{"id":"W2975466964","doi":"10.2196/12163","title":"Developing Machine Learning Algorithms for the Prediction of Early Death in Elderly Cancer Patients: Usability Study","year":2019,"lang":"en","type":"article","venue":"JMIR Cancer","topic":"Frailty in Older Adults","field":"Medicine","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medicine; C4.5 algorithm; Polypharmacy; Machine learning; Naive Bayes classifier; Algorithm; Multilayer perceptron; Decision tree; Cancer; Geriatric Depression Scale; Logistic regression; Internal medicine; Computer science; Artificial neural network; Anxiety; Support vector machine; Psychiatry","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":[],"consensus_categories":[],"category_scores_codex":[0.0002347496,0.00013906,0.0002871896,0.00006874408,0.00005406587,0.00001017206,0.0001112475,0.00006662381,0.0001420139],"category_scores_gemma":[0.0001010152,0.00009778063,0.00006012418,0.0002355428,0.00003150785,0.0001129149,0.0000407423,0.0002687238,0.000003571186],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004737712,"about_ca_system_score_gemma":0.0002054455,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007340401,"about_ca_topic_score_gemma":0.002294631,"domain_scores_codex":[0.9987313,0.00005101879,0.0003655629,0.0002990478,0.0003199868,0.0002330844],"domain_scores_gemma":[0.9991566,0.0001690587,0.0001392132,0.0002414482,0.0002548388,0.00003887379],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002560582,0.00024286,0.9510602,0.0001615262,0.00007774009,3.72438e-7,0.002901772,0.0002352595,0.0000706116,0.000005759507,0.00006240259,0.04492545],"study_design_scores_gemma":[0.004410753,0.001470882,0.9869518,0.0002941722,0.00005440352,3.749487e-7,0.000377815,0.003668041,0.0002258925,0.00001565981,0.002438084,0.00009210162],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9943194,0.0003574031,0.0002553564,0.0005660442,0.0004952819,0.003828249,0.0000848302,0.00003438384,0.00005910155],"genre_scores_gemma":[0.9971734,0.00006965124,0.0002496348,0.00009032575,0.0001218082,0.0015203,0.00001761277,0.0000257269,0.0007315369],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04483335,"threshold_uncertainty_score":0.9992698,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04141765506526159,"score_gpt":0.3439687238457957,"score_spread":0.3025510687805342,"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."}}