{"id":"W4389085809","doi":"10.3390/jpm13121660","title":"Enhancing Predictive Power: Integrating a Linear Support Vector Classifier with Logistic Regression for Patient Outcome Prognosis in Virtual Reality Therapy for Treatment-Resistant Schizophrenia","year":2023,"lang":"en","type":"article","venue":"Journal of Personalized Medicine","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut national de psychiatrie légale Philippe-Pinel; Université de Montréal; Institut Universitaire en Santé Mentale de Québec","funders":"Otsuka Canada Pharmaceutical; Canada First Research Excellence Fund","keywords":"Logistic regression; Predictive power; Support vector machine; Outcome (game theory); Machine learning; Classifier (UML); Virtual reality; Medicine; Artificial intelligence; Physical medicine and rehabilitation; Computer science; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.004773897,0.001150633,0.001054983,0.001808329,0.0004154488,0.001224307,0.001098974,0.00119173,0.001406803],"category_scores_gemma":[0.01192437,0.0002675203,0.0008926896,0.0008917124,0.0002726777,0.0008969188,0.0009057183,0.001754748,0.0007557246],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006542095,"about_ca_system_score_gemma":0.0008460637,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005653485,"about_ca_topic_score_gemma":0.004091455,"domain_scores_codex":[0.9982419,0.0009844046,0.0001164786,0.0003110826,0.0001914398,0.0001547013],"domain_scores_gemma":[0.9940318,0.004432721,0.0004019368,0.0002831797,0.0006352995,0.0002149954],"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":[0.002311351,0.001866906,0.2373771,0.0002666835,0.0006171987,0.0003493221,0.0003294525,0.1611693,0.002802908,0.0007060858,0.007451696,0.5847521],"study_design_scores_gemma":[0.00003545903,0.0004206111,0.01381916,0.00005033245,0.0001036172,0.00007094805,0.0001269425,0.9830625,0.000933232,0.0009192189,0.0004307756,0.0000273036],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8360316,0.002821825,0.1525816,0.002529486,0.0003052199,0.0003115723,0.001630508,0.001289543,0.002498605],"genre_scores_gemma":[0.9800248,0.0002504092,0.01797592,0.0001233823,0.00009610086,0.00008996797,0.0008847373,0.00001947528,0.0005352718],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005653485,"threshold_uncertainty_score":0.0252471,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07581010742608477,"score_gpt":0.3830934125846362,"score_spread":0.3072833051585514,"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."}}