{"id":"W3015587876","doi":"10.2196/16129","title":"Precision Health–Enabled Machine Learning to Identify Need for Wraparound Social Services Using Patient- and Population-Level Data Sets: Algorithm Development and Validation","year":2020,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Food Security and Health in Diverse Populations","field":"Health Professions","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Robert Wood Johnson Foundation","keywords":"Machine learning; Leverage (statistics); Health care; Population; Computer science; Social determinants of health; Artificial intelligence; Population health; Medicine; Data mining; Public health; Nursing; Environmental health","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.001135629,0.0001687985,0.0003585798,0.0001253992,0.002273271,0.00006371028,0.0002180639,0.0002632013,0.00005985883],"category_scores_gemma":[0.0003083311,0.000160578,0.00001829917,0.0002627653,0.00002845798,0.0007235745,0.0006428498,0.0005365182,0.00001952019],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001301588,"about_ca_system_score_gemma":0.0004103257,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004334229,"about_ca_topic_score_gemma":0.0002445573,"domain_scores_codex":[0.9971351,0.0001985248,0.001372285,0.0002103513,0.0006514261,0.0004323029],"domain_scores_gemma":[0.9982421,0.0002988169,0.0005440712,0.0001485856,0.0001518152,0.0006146143],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002815213,0.0001068788,0.02445703,0.01074055,0.00008835867,0.000001346202,0.5859408,0.0001124053,0.00000392682,0.0005834567,0.004358342,0.3733253],"study_design_scores_gemma":[0.002355373,0.0002882247,0.01996815,0.0009949534,0.00003499692,0.000003354053,0.05152938,0.8460244,0.000002697624,0.0003331056,0.07811684,0.0003485284],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9578794,0.0001216238,0.03042069,0.007708561,0.0003571593,0.002813616,0.0005438448,0.0001221694,0.00003294944],"genre_scores_gemma":[0.6016141,0.0001463223,0.3551981,0.02948223,0.0006714293,0.000304552,0.01248801,0.00006555489,0.00002967326],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.845912,"threshold_uncertainty_score":0.9990256,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.277019598799788,"score_gpt":0.4981201742458389,"score_spread":0.2211005754460509,"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."}}