{"id":"W2990538347","doi":"10.1186/s13638-019-1582-2","title":"Particularities of data mining in medicine: lessons learned from patient medical time series data analysis","year":2019,"lang":"en","type":"article","venue":"EURASIP Journal on Wireless Communications and Networking","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Process (computing); Knowledge extraction; Data science; Data mining; Task (project management); Domain knowledge; Domain (mathematical analysis); Time series; Artificial intelligence; Machine learning","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001901074,0.0001459342,0.0005429728,0.0002551683,0.0002865514,0.0001688303,0.004196311,0.00006796776,0.00008878995],"category_scores_gemma":[0.00008390343,0.0001184126,0.00005506786,0.001047965,0.0001837663,0.0009716767,0.00353292,0.0004502733,0.000003263332],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001846624,"about_ca_system_score_gemma":0.00006671173,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001726054,"about_ca_topic_score_gemma":0.0003976396,"domain_scores_codex":[0.9976057,0.0004908113,0.0007584402,0.0004057523,0.0005012683,0.000238053],"domain_scores_gemma":[0.9945801,0.0009302524,0.0005091525,0.003777799,0.00007715046,0.0001255755],"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":[0.0000317037,0.0001376088,0.03497892,0.00001016251,0.0007948392,0.00002580146,0.002912931,0.001275259,0.00009666472,0.00293612,0.0003624325,0.9564376],"study_design_scores_gemma":[0.0002791027,0.0000920297,0.003041407,0.0004996301,0.000151149,0.0000193258,0.0007795891,0.9864223,0.00000501629,0.0003334096,0.008227367,0.0001496674],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7947778,0.03371609,0.09960344,0.06709751,0.000658649,0.0003742675,0.0001482744,0.0001248918,0.003499083],"genre_scores_gemma":[0.9766551,0.008573558,0.01430359,0.0001350068,0.00008839637,0.000001270384,0.0002056796,0.00000948769,0.00002790639],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9851471,"threshold_uncertainty_score":0.779786,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1439619922344543,"score_gpt":0.3378294596297186,"score_spread":0.1938674673952643,"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."}}