{"id":"W4402443124","doi":"10.2196/56343","title":"Application of Spatial Analysis on Electronic Health Records to Characterize Patient Phenotypes: Systematic Review","year":2024,"lang":"en","type":"review","venue":"JMIR Medical Informatics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"U.S. National Library of Medicine; National Cancer Institute","keywords":"Geocoding; Medicine; MEDLINE; Medical diagnosis; Data science; Clinical decision support system; Data mining; Computer science; Cartography; Decision support system; Pathology; Geography","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001473971,0.0005655366,0.006577242,0.000718647,0.00003369802,0.00002324519,0.0005092588,0.0002990256,0.0002041313],"category_scores_gemma":[0.0008229752,0.0003696165,0.001150136,0.002172867,0.00005525826,0.00006322935,0.0001827217,0.0008873183,0.001238264],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007500692,"about_ca_system_score_gemma":0.00202794,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004152252,"about_ca_topic_score_gemma":0.00002807345,"domain_scores_codex":[0.9925208,0.0002981477,0.004386964,0.0003434967,0.001925622,0.0005249897],"domain_scores_gemma":[0.9950762,0.0002091404,0.002105367,0.001607423,0.0001848277,0.0008170598],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","study_design_scores_codex":[0.000008826419,0.0001069771,7.164633e-7,0.6434529,0.001154972,0.000003200682,0.0001379369,8.659649e-8,3.290185e-9,0.00006490647,0.002373965,0.3526954],"study_design_scores_gemma":[0.0001025066,0.0004673123,0.000005441026,0.5543355,0.009431027,0.00002121381,0.00002016889,0.000490805,4.158645e-8,0.000003143922,0.4348978,0.0002251278],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.000004807597,0.9867029,0.002176595,0.0003135433,0.0001530536,0.009360983,0.0008313513,0.0001478146,0.0003089027],"genre_scores_gemma":[0.00005736492,0.9869632,0.0001614397,0.004263821,0.0001261229,0.002313836,0.006002954,0.0000564543,0.00005486206],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.4325238,"threshold_uncertainty_score":0.9998756,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01914301187259774,"score_gpt":0.3596782694396949,"score_spread":0.3405352575670972,"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."}}