Machine Learning in Cardiology: Insights from a Bibliometric Study

In this repository you can find (file - description): 1 number of publication by geography.png a barchart where the number of documents (x-axis) over the period 2019-2025 is shown for the best geographic regions (y-axis) 2 number of publication by top venues.png a barchart where the number of published documents (x-axis) is shown over the 2019-2025 period for the top 15 venues (y-axis) that published the most in the identified research landscape. 3 medical keyword distribution over the clusters.png for each cluster identified in the study (groups of the x-axis), the quantity of keywords in % (y-axis) is show for each medical categories (diseases, diagnosis, treatment, outcome, the bars of the x-axis). 4 heatmap diseases x ML models.png for each diseases (x-axis) the heatmap shows the strenght of association (e.g. the number of time it is linked) with corresponding ML models (y-axis) 5 heatmap diseases categories x Care Pathway.png for each diseases categories (x-axis) the heatmap shows the number of time it is linked with a ML models concerning the Care Pathway Activity (y-axis) 6 heatmap diseases x Care Pathway.png for each specific diseases (y-axis) the heatmap shows the number of time it is linked with a ML models concerning the Care Pathway Activity (x-axis) 7 diseases x ML.png for each specific diseases (y-axis) it is shown the number of time it is associated with a ML model (the intensity of study under the ML lens)