{"id":"W4410380704","doi":"10.1007/s10728-025-00519-0","title":"Data Privacy in Medical Informatics and Electronic Health Records: A Bibliometric Analysis","year":2025,"lang":"en","type":"article","venue":"Health Care Analysis","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Akdeniz Üniversitesi; Türkiye Bilimsel ve Teknolojik Araştırma Kurumu","keywords":"Health informatics; Computer science; Data science; Cloud computing; Analytics; Big data; Authentication (law); Information privacy; Encryption; Health care; Internet privacy; World Wide Web; Data mining; Computer security; Political science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["bibliometrics"],"consensus_categories":["bibliometrics"],"category_scores_codex":[0.002741657,0.0001785513,0.00109756,0.08467337,0.0002129102,0.00005152933,0.0003348629,0.0001722712,0.0001735762],"category_scores_gemma":[0.0008855608,0.0001660042,0.0001717238,0.2596602,0.00005995604,0.0001970142,0.0001753051,0.0005540413,0.00001158923],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00102837,"about_ca_system_score_gemma":0.006286262,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.02899821,"about_ca_topic_score_gemma":0.06956053,"domain_scores_codex":[0.9962121,0.0002380609,0.001674559,0.0004633156,0.0006642821,0.0007476915],"domain_scores_gemma":[0.997237,0.0003587142,0.0003625049,0.001202157,0.0002992023,0.0005404372],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00004082195,0.00009849556,0.4711522,0.0007080582,0.001207269,0.000002526833,0.003557193,0.00004452988,5.368003e-8,0.00008533741,0.002829099,0.5202745],"study_design_scores_gemma":[0.0004675816,0.000908715,0.7246355,0.0005249497,0.006139531,0.00001363176,0.03507076,0.1717681,0.00001193417,0.00027991,0.05972265,0.000456702],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8421397,0.0605452,0.03280565,0.06226659,0.0002496085,0.001141442,0.0001013352,0.0001187424,0.0006317717],"genre_scores_gemma":[0.9544882,0.0278596,0.001617805,0.01415504,0.00007689841,0.0000301331,0.001665445,0.000009303008,0.00009755647],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5198178,"threshold_uncertainty_score":0.9993472,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1181633002920713,"score_gpt":0.5030142527736667,"score_spread":0.3848509524815955,"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."}}