This a repository collecting shiny apps I have been generating

a) VC_explorer v1 is completed. Synthea generated patients/control cohort with breast cancer data. VC_explorer v1 App

b) VCExplorer_CT is an advanced version of IME_Score2. The idea is to display some parameters from a virtual observational cohort especially tailored towards fitness and prevention. VCExplorer_CT App

c) IME_Score2 is very similar to VCExplorer_CT. There are no wearables data involved. IME_Score2 App

d) Colorectal_100 is yet-another virtual cohort based on Synthea. Basic visulization are same as in the above examples. Colorectal

e) HRV_V2 is different. It tries to displays RR_intervals and BPM from cardio measurements coming from wearables (usually 24hrs). From these measurements a lot of derived parameters are produced. See here for details. A PCA for the cohort is provided and finally a set of gauge indicators will try to summarize the stress level of probands. HRV_V2

f) This Shiny Hospitalization app has to do with the choice of best parameter to obtain the more realistic hospitalization time for a cohort in order to obtain a target Kaplan-Meier plot.

g) Public COVID-19 reference data have been used for the following Shiny for COVID-19 Hospitalization. Here a comparison through the Kaplan-Meier plots is provided so that the parameters chosen for the virtual cohort can be checked against the real-world data.

h) Starting from data reported in a glioblastoma trial, Takoua Korchani and me developed a Virtual Cohort which generates n patients with either Grad_I or Grad_IV glioblastoma and which can be visualized here

i) After cleaning and merging Vindex proprietary data, a shiny visualizer has been developed to check training probands versus control group: VIndex_v2

To run the shiny apps, you can either run on mybinder.org or build locally with repo2docker.

If you decide for direct run clicking links under mybinder, it might take few minutes to build the underlining virtual machine, so be patient and let it run.

To build locally:

• Install Docker if required
• Create a virtual environment and install repo2docker from PyPI.
• Clone this repository
• Run repo2docker
• Depending on the permissions, you might have to run the command as an admin and give --user-id xxxx and --user-name name

pip install jupyter-repo2docker
git clone https://github.com/agiani99/R_shinyapps.git
cd R_shinyapps
repo2docker .