{"id":"W3044964533","doi":"10.1016/j.artmed.2020.101931","title":"Indoor location identification of patients for directing virtual care: An AI approach using machine learning and knowledge-based methods","year":2020,"lang":"en","type":"article","venue":"Artificial Intelligence in Medicine","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Heuristics; Identification (biology); Artificial intelligence; Machine learning; Process (computing); Health care; Analytics; Semantics (computer science); Human–computer interaction; Data science","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":[0.0005115774,0.000714952,0.0007766759,0.002673401,0.0005979167,0.001486836,0.00118385,0.0009864278,0.001784886],"category_scores_gemma":[0.002389036,0.000245347,0.0008369245,0.001676556,0.000372292,0.001027372,0.0007741923,0.0006699532,0.000837102],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007377543,"about_ca_system_score_gemma":0.001390635,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009264506,"about_ca_topic_score_gemma":0.01028342,"domain_scores_codex":[0.9994794,0.0001309847,0.00004242074,0.0001289085,0.0001425051,0.00007589509],"domain_scores_gemma":[0.9988887,0.0004571457,0.0001558993,0.00009644264,0.0003463251,0.00005557987],"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":[0.0003316691,0.0006663377,0.03156628,0.0003929708,0.0002342868,0.0008002473,0.0006456443,0.2951888,0.01722327,0.01064997,0.005849156,0.6364514],"study_design_scores_gemma":[0.00001362948,0.00008273491,0.005377558,0.00004689745,0.00006362482,0.0002786045,0.0004365555,0.9825822,0.003960412,0.005483296,0.001642546,0.00003194994],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05850104,0.0004514952,0.9307318,0.0006207148,0.0001109762,0.0001189403,0.0003566161,0.0009656776,0.008142758],"genre_scores_gemma":[0.7548245,0.0004205309,0.2402447,0.0002138345,0.00009690555,0.0000959425,0.0004527469,0.00004618688,0.003604694],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009264506,"threshold_uncertainty_score":0.01842117,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06088236808318261,"score_gpt":0.3656424298976679,"score_spread":0.3047600618144853,"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."}}