0.0001211185],"genre_scores_gemma":[0.4154995,0.00007570532,0.5671156,0.0152818,0.001659125,0.00006857753,0.00001572532,0.00005764248,0.0002263202],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4131814,"threshold_uncertainty_score":0.999881,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05258624034517451,"score_gpt":0.350932823783868,"score_spread":0.2983465834386935,"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."}}