Tag Archives: Big Data

#Health2stat – all about data: experts from #nih talk about #health and #bigdata

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Translational science – Rosemary Filiart

Advancing research to improve human health.
Integrating multiple disciplines.
Research and clinical data sets, applying appropriate privacy protections and using big data to reveal understanding.

Re-purposing data in a predictable and reproducible way and advancing towards personalized medicine and providing actionable and informed decisions at the point of care.

Repurposing yesterday’s data – Lisa Federer

Radical re-use an example is shipping containers becoming hotel rooms http://www.sleepingaround.eu

Facilitating re-use

Translate expertise from analog to digital – look to librarians
1. Description: Standardized metadata eg. pubmed
2. Discoverability: data catalogs – these build on metadata
3. Dissemination: facilitating sharing while protecting privacy and intellectual property
4. Digital infrastructure: cyber infrastructure
5. Data Literacy: equipping people with tools and knowledge to be able to access data

How do we re-think the future of data. We don’t throw data away so how do we prepare it for future use.

Using clinical data at NIH – BTRIS data mining – Jim Cimino

The National Institutes of Health consists of 27 institutes and centers, many of which conduct clinical research. Research data are collected in the NIH Clinical Center’s electronic health record (EHR) and institute and laboratory systems. The Biomedical Translational Research Information System (BTRIS) is a repository that collects data from these sources to provide unified tools to support researchers in the analysis of their data. BTRIS is available to any NIH researcher who wishes to obtain data for secondary analyses to reexamine old questions or ask new ones. Non-NIH researchers can collaborate with NIH researchers in the analysis of BTRIS data.

50 data sources – mostly live daily feeds
Half a million patients
140,000 clinical concepts

They have a de-identified data set that goes back to 1976 that can be queried.

The self service query tool is incredibly powerful.

Discovering medical knowledge using BTRIS – Vojtech Huser

Vojtech works with BTRIS using R and a few other tools.
Meta map is an internal nih tool.

There are challenges in de-identifying data. Search and replace has to be very precise because there are numerous conditions that are named using people’s names. Eg. Removing Parkinson could also remove Parkinson’s disease references.

One interesting request: don’t take your EHR data to heaven – donate it to science.
This is something that consumer-mediated exchanges like MedYear.com could facilitate.

TB a world health problem – Stefan Jaeger

How do we detect TB in remote populations?

Some challenges: HIV populations have weakened immune systems making them susceptible to TB.
Some strains of TB are drug resistant.

USAID – AMPATH partnership working in Africa. Developing a portable X-ray machine that can be taken to villages to test people.

The next step is to use automatic image processing to identify TB in X-rays since there aren’t enough radiologists in Africa to review X-rays manually.

New computational tools and models for data mining – Jim DeLeo

John Von Neumann – “machines can think” 1955

Moved from what can the machine do to “what do we want the machine to do”

Jim’s area of expertise is computational intelligence.this subsumes artificial intelligence.

Machine Learning – just like humans by looking at lots of data and cluster and classify the data.

Jim’s latest focus is extreme multi-disciplinary teaming.
Every team member is passionate about the work. Short term. Clear deliverables.

Deep learning – new computational tools for biomedical learning – Jonathan Simon

Deep neural networks – a new technique for analyzing large volumes of data.
Machine learning by using existing data

Neural networks – simple inter-connected computational units. Modeled on the way the brain works.

Deep neural networks – based on neural networks and have very many hidden layers of computation. These emerged since 2006 as we gained new tools to analyze data and computational power became more affordable.

Biomedical deep learning is co-opting tools like image processing and adapting to medical applications. These tools are limited by the amount of data that is available. Fortunately more and more biomedical data is being made available online every year.

Now for the Q&A…

Mark Scrimshire
Health & Cloud Technology Consultant
Blog: http://blog.ekivemark.com

email: mark@ekivemark.comStay up-to-date: Twitter @ekivemark

Patient Engagement and Health Standards and the start of @Medyears

Today I am heading to Northern Virginia for meetings. On the agenda will be topics such as Health Standards and the Transformation underway in Healthcare. Consequently I thought it would be a good day to wear one of my Walking Gallery Jackets.

AEIOU of Patient Engagement

AEIOU of Patient Engagement

This jacket is one of two jackets painted for me by Regina Holiday as part of the Walking Gallery of Healthcare. The theme of this jacket came from a day at HIMSS in 2012. I was speaking there on Patient Engagement and after following Dr. Regina Benjamin, then the Surgeon General, and Regina Holiday you have to make an impact since they are incredibly engaging speakers. One of the themes I used in my talk was The “AEIOU of Patient Engagement.” I wanted to leave the audience with some simple guidelines for embracing Patient Engagement in Healthcare. Hence, AEIOU

  • Actionable
  • Easy
  • Immediate
  • Open
  • Unobtrusive

When I found this jacket on a trip to New York I asked Regina to paint it with the AEIOU of Patient Engagement.

After my presentation I also met Panha Chheng the founder of Personiform and Medyear.com. I am still advising Medyear as they build the first Personal Health Network. The technical Term Du Jour is “Consumer-Mediated Exchange.” What a mouthful. Basically Medyear is putting in to practice my recommendations. Medyear is built using the emerging BlueButton and Direct Project standards. It puts the member in charge of their Health information. Allowing them to consolidate information from multiple sources in to a timeline that they control. It then provides simple, but very powerful controls to allow them to share their health information at a very granular level with whomever they choose, for as long as they choose.

Medyear has come a long way since that first meeting with Panha. The Medyear platform has been built on Sqrrl to provide strong, atomic level security. This is essential for Medyear to give our members the granular security control to individual entries their timeline. I have been working with the Sqrrl team to put together a webinar about building Healthcare applications securely in the Cloud. I will post more details soon.