From 409b090e8e41a4d8048135ce3a557b9d56388f69 Mon Sep 17 00:00:00 2001 From: JieFu Zhu <1404914454@qq.com> Date: Wed, 6 Dec 2023 11:41:03 +0100 Subject: [PATCH] update --- ...A-AI_ Guideline Items - Tabellenblatt1.csv | 94 +++++++++---------- .../crona/decide_my_facourite_color.py | 4 +- .../visualizations/crona/guide_line_heatmap.R | 4 +- wanshi/visualizations/crona/special_needs.py | 23 +++-- 4 files changed, 63 insertions(+), 62 deletions(-) diff --git a/wanshi/visualizations/crona/META-AI_ Guideline Items - Tabellenblatt1.csv b/wanshi/visualizations/crona/META-AI_ Guideline Items - Tabellenblatt1.csv index 22ab6bb..f74a38f 100644 --- a/wanshi/visualizations/crona/META-AI_ Guideline Items - Tabellenblatt1.csv +++ b/wanshi/visualizations/crona/META-AI_ Guideline Items - Tabellenblatt1.csv @@ -1,47 +1,47 @@ -,Guideline Item,Description (specification and/or example content),Item type,AI-specific item?,STARE-HI,TRIPOD,Luo et al.,CONSORT-AI,SPIRIT-AI,CLEAR Derm,DECIDE-AI,,% Item included in high-level consensus guidelines (Y/P),,Good ML Practice,MI-CLAIM,PRIME,DOME,Shen,Hatt et al.,,% Item included in intermediate-level consensus guidelines (Y/P),,Vihinen,CLAIM,PIECES,Zukotynski et al.,Jones et al.,R-AI-DIOLOGY,Volovici et al.,,% Item included in low-level consensus guidelines (Y/P),,% Item included in all guidelines (Y/P) -Year,,,,,2009,2015,2016,2020,2020,2022,2022,,,,2019,2020,2020,2021,2022,2023,,,,2012,2020,2021,2021,2022,2022,2022,,,, -Inclusion Process,,,,,"""+""",%,*,*,*,*,*,,,,"""+""",*,*,*,*,#,,,,*,#,#,*,*,*,#,,,, -Guideline Type,,,,,G,G,G,G,G,S,G,,,,G,G,S,G,S,S,,,,G,S,S,S,S,S,G,,,, -Level of Consensus,,,,,H,H,H,H,H,H,H,,,,M,M,M,M,M,M,,,,L,L,L,L,L,L,L,,,, -,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -Clinical Rationale,Topic,Predictive AI,Content,Yes,N,Y,Y,Y,Y,N,Y,,"0,75",,N,N,P,N,N,N,,"0,17",,N,Y,N,N,Y,N,N,,"0,29",,"0,43" -,Study Design,"Retrospective vs. prospective, prognostic vs. diagnostic",Content,Partially,Y,Y,Y,N,N,N,Y,,"0,63",,N,Y,Y,N,N,Y,,"0,50",,N,Y,N,N,N,N,N,,"0,14",,"0,43" -,Prediction Problem,"Prediction target, outcome parameters, performance metrics",Content,Yes,Y,Y,Y,Y,Y,N,Y,,"0,88",,P,Y,Y,N,N,Y,,"0,67",,N,Y,Y,N,Y,P,N,,"0,57",,"0,71" -,Clinical Setting,Details on the clinical problem and intended use,Content,No,Y,Y,Y,Y,Y,P/N,Y,,"1,00",,Y,Y,N,N,P,Y,,"0,67",,N,Y,Y,N,Y,Y,N,,"0,57",,"0,76" -,Rationale,Relation between prediction problem and clinical goal,Content,Yes,Y,P,Y,Y,Y,P/Y,Y,,"1,00",,Y,Y,N,N,N,Y,,"0,50",,N,Y,P,N,Y,P,N,,"0,57",,"0,71" -,Existing AI and Statistical Models,"Performance metrics, level of translation, clinical application",Content,Yes,P,Y,Y,N,Y,N,P/Y,,"0,75",,N,P,Y,Y,N,N,,"0,50",,Y,N,P,N,P,P,Y,,"0,71",,"0,67" -,State-of-the-art,Identify state-of-the-art clinical solution and use as a baseline for comparison,Quality,Partially,P,P,N,N,N,N,P/Y,,"0,50",,P,Y,N,Y,P,N,,"0,67",,P,N,N,N,N,N,N,,"0,14",,"0,43" -,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -Data,"Data Sources, Types, and Structure","Original data format and volume, facility details, structured vs. unstructured data",Content,Partially,Y,Y,Y,P/N,P/Y,Y,Y,,"1,00",,P,Y,Y,P,P,Y,,"1,00",,N,Y,Y,Y,Y,Y,Y,,"0,86",,"0,95" -,Data Selection,Inclusion and exclusion criteria at the level of data and participants,Content,Partially,Y,Y,Y,Y,Y,N,Y,,"0,88",,P,N,N,N,P,P,,"0,50",,N,Y,Y,N,Y,N,Y,,"0,57",,"0,67" -,Data Preprocessing,"Data transformation, handling of missing data and outliers",Content,Yes,Y,Y,Y,Y,Y,Y,Y,,"1,00",,P,Y,Y,N,P,Y,,"0,83",,N,Y,Y,N,P,Y,Y,,"0,71",,"0,86" -,Labeling of Input Data,"Clinical outcome vs. expert rating, number and expertise of labellers","Content, Quality",Partially,Y,N,N,Y,Y,P/Y,Y,,"0,75",,Y,N,N,N,P,Y,,"0,50",,N,Y,Y,N,Y,N,P,,"0,57",,"0,62" -,Rater Variability,Inter- and intrarater variability,Quality,Partially,Y,N,N,N,N,N,N,,"0,25",,N,N,N,N,N,N,,"0,00",,N,Y,N,N,N,N,N,,"0,14",,"0,14" -,Data Processing Location,"Specification of data processing location (local vs. cloud, external institutions involved in data processing, data flow)",Content,Partially,N,N,N,N,N,N,N,,"0,13",,N,N,N,N,Y,N,,"0,17",,N,N,N,N,N,Y,N,,"0,14",,"0,14" -,De-Identification,Address anonymization/de-identification of data,Quality,Partially,N,N,N,N,N,N,N,,"0,13",,N,N,N,N,Y,N,,"0,17",,N,Y,Y,N,N,Y,N,,"0,43",,"0,24" -,Data Dictionary,Release data dictionary with explanations of variables,Content,Partially,N,N,N,N,N,N,N,,"0,13",,P,N,Y,N,N,N,,"0,33",,N,Y,N,N,N,N,N,,"0,14",,"0,19" -,Data Leakage,"Independence of training/validation/test data (i.e. do not use evaluation sets for feature selection, preprocessing steps or parameter tuning)",Quality,Yes,N,N,N,N,N,N,N,,"0,13",,Y,Y,N,Y,N,Y,,"0,67",,Y,N,Y,N,Y,N,N,,"0,43",,"0,38" -,Representativeness,Training and test data should be representative of real-world clinical settings,Quality,Yes,P,P/N,N,N,N,N,N,,"0,38",,Y,Y,N,Y,P,P,,"0,83",,N,N,N,N,Y,P,Y,,"0,43",,"0,52" -,Basic Statistics of the Dataset,Distribution of input and outcomes,Content,Partially,Y,Y,Y,N,N,Y,P/N,,"0,75",,Y,N,Y,Y,N,N,,"0,50",,N,Y,N,N,Y,N,Y,,"0,43",,"0,57" -,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -Model Training and Validation,Type of Prediction Model,"Type of algorithm, classification vs. regression",Content,Yes,N,Y,Y,Y,Y,N,Y,,"0,75",,N,P,Y,Y,N,N,,"0,50",,N,Y,Y,Y,Y,N,N,,"0,57",,"0,62" -,Model Development,"Identification and removal of redundant independent variables, model training and selection strategy",Content,Yes,P/N,Y,Y,N,P/N,Y,P,,"0,88",,P,Y,Y,Y,P,P,,"1,00",,Y,Y,Y,P,Y,P,P,,"1,00",,"0,95" -,Model Validation,"Internal vs. external vs. cross validation, validation metrics",Content,Yes,Y,Y,Y,N,N,P/Y,P/N,,"0,75",,P,P,Y,Y,N,P,,"0,83",,Y,Y,Y,Y,Y,P,P,,"1,00",,"0,86" -,Model Interpretability,Statement on model interpretability,"Content, Quality",Yes,N,N,Y,N,N,N,N,,"0,25",,Y,N,Y,Y,N,N,,"0,50",,N,Y,N,N,Y,N,Y,,"0,43",,"0,38" -,Model Performance and Interpretation,"Outcome metrics, confidence intervals",Content,Yes,Y,Y,Y,P/N,P/Y,Y,Y,,"1,00",,Y,P,P,Y,P,N,,"0,83",,Y,Y,Y,N,Y,P,P,,"0,86",,"0,90" -,Computational Cost,"Model execution time, floating point operations per second",Content,Yes,N,N,N,N,N,N,N,,"0,13",,N,N,N,Y,N,N,,"0,17",,N,N,N,Y,N,N,N,,"0,14",,"0,14" -,Statistical Methods,Appropriate methods and significance levels for performance comparison of baseline and proposed model,Quality,Partially,Y,Y,N,N,N,N,N,,"0,38",,Y,Y,N,Y,N,N,,"0,50",,P,Y,P,N,Y,N,Y,,"0,71",,"0,52" -,Performance Errors,Identification and analysis of errors,"Content, Quality",Yes,Y,Y,N,Y,Y,P/Y,Y,,"0,88",,Y,N,P,N,P,N,,"0,50",,P,Y,N,N,N,N,P,,"0,43",,"0,62" -,Over-/Underfitting,Assessment of the possibility of over-/underfitting (i.e. by reporting indicators such as train vs. test error),Quality,Yes,N,N,N,N,N,N,N,,"0,13",,Y,N,N,Y,N,N,,"0,33",,P,N,N,N,N,N,N,,"0,14",,"0,19" -,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -Critical Appraisal,Clinical Implications and Practical Value,"Potential augmentations of clinical workflows, potential changes in clinical decision making",Content,Partially,Y,Y,Y,Y,Y,Y,Y,,"1,00",,Y,N,N,N,Y,N,,"0,33",,N,Y,N,N,N,Y,N,,"0,29",,"0,57" -,Translation,Details on integration into clinical workflow,Content,Partially,N,Y,N,N,N,N,Y,,"0,38",,Y,N,N,N,P,N,,"0,33",,N,Y,N,N,Y,Y,N,,"0,43",,"0,38" -,Limitations,"Bias, generalizability, interpretation pitfalls",Content,Partially,Y,Y,Y,P/Y,P/N,P/Y,Y,,"1,00",,Y,N,Y,N,P,N,,"0,50",,N,Y,N,N,P,N,Y,,"0,43",,"0,67" -,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -Ethics and Reproducibility,Data Publication,Publication of datasets or inclusion of a statement on public availability,Content,Partially,N,Y,N,N,N,N,Y,,"0,38",,N,Y,N,Y,N,N,,"0,33",,Y,N,N,N,Y,N,Y,,"0,43",,"0,38" -,Code Publication,Publication of code or inclusion of a statement on public availability,Content,Yes,N,Y,N,Y,Y,N,Y,,"0,63",,N,Y,Y,Y,N,N,,"0,50",,N,N,N,P,N,N,Y,,"0,29",,"0,48" -,AI Intervention Publication,Publication of AI Intervention or inclusion of a statement on public availability,Content,Yes,P/N,N,N,Y,Y,N,N,,"0,50",,N,Y,N,Y,N,N,,"0,33",,N,N,N,N,N,N,N,,"0,00",,"0,29" -,Future Updates,Details on future software/algorithm updates (i.e. how users will be informed),Content,Partially,P/N,P/N,N,N,N,N,P/N,,"0,50",,P,N,N,N,P,N,,"0,33",,N,N,P,N,Y,Y,N,,"0,43",,"0,43" -,Ethical Statement,Details on IRB approval and informed consent procedure,Content,No,Y,Y,Y,N,N,N,Y,,"0,63",,N,N,N,N,Y,N,,"0,17",,N,N,N,N,N,N,N,,"0,00",,"0,29" -,Equity and Access,"Statement on equity, diversity and access to AI application","Content, Quality",Yes,N,N,N,N,N,N,P,,"0,25",,P,N,N,N,Y,N,,"0,33",,N,N,N,N,N,N,N,,"0,00",,"0,19" -,Legal and regulatory Aspects,Statement on legal and regulatory aspects,Content,Partially,N,N,N,N,N,N,N,,"0,13",,N,N,N,N,Y,N,,"0,17",,N,N,N,N,Y,Y,N,,"0,29",,"0,19" \ No newline at end of file +,Guideline Item,Description (specification and/or example content),Item type,AI-specific item?,STARE-HI,TRIPOD,Luo et al.,CONSORT-AI,SPIRIT-AI,Schwendicke et al.,CLEAR Derm,DECIDE-AI,CLEAR,,% Item included in high-level consensus guidelines (Y/P),,Good ML Practice,MI-CLAIM,PRIME,DOME,Shen,Hatt et al.,,% Item included in intermediate-level consensus guidelines (Y/P),,Vihinen,CLAIM,MINIMAR,Stevens et al.,CAIR,PIECES,Zukotynski et al.,El Naqa et al.,Jones et al.,R-AI-DIOLOGY,Volovici et al.,,% Item included in low-level consensus guidelines (Y/P),,% Item included in all guidelines (Y/P),,,General,,Specific +Year,,,,,2009,2015,2016,2020,2020,2021,2022,2022,2023,,,,2019,2020,2020,2021,2022,2023,,,,2012,2020,2020,2020,2021,2021,2021,2021,2022,2022,2022,,,,,,,,, +Inclusion Process,,,,,"""+""",%,*,*,*,*,*,*,*,,,,"""+""",*,*,*,*,#,,,,*,#,*,*,*,#,*,#,*,*,#,,,,,,,,, +Guideline Type,,,,,G,G,G,G,G,S,S,G,S,,,,G,G,S,G,S,S,,,,G,S,G,G,G,S,S,S,S,S,G,,,,,,,,, +Level of Consensus,,,,,H,H,H,H,H,H,H,H,H,,,,M,M,M,M,M,M,,,,L,L,L,L,L,L,L,L,L,L,L,,,,,,,,, +,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Clinical Rationale,Topic,Predictive AI,Content,Yes,N,Y,Y,Y,Y,Y,N,Y,Y,,"0,78",,N,N,P,N,N,N,,"0,17",,N,Y,N,N,Y,N,N,N,Y,N,N,,"0,27",,"0,42",,,"0,43",,"0,50" +,Study Design,"Retrospective vs. prospective, prognostic vs. diagnostic",Content,Partially,Y,Y,Y,N,N,Y,N,Y,Y,,"0,67",,N,Y,Y,N,N,Y,,"0,50",,N,Y,N,N,N,N,N,N,N,N,N,,"0,09",,"0,38",,,"0,36",,"0,50" +,Prediction Problem,"Prediction target, outcome parameters, performance metrics",Content,Yes,Y,Y,Y,Y,Y,Y,N,Y,Y,,"0,89",,P,Y,Y,N,N,Y,,"0,67",,N,Y,P,P,P,Y,N,N,Y,P,N,,"0,64",,"0,73",,,"0,79",,"0,75" +,Clinical Setting,Details on the clinical problem and intended use,Content,No,Y,Y,Y,Y,Y,Y,Y,Y,Y,,"1,00",,Y,Y,N,N,P,Y,,"0,67",,N,Y,P,P,Y,Y,N,P,Y,Y,N,,"0,73",,"0,81",,,"0,79",,"0,83" +,Rationale,Relation between prediction problem and clinical goal,Content,Yes,Y,P,Y,Y,Y,P,P,Y,P,,"1,00",,Y,Y,N,N,N,Y,,"0,50",,N,Y,N,P,P,P,N,P,Y,P,N,,"0,64",,"0,73",,,"0,71",,"0,75" +,Existing AI and Statistical Models,"Performance metrics, level of translation, clinical application",Content,Yes,P,Y,Y,N,Y,Y,N,P,Y,,"0,78",,N,P,Y,Y,N,N,,"0,50",,Y,N,N,N,N,P,N,P,P,P,Y,,"0,55",,"0,62",,,"0,64",,"0,58" +,State-of-the-art,Identify state-of-the-art clinical solution and use as a baseline for comparison,Quality,Partially,P,P,N,N,N,P,N,N,P,,"0,44",,P,Y,N,Y,P,N,,"0,67",,P,N,N,N,N,N,N,N,N,N,N,,"0,09",,"0,35",,,"0,43",,"0,33" +,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Data,"Data Sources, Types, and Structure","Original data format and volume, facility details, structured vs. unstructured data",Content,Partially,Y,Y,Y,P,P,Y,Y,Y,Y,,"1,00",,P,Y,Y,P,P,Y,,"1,00",,N,Y,Y,Y,Y,Y,Y,Y,Y,Y,Y,,"0,91",,"0,96",,,"0,93",,"1,00" +,Data Selection,Inclusion and exclusion criteria at the level of data and participants,Content,Partially,Y,Y,Y,Y,Y,Y,N,Y,Y,,"0,89",,P,N,N,N,P,P,,"0,50",,N,Y,Y,N,Y,Y,N,N,Y,N,Y,,"0,55",,"0,65",,,"0,71",,"0,67" +,Data Preprocessing,"Data transformation, handling of missing data and outliers",Content,Yes,Y,Y,Y,Y,Y,Y,Y,Y,Y,,"1,00",,P,Y,Y,N,P,Y,,"0,83",,N,Y,N,Y,Y,Y,N,Y,P,Y,Y,,"0,73",,"0,85",,,"0,79",,"0,92" +,Labeling of Input Data,"Clinical outcome vs. expert rating, number and expertise of labellers","Content, Quality",Partially,Y,N,N,Y,Y,Y,P,Y,Y,,"0,78",,Y,N,N,N,P,Y,,"0,50",,N,Y,P,P,P,Y,N,Y,Y,N,P,,"0,73",,"0,69",,,"0,64",,"0,75" +,Rater Variability,Inter- and intrarater variability,Quality,Partially,Y,N,N,N,N,Y,N,N,Y,,"0,33",,N,N,N,N,N,N,,"0,00",,N,Y,N,N,Y,N,N,P,N,N,N,,"0,27",,"0,23",,,"0,14",,"0,33" +,Data Processing Location,"Specification of data processing location (local vs. cloud, external institutions involved in data processing, data flow)",Content,Partially,N,N,N,N,N,Y,N,N,N,,"0,11",,N,N,N,N,Y,N,,"0,17",,N,N,N,N,N,N,N,N,N,Y,N,,"0,09",,"0,12",,,"0,00",,"0,33" +,De-Identification,Address anonymization/de-identification of data,Quality,Partially,N,N,N,N,N,Y,N,N,Y,,"0,22",,N,N,N,N,Y,N,,"0,17",,N,Y,N,N,N,Y,N,N,N,Y,N,,"0,27",,"0,23",,,"0,00",,"0,58" +,Data Dictionary,Release data dictionary with explanations of variables,Content,Partially,N,N,N,N,N,N,N,N,N,,"0,00",,P,N,Y,N,N,N,,"0,33",,N,Y,N,N,N,N,N,N,N,N,N,,"0,09",,"0,12",,,"0,07",,"0,25" +,Data Leakage,"Independence of training/validation/test data (i.e. do not use evaluation sets for feature selection, preprocessing steps or parameter tuning)",Quality,Yes,N,N,N,N,N,N,N,N,P,,"0,11",,Y,Y,N,Y,N,Y,,"0,67",,Y,N,N,P,N,Y,N,P,Y,N,N,,"0,45",,"0,38",,,"0,36",,"0,42" +,Representativeness,Training and test data should be representative of real-world clinical settings,Quality,Yes,P,P,N,N,N,Y,N,N,N,,"0,33",,Y,Y,N,Y,P,P,,"0,83",,N,N,N,N,N,N,N,Y,Y,P,Y,,"0,36",,"0,46",,,"0,43",,"0,50" +,Basic Statistics of the Dataset,Distribution of input and outcomes,Content,Partially,Y,Y,Y,N,N,N,Y,N,N,,"0,44",,Y,N,Y,Y,N,N,,"0,50",,N,Y,Y,N,N,N,N,Y,Y,N,Y,,"0,45",,"0,46",,,"0,50",,"0,42" +,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Model Training and Validation,Type of Prediction Model,"Type of algorithm, classification vs. regression",Content,Yes,N,Y,Y,Y,Y,Y,N,Y,Y,,"0,78",,N,P,Y,Y,N,N,,"0,50",,N,Y,Y,N,Y,Y,Y,Y,Y,N,N,,"0,64",,"0,65",,,"0,64",,"0,67" +,Model Development,"Identification and removal of redundant independent variables, model training and selection strategy",Content,Yes,N,Y,Y,N,P,Y,Y,P,Y,,"0,78",,P,Y,Y,Y,P,P,,"1,00",,Y,Y,Y,Y,Y,Y,P,Y,Y,P,P,,"1,00",,"0,92",,,"0,86",,"1,00" +,Model Validation,"Internal vs. external vs. cross validation, validation metrics",Content,Yes,Y,Y,Y,N,N,Y,P,P,Y,,"0,78",,P,P,Y,Y,N,P,,"0,83",,Y,Y,P,Y,Y,Y,Y,Y,Y,P,P,,"1,00",,"0,88",,,"0,86",,"0,92" +,Model Interpretability,Statement on model interpretability,"Content, Quality",Yes,N,N,Y,N,N,Y,N,N,Y,,"0,33",,Y,N,Y,Y,N,N,,"0,50",,N,Y,N,P,N,N,N,P,Y,N,Y,,"0,45",,"0,42",,,"0,36",,"0,50" +,Model Performance and Interpretation,"Outcome metrics, confidence intervals",Content,Yes,Y,Y,Y,P,P,P,Y,Y,Y,,"1,00",,Y,P,P,Y,P,N,,"0,83",,Y,Y,Y,Y,P,Y,N,Y,Y,P,P,,"0,91",,"0,92",,,"1,00",,"0,83" +,Computational Cost,"Model execution time, floating point operations per second",Content,Yes,N,N,N,N,N,N,N,N,N,,"0,00",,N,N,N,Y,N,N,,"0,17",,N,N,N,N,N,N,Y,P,N,N,N,,"0,18",,"0,12",,,"0,07",,"0,17" +,Statistical Methods,Appropriate methods and significance levels for performance comparison of baseline and proposed model,Quality,Partially,Y,Y,N,N,N,Y,N,N,Y,,"0,44",,Y,Y,N,Y,N,N,,"0,50",,P,Y,N,N,N,P,N,Y,Y,N,Y,,"0,55",,"0,50",,,"0,50",,"0,50" +,Performance Errors,Identification and analysis of errors,"Content, Quality",Yes,Y,Y,N,Y,Y,N,P,Y,P,,"0,78",,Y,N,P,N,P,N,,"0,50",,P,Y,N,N,Y,N,N,N,N,N,P,,"0,36",,"0,54",,,"0,64",,"0,50" +,Over-/Underfitting,Assessment of the possibility of over-/underfitting (i.e. by reporting indicators such as train vs. test error),Quality,Yes,N,N,N,N,N,N,N,N,N,,"0,00",,Y,N,N,Y,N,N,,"0,33",,P,N,N,P,N,N,N,Y,N,N,N,,"0,27",,"0,19",,,"0,29",,"0,08" +,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Critical Appraisal,Clinical Implications and Practical Value,"Potential augmentations of clinical workflows, potential changes in clinical decision making",Content,Partially,Y,Y,Y,Y,Y,Y,Y,Y,Y,,"1,00",,Y,N,N,N,Y,N,,"0,33",,N,Y,N,N,N,N,N,Y,N,Y,N,,"0,27",,"0,54",,,"0,50",,"0,58" +,Translation,Details on integration into clinical workflow,Content,Partially,N,Y,N,N,N,N,N,Y,N,,"0,22",,Y,N,N,N,P,N,,"0,33",,N,Y,N,N,N,N,N,N,Y,Y,N,,"0,27",,"0,27",,,"0,21",,"0,42" +,Limitations,"Bias, generalizability, interpretation pitfalls",Content,Partially,Y,Y,Y,N,N,P,Y,Y,Y,,"0,78",,Y,N,Y,N,P,N,,"0,50",,N,Y,N,N,N,N,N,Y,P,N,Y,,"0,36",,"0,54",,,"0,43",,"0,67" +,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Ethics and Reproducibility,Data Publication,Publication of datasets or inclusion of a statement on public availability,Content,Partially,N,Y,N,N,N,Y,N,Y,Y,,"0,44",,N,Y,N,Y,N,N,,"0,33",,Y,N,Y,Y,Y,N,N,N,Y,N,Y,,"0,55",,"0,46",,,"0,64",,"0,33" +,Code Publication,Publication of code or inclusion of a statement on public availability,Content,Yes,N,Y,N,Y,Y,Y,N,Y,Y,,"0,67",,N,Y,Y,Y,N,N,,"0,50",,N,N,Y,Y,Y,N,P,Y,N,N,Y,,"0,55",,"0,58",,,"0,71",,"0,42" +,AI Intervention Publication,Publication of AI Intervention or inclusion of a statement on public availability,Content,Yes,P,N,N,Y,Y,N,N,N,Y,,"0,44",,N,Y,N,Y,N,N,,"0,33",,N,N,N,N,N,N,N,N,N,N,N,,"0,00",,"0,23",,,"0,36",,"0,17" +,Future Updates,Details on future software/algorithm updates (i.e. how users will be informed),Content,Partially,N,N,N,N,N,N,N,N,N,,"0,00",,P,N,N,N,P,N,,"0,33",,N,N,N,N,N,P,N,N,Y,Y,N,,"0,27",,"0,19",,,"0,07",,"0,42" +,Ethical Statement,Details on IRB approval and informed consent procedure,Content,No,Y,Y,Y,N,N,Y,N,Y,Y,,"0,67",,N,N,N,N,Y,N,,"0,17",,N,N,N,N,Y,N,N,Y,N,N,N,,"0,18",,"0,35",,,"0,36",,"0,33" +,Equity and Access,"Statement on equity, diversity and access to AI application","Content, Quality",Yes,N,N,N,N,N,P,N,P,P,,"0,33",,P,N,N,N,Y,N,,"0,33",,N,N,N,N,N,N,N,N,N,N,N,,"0,00",,"0,19",,,"0,14",,"0,33" +,Legal and Regulatory Aspects,Statement on legal and regulatory aspects,Content,Partially,N,N,N,N,N,Y,N,N,N,,"0,11",,N,N,N,N,Y,N,,"0,17",,N,N,N,N,N,N,N,N,Y,Y,N,,"0,18",,"0,15",,,"0,00",,"0,42" \ No newline at end of file diff --git a/wanshi/visualizations/crona/decide_my_facourite_color.py b/wanshi/visualizations/crona/decide_my_facourite_color.py index c750ee6..7067ff0 100644 --- a/wanshi/visualizations/crona/decide_my_facourite_color.py +++ b/wanshi/visualizations/crona/decide_my_facourite_color.py @@ -19,8 +19,8 @@ font_size = 9 -alphaY = 0.1 -alphaP = 0.03 +alphaY = 0.08 +alphaP = 0.02 if __name__ == '__main__': diff --git a/wanshi/visualizations/crona/guide_line_heatmap.R b/wanshi/visualizations/crona/guide_line_heatmap.R index bdb123b..ebf7c02 100644 --- a/wanshi/visualizations/crona/guide_line_heatmap.R +++ b/wanshi/visualizations/crona/guide_line_heatmap.R @@ -2,8 +2,10 @@ library(ComplexHeatmap) # Load circlize package library(circlize) +par(family = "Arial") + # Read the data -data <- read.csv("/home/jeff/PycharmProjects/wanshi-utils/wanshi/visualizations/crona/finial2.csv", sep = ",") +data <- read.csv("E:\\PycharmProjects\\wanshi-utils\\wanshi\\visualizations\\crona\\finial2.csv", sep = ",") # Change the column names to make them easier to work with colnames(data) <- c("Groups", "GuidelineItem", "Comprehensive", "Collaborative", "Expert-Led", "Overall","General","Specific") diff --git a/wanshi/visualizations/crona/special_needs.py b/wanshi/visualizations/crona/special_needs.py index 0876db7..f4511fa 100644 --- a/wanshi/visualizations/crona/special_needs.py +++ b/wanshi/visualizations/crona/special_needs.py @@ -6,9 +6,9 @@ import met_brewer from decide_my_facourite_color import * # Set the font globally -mpl.rcParams['font.family'] = 'Sans-serif' # or 'Helvetica' if available +mpl.rcParams['font.family'] = 'Arial' # or 'Helvetica' if available -csv_path = '/home/jeff/Downloads/META-AI_ Guideline Items - Tabellenblatt_updated.csv' +csv_path = 'META-AI_ Guideline Items - Tabellenblatt1.csv' core_data = 'core data.csv' import csv @@ -73,8 +73,8 @@ label_margin=2, label_orientation="vertical", label_size=font_size, - text_kws={'fontname':'Sans-serif'}, - line_kws={'fontname':'Sans-serif'}, + text_kws={'fontname':'Arial'}, + line_kws={'fontname':'Arial'}, ) track1.xticks_by_interval(1, show_label=False) @@ -178,10 +178,10 @@ # get row and column number of the dataframe when the value is Y tuple_listY = [] tuple_listP = [] + for cri in range(len(all_critirian_names)): #print(cri) for item in range(len(item_names)): - print(item_names) if df_core_data.iloc[item, cri+4] == "Y": #print(item, cri) tuple_listY.append((item, cri)) @@ -189,17 +189,16 @@ #print(item, cri) tuple_listP.append((item, cri)) -for i in range(len(tuple_listY)): - #print(("Guideline Item", tuple_listY[i][0], tuple_listY[i][0]+1), ("Critirian", tuple_listY[i][1], tuple_listY[i][1]+1)) - circos.link(("Guideline Item", tuple_listY[i][0]+gap, tuple_listY[i][0]+1-gap), ("Critirian", tuple_listY[i][1]+gap, tuple_listY[i][1]+1-gap),alpha=alphaY, color=color_link, r1=90, r2=70) +for i in (range(len(tuple_listY))): + circos.link(("Guideline Item", 36-tuple_listY[i][0]+gap, 36-tuple_listY[i][0]+1-gap), ("Critirian", tuple_listY[i][1]+gap, tuple_listY[i][1]+1-gap),alpha=alphaY, color=color_link, r1=90, r2=70) for i in range(len(tuple_listP)): - circos.link(("Guideline Item", tuple_listP[i][0]+gap, tuple_listP[i][0]+1-gap), ("Critirian", tuple_listP[i][1]+gap, tuple_listP[i][1]+1-gap),alpha=alphaP, color=color_link, r1=90, r2=70) + circos.link(("Guideline Item", 36-tuple_listP[i][0]+gap, 36-tuple_listP[i][0]+1-gap), ("Critirian", tuple_listP[i][1]+gap, tuple_listP[i][1]+1-gap),alpha=alphaP, color=color_link, r1=90, r2=70) text_common_kws = {'ha':"left", 'va':"center", 'size':8} -circos.text(" Consensus Process", r=95, color="black", **text_common_kws, weight="bold") -circos.text(" Guideline Type", r=85, color="black", **text_common_kws, weight="bold") -circos.text(" Year of Publication", r=75, color="black", **text_common_kws, weight="bold") +circos.text(" Consensus Process", r=95, color="black", **text_common_kws, weight="bold", **{'fontname':'Arial'}) +circos.text(" Guideline Type", r=85, color="black", **text_common_kws, weight="bold", **{'fontname':'Arial'}) +circos.text(" Year of Publication", r=75, color="black", **text_common_kws, weight="bold", **{'fontname':'Arial'}) # circos.text(" META AI ", r=185, color="black", **{'ha':"center", 'va':"center", 'size':18})