{"id":"W2947402118","doi":"10.2196/13946","title":"Predicting Posttraumatic Stress Disorder Risk: A Machine Learning Approach","year":2019,"lang":"en","type":"article","venue":"JMIR Mental Health","topic":"Mental Health via Writing","field":"Psychology","cited_by":59,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Random forest; Naive Bayes classifier; Logistic regression; Posttraumatic stress; Psychological intervention; Support vector machine; Machine learning; Intervention (counseling); Clinical psychology; Artificial intelligence; Psychology; Medicine; Computer science; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002823178,0.0008133813,0.0006953859,0.002015902,0.0003824616,0.001097123,0.0007484836,0.0009662943,0.0007775257],"category_scores_gemma":[0.008761885,0.0002898642,0.0006773369,0.0008481446,0.0002823629,0.0008876172,0.0005935201,0.001630397,0.000363458],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007580869,"about_ca_system_score_gemma":0.0007123442,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003740692,"about_ca_topic_score_gemma":0.003409968,"domain_scores_codex":[0.998943,0.0005566438,0.00007282998,0.0002075216,0.0001455608,0.00007437309],"domain_scores_gemma":[0.995819,0.003213756,0.0002892066,0.0001597142,0.0004214369,0.00009690747],"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":[0.0002896957,0.001145214,0.1300765,0.0001607153,0.000580626,0.0001827527,0.0001692596,0.4703347,0.001557444,0.002422848,0.005755458,0.3873248],"study_design_scores_gemma":[0.000007489557,0.00008339916,0.004845268,0.00002138583,0.00002545598,0.00003120232,0.00002641017,0.9911324,0.000266758,0.003316093,0.0002347513,0.000009350133],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4155805,0.002830912,0.5694821,0.004813349,0.0002200182,0.0003854137,0.001341472,0.001388585,0.003957667],"genre_scores_gemma":[0.930209,0.0004820452,0.06750099,0.0002896262,0.0001564166,0.0001468406,0.0005940634,0.00001428361,0.0006067158],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003740692,"threshold_uncertainty_score":0.01493055,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02560265170945674,"score_gpt":0.3685473931343947,"score_spread":0.342944741424938,"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."}}