Post 1: The John Hopkins — ML and Healthcare

(cut copy pasted from JHU website)

At Johns Hopkins, machine learning researchers are partnering with health care professionals to tackle these high impact problems, such as:

  • Are there sub-populations that respond more effectively to a treatment?
  • What observations by physicians can serve as early warning signs for the onset of chronic illness?
  • Can we detect emerging medical epidemics from social networks?
  • Can we build personalized real-time data-driven care managers to manage chronic conditions?
  • Can we aid medical decision making in high-risk breast, ovarian, prostate, and colorectal cancer families based on computational analyses of protein evolution and structure?
  • How can clinical random trials be optimized for better estimates of treatment effects?
  • What patterns can we discover from large-scale DCE-MRI and fMRI data?

These challenges require novel machine learning algorithms tailored to the challenges of large scale medical data. We seek to build accurate and reliable algorithms upon which doctors can make decisions, from the level of individual patients to widespread public policy.

The new Center for Personalized Cancer Medicine and Center for Population Health Information Technology (CPHIT) both aim to systematically improve patient care through learning algorithms that run on massive datasets of genetic data or electronic medical records.


My Notes