update

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"
+,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"

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__':

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")