What is computational medicine and why should I care about it?

Computational medicine is a new field at the intersection of medicine, statistics, and computation. But this field is being stymied by lack of data.

Fields like computer vision and natural language processing have benefitted from shared data where researchers can compete and collaborate on high value questions and problems - such as ImageNet for object detection and MNIST for digit recognition.

But computational researchers have no comparable datasets to answer critical questions in health and medicine. Making such datasets available is a key part of building this new field. Once researchers have the raw material they need to develop and apply new computational techniques to medicine, we expect to see similar leaps and bounds in our understanding and capabilities as occurred in other fields.

What kinds of data do you work with?

We focus on data that is high-dimensional, such as imaging and waveforms, which are ideally suited to machine learning.

We also emphasize linking these imaging data to ground-truth outcomes: what happened to the patient’s health, not just what a doctor thought about an image. This allows researchers to develop algorithms that learn from nature—not from humans.

Who do you work with?

We work with a variety of health systems in the United States and internationally to define specific and compelling research questions. We collaboratively build a dataset around those research questions and help the institution conduct analysis and gather findings. We then feature some of the deidentified data variables on our platform for researcher collaboration and competition. 

Interested in partnering with us? Contact us here.

What clinical areas do you focus on?

We are open to focusing on a broad range of clinical areas with our partners. Here are examples of work from previous partnerships:

  • Sudden Cardiac Death
    How can we better understand and predict adverse cardiac risk states? We are working with our partners to link electrocardiogram (ECG) waveforms with clinical outcomes to help identify new, subtle patterns and markers indicative of risk for heart attack.
  • Cancer
    There is still much about cancer’s progression and staging that we do not understand. Patients are commonly subjected to unnecessary procedures while others die from late-stage disease that should have been caught earlier. Linking biopsy specimen images to cancer registry data can enable us to better understand progression, therapeutic responses, and patient outcomes.
  • COVID-19
    Chest x-ray data linked to pulmonary outcomes will allow us to better understand and predict deterioration due to COVID, and other similar respiratory illnesses.

What privacy safeguards do you provide?

All data that is featured on Nightingale is completely de-identified and non-PHI. Our platform hosts researchers inside a secure, monitored computing environment tailored for cutting-edge AI research. 

All content on the Nightingale Open Science platform is strictly limited to non-commercial academic research.

Questions about getting access, platform features, compute resources, data sets, pricing, and collaboration?

Many of these questions are answered in the platform and data FAQs. If you have additional questions, please contact us.

Ready to get involved?