Newswise — The Johns Hopkins Applied Physics Laboratory (APL), in Laurel, Maryland, and Johns Hopkins Medicine have launched the Precision Medicine Analytics Platform (PMAP), a new data analytics tool that will facilitate big-data research across the JHU enterprise and enable clinicians to make discoveries and improve their patient care.

PMAP was released recently at the second annual Johns Hopkins in Health Precision Medicine Symposium in Baltimore, where national thought leaders joined experts from APL, the School of Medicine and Johns Hopkins Information Technology to share the latest research discoveries and new JHU-developed resources that will allow investigators to more efficiently request and analyze patient information in a secure environment and guide health care providers in prescribing more appropriate treatment plans for patients.

The Promise of Precision Medicine

Precision medicine has the power to transform the understanding and treatment of diseases, said Antony Rosen, vice dean for research at Hopkins Medicine. “This is a particularly promising moment for harnessing big data, because high-powered computers can analyze newly available troves of information, including data from genetic sequencing, heart monitors, images and electronic medical records. New technologies make it possible for researchers to combine and analyze data that before was hard to quantify, such as text from clinic notes.”

To understand the subgroups of diseases, precision medicine researchers gather and study vast amounts of information on thousands of patients and their families. “As a result, patients get the treatment that is right for them, avoiding unnecessary tests and therapies,” said APL’s Geoff Osier, PMAP project manager. “PMAP gives researchers access to data, a virtual collaborative workspace and tools to help develop new algorithms that can ultimately improve clinical decisions and patient care.”

Precision medicine is an emerging approach to disease prevention and treatment that considers a person’s unique behavioral, biological, social and environmental determinants of health. A precise understanding of an individual’s health determinants can be used keep them healthy or, when necessary, to treat them and help them quickly recover from illness or injury.

Key components of precision health include the ability to comb data found in familiar sources such as medical records and research studies as well as data from nontraditional sources such as patient-reported surveys. But the volume, variety and velocity of this information far exceed the ability of researchers and systems to capitalize on the valuable insights potentially existing in the data.

Leveraging the Data

To tackle the challenge, APL pulled together a team from a variety of disciplines, including systems engineering, cloud platform construction, cybersecurity, data and computer science, and system usability analysis. In collaboration with Johns Hopkins inHealth, the university and health system’s precision medicine effort, the team developed an information technology system that couples biomedical research and discovery with clinical decision-making in a continually learning precision medicine system.

They designed an analytics platform that supports both precision medicine research and health care delivery as the basis for the learning health care ecosystem. Key elements of this platform include accessing electronic medical records, imaging, genomics and many other health records; transforming and loading data from those systems into a “data commons”; and having appropriate security and access controls and a robust analytics framework.

The partnership builds on APL’s expertise in data analytics and clinical and research precision medicine assets at Johns Hopkins. For several conditions, including multiple sclerosis and prostate cancer, JHM has launched precision medicine centers of excellence where these new technologies and measurement tools have already been applied.

“We helped clinicians at the Center of Excellence for Prostate Cancer extract meaningful data from biopsy reports using a natural language processing machine learning model,” Osier noted. “We’ve changed clinical data representation in the Multiple Sclerosis clinic. We’ve also done some initial development with clinicians to create tools that will be useful for allowing data to drive the creation of subpopulations within their cohort.”

APL will spend the next few months helping to promote data science at Johns Hopkins Medicine.

“The clinicians and researchers in the Precision Medicine Centers of Excellence have compelling, hard challenges associated with how to better care for their patients, and we intend to work with them to make discoveries in the treasure trove of data on PMAP,” Osier said. “As we work on specific problems with specific researchers, we will work to create generalizable tools that others can leverage for their own challenges — the creation of a data science ecosystem on PMAP is a key goal for transforming the hospital.”

Read more about the other precision medicine tools launched at the symposium.

Media contact: Paulette Campbell, 240-228-6792,

The Applied Physics Laboratory, a not-for-profit division of The Johns Hopkins University, meets critical national challenges through the innovative application of science and technology. For more information, visit

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