Appsilon Talks at the 2021 R/Medicine Conference: Focus on Automation

The 2021 R/Medicine Conference is already upon us! This week in August (24-27) will see a virtual gathering of the R community with a keen eye on medical data science. Damian Rodziewicz, President and Co-Founder of Appsilon, and Oriol Senan, R Shiny Developer and computational biology expert, will represent Appsilon at the conference with talks focused on automation.

R, Shiny, and automation are an inseparable trio in data science. Especially in the context of highly regulated environments, where the need for speed and precision reaches a new level of importance. Both talks aim to address this need by looking at different ways of using automation in clinical trial reporting and medical data analysis.

Curious to learn what kind of R and Shiny insights you can expect from Damian and Oriol? Read on to take a sneak-peek of presentation abstracts and learn more about the speakers.

• Damian Rodziewicz on automation in clinical trial reporting
• Clinical trial reporting made easier with R Markdown and {officedown} package
• About Damian Rodziewicz
• Oriol Senan on Shiny and pipeline automation in medical data analysis
• Fast and controlled: Improve your data analysis workflow
• About Oriol Senan
• Register for R/Medicine 2021!
• Leverage Data Science in Health Care with Appsilon

Damian Rodziewicz on automation in clinical trial reporting

R/Medicine | Friday | 27 August | 4:50 PM EDT

Clinical trial reporting made easier with R Markdown and {officedown} package

Damian’s presentation is a rare treat for medical data analysts, clinical researchers, clinical trial project managers, and all data professionals dealing with high-volume reporting and strict quality assurance procedures on a daily basis. He’ll introduce R Markdown and the {officedown} package as an automation solution fit for regulated environments: empowering clinicians and researchers to turn data into valuable insights with speed and accuracy.

R Markdown combines a number of R packages and external tools to form a complete ecosystem for authoring documents. Within R Markdown, R code can be used to export results of computations and analyses directly into Word files, PDFs, slideshows, and dozens of other output formats. When enhanced with the {officedown} package, it gives users additional tools and formatting options, especially for Microsoft Word and PowerPoint documents. Once integrated with RStudio Connect, the reporting framework can be easily deployed in regulated environments. More automation options are then available to help generate periodical reports and send results via email.

Damian will give you a step-by-step guide to R Markdown and {officedown}. You’ll learn how to implement an automated workflow to turn data into shareable documents that meet formal and quality assurance requirements. An overview of available functionalities will help you get up to speed with the custom setup to best fit your reporting needs. Topped off with use cases and hands-on examples that showcase R Markdown and {officedown} capabilities, Damian’s presentation is a perfect introduction to automation in clinical research and clinical trial reporting.

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About Damian Rodziewicz

President and one of the four founders of Appsilon. With a Master’s Degree in Computer Science and a Postgraduate Degree in Managerial Law, Damian kick-started his professional career working at Accenture, UBS, Microsoft, and Domino Data Lab. Founding Appsilon opened a new chapter dedicated to R and Shiny. Seeing how many companies struggle with UX/UI, performance, and scalability issues of their Shiny apps and data dashboards, Appsilon made it their mission to support data science teams with expert R/Shiny consulting services. Damian is deeply involved in keeping Appsilon on course to deliver optimal business value to both corporate and institutional customers while addressing as many Data for Good issues as possible.

Oriol Senan on Shiny and pipeline automation in medical data analysis

R/Medicine | Wednesday | 25 August | 2:10 PM EDT

Fast and controlled: Improve your data analysis workflow

Reproducible data analysis is a hot topic in medical data science circles. This poster presentation has an eye-opening potential for all researchers and data analysts considering introducing Shiny in their Medtech stack. If you and your data science team want to know how to improve data analysis workflow using Shiny and pipeline automation, Oriol Senan might just have the answer.

Looking at the data analysis software landscape, the existing solutions vary most in terms of ease of use and the time needed to produce a plot, run a model, or perform an analysis. Data professionals looking to upgrade their analysis from regular to outstanding are constantly searching for ways to improve quality and ensure reproducible results. With this in mind, frameworks optimized for quality, reproducibility, and the interpretation of results can make all the difference.

Pipeline toolkits are a great starting point to gain more control over data analysis workflows, especially in terms of automation and data management. The {targets} R package is a good example: helping automate processes, save results and re-run analyses if the outcome is subject to change.

The next crucial element is the interpretation of results. At this stage, what matters most is how easily you can compare models, remove or add features, as well as analyze trends, charts, or tables. Shiny was originally conceived as a tool for data visualization and sharing data analysis results. It is this unique property of Shiny dashboards that makes them so useful for data analysts. Combined with pipeline automation, Shiny dashboards can help interpret data across different dimensions and communicate results using visual cues adapted to the specific project needs.

As part of his poster presentation, Oriol will take a closer look at pipeline automation and Shiny dashboards both as individual solutions and as a powerful combination. He will focus on the ease of use, explore different real-life scenarios, and offer recommendations for using both or just one of them to achieve the intended results.

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About Oriol Senan

R/Shiny developer at Appsilon with an academic background in research and computational biology. Oriol is an enthusiast of Shiny dashboards, driven by the challenge to combine different technologies and disciplines in the common pursuit of delivering actionable insights from data. He is very active in the open source community, contributing to the development of R Shiny and R Bioconductor packages. Oriol’s approach to data science is exceptionally open-minded. He sees scientific progress as an ongoing collaboration rooted in creative synergy and fuelled by a diversity of perspectives and experiences.

Register for R/Medicine 2021!

Registrations for the R/Medicine conference are still open: you can get your virtual pass on the R/Medicine website. The conference package comes with recordings of presentations and workshops, so don’t worry about missing out on tips and tricks for using R in your data science project.

Follow R/Medicine on Twitter to receive the latest updates straight to your newsfeed and meet other members of the R community.

Leverage Data Science in Health Care with Appsilon

Let’s connect to find out how data science can help you and your patients. From modeling and data handling to creative reporting and visualizations, the data science experts at Appsilon provide world-class solutions for the healthcare industry. 

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Appsilon provides innovative data analytics, machine learning, and managed services solutions for Fortune 500 companies, NGOs, and non-profit organizations. We deliver the world’s most advanced R Shiny applications, with a unique ability to rapidly develop and scale enterprise Shiny dashboards. Our proprietary machine learning frameworks allow us to deliver Computer Vision, NLP, and fraud detection prototypes in as little as one week.

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