Tuesday, November 13 from 6 p.m. - 9:30 p.m.
Assembly Row 1W22
399 Revolution Drive, Somerville, MA 02145
R is a free and open programming language for statistical computing, data analysis, and graphical visualization. Along with this powerful software, comes a dynamic and vast community. The Partners R User Group seeks to bring this community together to share ideas, discuss R related topics, and provide direction for new and experienced users.
This time, the theme of R user group meeting is "Machine Learning"
We are pleased to have 6 "Show-and-Tell" speakers
- Niranjan Bhosarekar (Business Intelligence Knowledge Engineer at Neighborhood Health Plan)
- Introduction to Machine Learning & Evaluating Models
- Using R on IDEA Platform, I will go through the very basics of building and evaluating predictive models. I will cover some of the ways we are using R at Neighborhood Health Plan. Based on our own experience, I will also touch upon how IDEA platform gives us the flexibility to switch between Big Data tools and programming languages
- Melissa Zhao (Research Scientist, Harvard Public Health)
- Machine Learning with Caret package
- A practical introduction to machine learning in R, using the popular Caret package. We will cover a general overview of the principles behine different machine learning methods, and go through some baskc steps of commonly used methods of machine learning, such as random forest and support vector machines.
- Amanda Zheutlin (Research Fellow at Psychiatry - MGH)
- Machine Learning in Collaborations: Psychiatric Risk Stratification across Healthcare Systems
- We have been collaborating with other US healthcare systems to build risk stratification algorithms for psychiatric disorders using easily accessible, structured EHR data common across sites. I will share an overview of our pipeline – from data extraction to model testing – for building and sharing models across sites and some of our preliminary results.
- Mark Ommerborn (Research manager at Center for Community Health and Health Equity - BWH)
- Machine Learning Applied to Understanding Health Disparities in the Behavioral Risk Factor Surveillance Survey (BRFSS)
- I will present our methods of using machine learning techniques to predict wellness in a large public health dataset from the CDC, the BRFSS. I will give an introduction to using the caret package in R to compare different machine learning algorithms to build predictive models of self-rated health in diverse groups.
- Maciej Pacula (Team Lead, Bioinformatics & Data Science at Pathology - MGH )
- Machine Learning in the Clinic: Lessons from MGH Pathology
- Despite the extraordinary success of Machine Learning in medical research, successful deployments of ML in the clinical setting remain elusive. We present our approach to Machine Learning in MGH Pathology and showcase some of the predictive models we have successfully integrated into day-to-day clinical workflows, with a focus on cancer genomics.
- Joseph Chou (PHYSICIAN, Pediatric Medical Services - MGH)
- Machine learning analysis of maternal pregnancy clinical notes to predict newborns at risk for neonatal abstinence syndrome
Natural language processing was applied to unstructured free text clinical notes to generate a predictive model identifying newborns at risk for opioid withdrawal. The predictive model had good performance, without requiring subject matter expertise or manual data abstraction for training, and selected sparse and interpretable features. This approach has promise to both improve patient care and facilitate clinical research.
About this meeting-
- This is an informal event for employees who work within the Partners network.
- Pizza and beverages will be provided.