Dynamical Phenotyping: Using Temporal Analysis of Clinically Collected Physiologic Data to Stratify Populations
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{"title"=>"Dynamical phenotyping: Using temporal analysis of clinically collected physiologic data to stratify populations", "type"=>"journal", "authors"=>[{"first_name"=>"D. J.", "last_name"=>"Albers", "scopus_author_id"=>"55341958200"}, {"first_name"=>"Noémie", "last_name"=>"Elhadad", "scopus_author_id"=>"6505774943"}, {"first_name"=>"E.", "last_name"=>"Tabak", "scopus_author_id"=>"6701511841"}, {"first_name"=>"A.", "last_name"=>"Perotte", "scopus_author_id"=>"17233935400"}, {"first_name"=>"George", "last_name"=>"Hripcsak", "scopus_author_id"=>"7004471151"}], "year"=>2014, "source"=>"PLoS ONE", "identifiers"=>{"scopus"=>"2-s2.0-84903172946", "sgr"=>"84903172946", "issn"=>"19326203", "doi"=>"10.1371/journal.pone.0096443", "pmid"=>"24933368", "isbn"=>"1932-6203 (Electronic)\\r1932-6203 (Linking)", "pui"=>"373381728"}, "id"=>"62be104b-8aaa-3460-b661-db0b0ca73868", "abstract"=>"Using glucose time series data from a well measured population drawn from an electronic health record (EHR) repository, the variation in predictability of glucose values quantified by the time-delayed mutual information (TDMI) was explained using a mechanistic endocrine model and manual and automated review of written patient records. The results suggest that predictability of glucose varies with health state where the relationship (e.g., linear or inverse) depends on the source of the acuity. It was found that on a fine scale in parameter variation, the less insulin required to process glucose, a condition that correlates with good health, the more predictable glucose values were. Nevertheless, the most powerful effect on predictability in the EHR subpopulation was the presence or absence of variation in health state, specifically, in- and out-of-control glucose versus in-control glucose. Both of these results are clinically and scientifically relevant because the magnitude of glucose is the most commonly used indicator of health as opposed to glucose dynamics, thus providing for a connection between a mechanistic endocrine model and direct insight to human health via clinically collected data.", "link"=>"http://www.mendeley.com/research/dynamical-phenotyping-using-temporal-analysis-clinically-collected-physiologic-data-stratify-populat", "reader_count"=>38, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>1, "Researcher"=>13, "Student > Doctoral Student"=>3, "Student > Ph. D. Student"=>12, "Student > Postgraduate"=>1, "Student > Master"=>3, "Other"=>1, "Student > Bachelor"=>1, "Lecturer"=>2}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>1, "Researcher"=>13, "Student > Doctoral Student"=>3, "Student > Ph. D. Student"=>12, "Student > Postgraduate"=>1, "Student > Master"=>3, "Other"=>1, "Student > Bachelor"=>1, "Lecturer"=>2}, "reader_count_by_subject_area"=>{"Unspecified"=>4, "Engineering"=>2, "Environmental Science"=>1, "Nursing and Health Professions"=>1, "Medicine and Dentistry"=>13, "Agricultural and Biological Sciences"=>3, "Social Sciences"=>2, "Computer Science"=>12}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>2}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>13}, "Social Sciences"=>{"Social Sciences"=>2}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>3}, "Computer Science"=>{"Computer Science"=>12}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>1}, "Unspecified"=>{"Unspecified"=>4}, "Environmental Science"=>{"Environmental Science"=>1}}, "reader_count_by_country"=>{"Netherlands"=>1, "United States"=>4, "France"=>1}, "group_count"=>5}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1536100"], "description"=>"<p>Depicted above are: (a) glucose time series for three different values of a linear constant affecting IDGU, ; (b) glucose time series density for three different values of a linear constant affecting IDGU, .</p>", "links"=>[], "tags"=>["Computational biology", "Population modeling", "physiology", "Endocrine physiology", "Population biology", "Theoretical biology", "Information technology", "data mining", "text mining", "signal processing", "Statistical signal processing", "Endocrinology", "research design", "Clinical research design", "Retrospective studies", "glucose", "linear", "affecting"], "article_id"=>1058400, "categories"=>["Biological Sciences"], "users"=>["D. J. Albers", "Noémie Elhadad", "E. Tabak", "A. Perotte", "George Hripcsak"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0096443.g010", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Depicted_above_are_a_glucose_time_series_for_three_different_values_of_a_linear_constant_affecting_IDGU_b_glucose_time_series_density_for_three_different_values_of_a_linear_constant_affecting_IDGU_/1058400", "title"=>"Depicted above are: (a) glucose time series for three different values of a linear constant affecting IDGU, ; (b) glucose time series density for three different values of a linear constant affecting IDGU, .", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-06-16 03:04:24"}
  • {"files"=>["https://ndownloader.figshare.com/files/1536070"], "description"=>"<p>Depicted above are the two different dynamical phenotyping strategies, <i>directive dynamical phenotyping</i> where the population is stratified and then characterized by differences in dynamics, and <i>undirected dynamical phenotyping</i> where a complex population is stratified by differences in dynamics.</p>", "links"=>[], "tags"=>["Computational biology", "Population modeling", "physiology", "Endocrine physiology", "Population biology", "Theoretical biology", "Information technology", "data mining", "text mining", "signal processing", "Statistical signal processing", "Endocrinology", "research design", "Clinical research design", "Retrospective studies", "dynamical", "phenotyping", "stratified", "characterized", "differences"], "article_id"=>1058377, "categories"=>["Biological Sciences"], "users"=>["D. J. Albers", "Noémie Elhadad", "E. Tabak", "A. Perotte", "George Hripcsak"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0096443.g001", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Depicted_above_are_the_two_different_dynamical_phenotyping_strategies_directive_dynamical_phenotyping_where_the_population_is_stratified_and_then_characterized_by_differences_in_dynamics_and_undirected_dynamical_phenotyping_where_a_complex_population_is_/1058377", "title"=>"Depicted above are the two different dynamical phenotyping strategies, <i>directive dynamical phenotyping</i> where the population is stratified and then characterized by differences in dynamics, and <i>undirected dynamical phenotyping</i> where a complex population is stratified by differences in dynamics.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-06-16 03:04:24"}
  • {"files"=>["https://ndownloader.figshare.com/files/1536106"], "description"=>"<p>Summary of the effects of various key parameters on the glucose dynamics, and TDMI that are observed when varying a parameter from below the nominal value to above the nominal value.</p>", "links"=>[], "tags"=>["Computational biology", "Population modeling", "physiology", "Endocrine physiology", "Population biology", "Theoretical biology", "Information technology", "data mining", "text mining", "signal processing", "Statistical signal processing", "Endocrinology", "research design", "Clinical research design", "Retrospective studies", "glucose", "tdmi", "observed", "varying", "parameter", "nominal"], "article_id"=>1058406, "categories"=>["Biological Sciences"], "users"=>["D. J. Albers", "Noémie Elhadad", "E. Tabak", "A. Perotte", "George Hripcsak"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0096443.t002", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Summary_of_the_effects_of_various_key_parameters_on_the_glucose_dynamics_and_TDMI_that_are_observed_when_varying_a_parameter_from_below_the_nominal_value_to_above_the_nominal_value_/1058406", "title"=>"Summary of the effects of various key parameters on the glucose dynamics, and TDMI that are observed when varying a parameter from below the nominal value to above the nominal value.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-06-16 03:04:24"}
  • {"files"=>["https://ndownloader.figshare.com/files/1536103"], "description"=>"<p>Depicted above are: (a) glucose time series for different values of the constant affecting insulin secretion, ; (b) glucose time series density for different values of the constant affecting insulin secretion, .</p>", "links"=>[], "tags"=>["Computational biology", "Population modeling", "physiology", "Endocrine physiology", "Population biology", "Theoretical biology", "Information technology", "data mining", "text mining", "signal processing", "Statistical signal processing", "Endocrinology", "research design", "Clinical research design", "Retrospective studies", "glucose", "affecting", "insulin"], "article_id"=>1058403, "categories"=>["Biological Sciences"], "users"=>["D. J. Albers", "Noémie Elhadad", "E. Tabak", "A. Perotte", "George Hripcsak"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0096443.g012", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Depicted_above_are_a_glucose_time_series_for_different_values_of_the_constant_affecting_insulin_secretion_b_glucose_time_series_density_for_different_values_of_the_constant_affecting_insulin_secretion_/1058403", "title"=>"Depicted above are: (a) glucose time series for different values of the constant affecting insulin secretion, ; (b) glucose time series density for different values of the constant affecting insulin secretion, .", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-06-16 03:04:24"}
  • {"files"=>["https://ndownloader.figshare.com/files/1536085"], "description"=>"<p>Depicted above are: (a) the glucose time series of an individual with high TDMI, , in the hrs bin — this individual falls into cluster two; (b) the glucose time series of an individual with low TDMI, , in the hrs bin — this individual falls into cluster one.</p>", "links"=>[], "tags"=>["Computational biology", "Population modeling", "physiology", "Endocrine physiology", "Population biology", "Theoretical biology", "Information technology", "data mining", "text mining", "signal processing", "Statistical signal processing", "Endocrinology", "research design", "Clinical research design", "Retrospective studies", "glucose", "bin", "falls"], "article_id"=>1058385, "categories"=>["Biological Sciences"], "users"=>["D. J. Albers", "Noémie Elhadad", "E. Tabak", "A. Perotte", "George Hripcsak"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0096443.g003", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Depicted_above_are_a_the_glucose_time_series_of_an_individual_with_high_TDMI_in_the_hrs_bin_this_individual_falls_into_cluster_two_b_the_glucose_time_series_of_an_individual_with_low_TDMI_in_the_hrs_bin_this_individual_falls_into_cluster_one_/1058385", "title"=>"Depicted above are: (a) the glucose time series of an individual with high TDMI, , in the hrs bin — this individual falls into cluster two; (b) the glucose time series of an individual with low TDMI, , in the hrs bin — this individual falls into cluster one.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-06-16 03:04:24"}
  • {"files"=>["https://ndownloader.figshare.com/files/1536097"], "description"=>"<p>Depicted above are: (a) glucose time series for three different values of a time constant for plasma insulin degradation (via kidney and liver filtering), ; (b) glucose time series density for three different values of a time constant for plasma insulin degradation (via kidney and liver filtering), .</p>", "links"=>[], "tags"=>["Computational biology", "Population modeling", "physiology", "Endocrine physiology", "Population biology", "Theoretical biology", "Information technology", "data mining", "text mining", "signal processing", "Statistical signal processing", "Endocrinology", "research design", "Clinical research design", "Retrospective studies", "glucose", "plasma", "insulin", "degradation", "kidney"], "article_id"=>1058397, "categories"=>["Biological Sciences"], "users"=>["D. J. Albers", "Noémie Elhadad", "E. Tabak", "A. Perotte", "George Hripcsak"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0096443.g008", "stats"=>{"downloads"=>1, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Depicted_above_are_a_glucose_time_series_for_three_different_values_of_a_time_constant_for_plasma_insulin_degradation_via_kidney_and_liver_filtering_b_glucose_time_series_density_for_three_different_values_of_a_time_constant_for_plasma_insulin_degradatio/1058397", "title"=>"Depicted above are: (a) glucose time series for three different values of a time constant for plasma insulin degradation (via kidney and liver filtering), ; (b) glucose time series density for three different values of a time constant for plasma insulin degradation (via kidney and liver filtering), .", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-06-16 03:04:24"}
  • {"files"=>["https://ndownloader.figshare.com/files/1536083"], "description"=>"<p>Depicted above are: (a) the histogram of the raw TDMI of glucose time series for hrs for the population of patients; (b) the mode FBC model of the TDMI distribution; (c) the mode FBC model of the TDMI distribution; (d) the mode FBC model of the TDMI distribution; (e) variation in the distribution (as quantified by the mean and variance) of the log-likelihood for models with – modes.</p>", "links"=>[], "tags"=>["Computational biology", "Population modeling", "physiology", "Endocrine physiology", "Population biology", "Theoretical biology", "Information technology", "data mining", "text mining", "signal processing", "Statistical signal processing", "Endocrinology", "research design", "Clinical research design", "Retrospective studies", "histogram", "tdmi", "glucose", "hrs", "fbc", "quantified", "log-likelihood"], "article_id"=>1058382, "categories"=>["Biological Sciences"], "users"=>["D. J. Albers", "Noémie Elhadad", "E. Tabak", "A. Perotte", "George Hripcsak"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0096443.g002", "stats"=>{"downloads"=>1, "page_views"=>17, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Depicted_above_are_a_the_histogram_of_the_raw_TDMI_of_glucose_time_series_for_hrs_for_the_population_of_patients_b_the_mode_FBC_model_of_the_TDMI_distribution_c_the_mode_FBC_model_of_the_TDMI_distribution_d_the_mode_FBC_model_of_the_TDMI_distribution_e_v/1058382", "title"=>"Depicted above are: (a) the histogram of the raw TDMI of glucose time series for hrs for the population of patients; (b) the mode FBC model of the TDMI distribution; (c) the mode FBC model of the TDMI distribution; (d) the mode FBC model of the TDMI distribution; (e) variation in the distribution (as quantified by the mean and variance) of the log-likelihood for models with – modes.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-06-16 03:04:24"}
  • {"files"=>["https://ndownloader.figshare.com/files/1536093"], "description"=>"<p>Depicted above are: (a) glucose time series for three different values of an exponential constant affecting insulin secretion, ; (b) glucose time series density for three different values of an exponential constant affecting insulin secretion, .</p>", "links"=>[], "tags"=>["Computational biology", "Population modeling", "physiology", "Endocrine physiology", "Population biology", "Theoretical biology", "Information technology", "data mining", "text mining", "signal processing", "Statistical signal processing", "Endocrinology", "research design", "Clinical research design", "Retrospective studies", "glucose", "exponential", "affecting", "insulin"], "article_id"=>1058393, "categories"=>["Biological Sciences"], "users"=>["D. J. Albers", "Noémie Elhadad", "E. Tabak", "A. Perotte", "George Hripcsak"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0096443.g007", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Depicted_above_are_a_glucose_time_series_for_three_different_values_of_an_exponential_constant_affecting_insulin_secretion_b_glucose_time_series_density_for_three_different_values_of_an_exponential_constant_affecting_insulin_secretion_/1058393", "title"=>"Depicted above are: (a) glucose time series for three different values of an exponential constant affecting insulin secretion, ; (b) glucose time series density for three different values of an exponential constant affecting insulin secretion, .", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-06-16 03:04:24"}
  • {"files"=>["https://ndownloader.figshare.com/files/1536092"], "description"=>"<p>Depicted above are: (a) glucose time series for three different values of a linear constant affecting IIGU, ; (b) glucose time series density for three different values of a linear constant affecting IIGU .</p>", "links"=>[], "tags"=>["Computational biology", "Population modeling", "physiology", "Endocrine physiology", "Population biology", "Theoretical biology", "Information technology", "data mining", "text mining", "signal processing", "Statistical signal processing", "Endocrinology", "research design", "Clinical research design", "Retrospective studies", "glucose", "linear", "affecting", "iigu"], "article_id"=>1058392, "categories"=>["Biological Sciences"], "users"=>["D. J. Albers", "Noémie Elhadad", "E. Tabak", "A. Perotte", "George Hripcsak"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0096443.g006", "stats"=>{"downloads"=>0, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Depicted_above_are_a_glucose_time_series_for_three_different_values_of_a_linear_constant_affecting_IIGU_b_glucose_time_series_density_for_three_different_values_of_a_linear_constant_affecting_IIGU_/1058392", "title"=>"Depicted above are: (a) glucose time series for three different values of a linear constant affecting IIGU, ; (b) glucose time series density for three different values of a linear constant affecting IIGU .", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-06-16 03:04:24"}
  • {"files"=>["https://ndownloader.figshare.com/files/1536105"], "description"=>"<p>Full list of parameters for the glucose/insulin model <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0096443#pone.0096443-Sturis1\" target=\"_blank\">[4]</a> used in this paper; note that these are the model parameters we us in this paper.</p>", "links"=>[], "tags"=>["Computational biology", "Population modeling", "physiology", "Endocrine physiology", "Population biology", "Theoretical biology", "Information technology", "data mining", "text mining", "signal processing", "Statistical signal processing", "Endocrinology", "research design", "Clinical research design", "Retrospective studies"], "article_id"=>1058405, "categories"=>["Biological Sciences"], "users"=>["D. J. Albers", "Noémie Elhadad", "E. Tabak", "A. Perotte", "George Hripcsak"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0096443.t001", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Full_list_of_parameters_for_the_glucose_insulin_model_4_used_in_this_paper_note_that_these_are_the_model_parameters_we_us_in_this_paper_/1058405", "title"=>"Full list of parameters for the glucose/insulin model [4] used in this paper; note that these are the model parameters we us in this paper.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-06-16 03:04:24"}
  • {"files"=>["https://ndownloader.figshare.com/files/1536090"], "description"=>"<p>Parameter variation plot versus predictability (TDMI) for selected parameters.</p>", "links"=>[], "tags"=>["Computational biology", "Population modeling", "physiology", "Endocrine physiology", "Population biology", "Theoretical biology", "Information technology", "data mining", "text mining", "signal processing", "Statistical signal processing", "Endocrinology", "research design", "Clinical research design", "Retrospective studies", "predictability"], "article_id"=>1058390, "categories"=>["Biological Sciences"], "users"=>["D. J. Albers", "Noémie Elhadad", "E. Tabak", "A. Perotte", "George Hripcsak"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0096443.g005", "stats"=>{"downloads"=>0, "page_views"=>14, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Parameter_variation_plot_versus_predictability_TDMI_for_selected_parameters_/1058390", "title"=>"Parameter variation plot versus predictability (TDMI) for selected parameters.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-06-16 03:04:24"}
  • {"files"=>["https://ndownloader.figshare.com/files/1536087"], "description"=>"<p>Depicted above are: (a) KDE of the length of individual records; (b) KDE of the number of measurements per individual; (c) KDE of the mean glucose per record.</p>", "links"=>[], "tags"=>["Computational biology", "Population modeling", "physiology", "Endocrine physiology", "Population biology", "Theoretical biology", "Information technology", "data mining", "text mining", "signal processing", "Statistical signal processing", "Endocrinology", "research design", "Clinical research design", "Retrospective studies", "kde", "glucose"], "article_id"=>1058387, "categories"=>["Biological Sciences"], "users"=>["D. J. Albers", "Noémie Elhadad", "E. Tabak", "A. Perotte", "George Hripcsak"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0096443.g004", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Depicted_above_are_a_KDE_of_the_length_of_individual_records_b_KDE_of_the_number_of_measurements_per_individual_c_KDE_of_the_mean_glucose_per_record_/1058387", "title"=>"Depicted above are: (a) KDE of the length of individual records; (b) KDE of the number of measurements per individual; (c) KDE of the mean glucose per record.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-06-16 03:04:24"}
  • {"files"=>["https://ndownloader.figshare.com/files/1536104"], "description"=>"<p>Depicted above are: (a) glucose time series for different values of the constant affecting kidney/liver function, ; (b) glucose time series density for different values of the constant affecting kidney/liver function, .</p>", "links"=>[], "tags"=>["Computational biology", "Population modeling", "physiology", "Endocrine physiology", "Population biology", "Theoretical biology", "Information technology", "data mining", "text mining", "signal processing", "Statistical signal processing", "Endocrinology", "research design", "Clinical research design", "Retrospective studies", "glucose", "affecting"], "article_id"=>1058404, "categories"=>["Biological Sciences"], "users"=>["D. J. Albers", "Noémie Elhadad", "E. Tabak", "A. Perotte", "George Hripcsak"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0096443.g013", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Depicted_above_are_a_glucose_time_series_for_different_values_of_the_constant_affecting_kidney_liver_function_b_glucose_time_series_density_for_different_values_of_the_constant_affecting_kidney_liver_function_/1058404", "title"=>"Depicted above are: (a) glucose time series for different values of the constant affecting kidney/liver function, ; (b) glucose time series density for different values of the constant affecting kidney/liver function, .", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-06-16 03:04:24"}
  • {"files"=>["https://ndownloader.figshare.com/files/1536101"], "description"=>"<p>Note that both undergo at least one bifurcation (qualitative state change) over this variation in parameters.</p>", "links"=>[], "tags"=>["Computational biology", "Population modeling", "physiology", "Endocrine physiology", "Population biology", "Theoretical biology", "Information technology", "data mining", "text mining", "signal processing", "Statistical signal processing", "Endocrinology", "research design", "Clinical research design", "Retrospective studies", "variations", "tdmi", "insulin", "varied", "nominal"], "article_id"=>1058401, "categories"=>["Biological Sciences"], "users"=>["D. J. Albers", "Noémie Elhadad", "E. Tabak", "A. Perotte", "George Hripcsak"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0096443.g011", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Depicted_above_are_the_variations_in_TDMI_for_insulin_secretion_and_kidney_liver_function_when_varied_by_up_to_of_their_nominal_values_/1058401", "title"=>"Depicted above are: the variations in TDMI for insulin secretion, , and kidney/liver function, , when varied by up to of their nominal values.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-06-16 03:04:24"}
  • {"files"=>["https://ndownloader.figshare.com/files/1536099"], "description"=>"<p>Depicted above are: (a) glucose time series for three different values of the delay rate between plasma insulin and glucose production, ; (b) glucose time series density for three different values of the delay rate between plasma insulin and glucose production, .</p>", "links"=>[], "tags"=>["Computational biology", "Population modeling", "physiology", "Endocrine physiology", "Population biology", "Theoretical biology", "Information technology", "data mining", "text mining", "signal processing", "Statistical signal processing", "Endocrinology", "research design", "Clinical research design", "Retrospective studies", "glucose", "plasma", "insulin"], "article_id"=>1058399, "categories"=>["Biological Sciences"], "users"=>["D. J. Albers", "Noémie Elhadad", "E. Tabak", "A. Perotte", "George Hripcsak"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0096443.g009", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Depicted_above_are_a_glucose_time_series_for_three_different_values_of_the_delay_rate_between_plasma_insulin_and_glucose_production_b_glucose_time_series_density_for_three_different_values_of_the_delay_rate_between_plasma_insulin_and_glucose_production_/1058399", "title"=>"Depicted above are: (a) glucose time series for three different values of the delay rate between plasma insulin and glucose production, ; (b) glucose time series density for three different values of the delay rate between plasma insulin and glucose production, .", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-06-16 03:04:24"}

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Relative Metric

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