Understanding 'data cleaning' in equipment service, and the tools used to do it

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How can OEMs and in-house service teams work better together? Experts from both sides get together and share their views at AAMI

Understanding the value of data analytics in HTM Helps with purchasing, keeping inventory of equipment, end-of-life decisions for devices

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Tips for creating better collaboration between HTM and IT Streamlining these increasingly complex partnerships

AAMI Product Showcase A sneak peek at some of the products to check out on the show floor

Clinical engineering and the science of the capital budget process Purchasing insights from the experts at MD Buyline

Q & A with Robert Jensen, president and CEO of AAMI Find out what to expect at AAMI Exchange, the premier event for the HTM community

Barriers to genuine service collaboration with OEMs are hurting hospitals A call to better-align objectives toward value-based care

Testing equipment continues to advance Managing your systems and scanners requires the right tools for the job

Understanding 'data cleaning' in equipment service, and the tools used to do it

por John R. Fischer , Staff Reporter
  • Unsupervised Learning – The computer relies on patterns in all images to identify variables, without "knowing" what the image is. This is best for anomaly detection and differentiating stand-alone activities from continuous ones.

  • Reinforcement learning – The computer learns from its mistakes again and again until it completes the task error free and without being directed. This is often used in robotics and navigation.

  • Semi-supervised – Groups of different variables are assigned in different ways, with the algorithm over time learning the most optimal approach. Used in speech recognition, web page classification, and image recognition and classification.


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Deep Learning
A more accurate and faster form of machine learning, deep learning does not require as much upfront, as the tools and framework are already built in.. It can train networks and adjust variables within them. The downside is the greater amounts of data required for training, compared to standard machine learning.

  • Convolutional – Extracts complex features of data at each level to determine the output. This is suited well for noisier images that contain features other than the specific information one is looking for.

  • Recurrent Networks – Form of deep learning that stores information in context nodes so that the machine can learn data sequences and output another sequence. This is especially helpful for real language translation.

  • Generative Adversarial Networks (GANS) – Composed of two neural networks — one of which produces real images, another which produces false images. These networks teach one another to detect fake from real data and generate information that is indistinguishable. With it, users could potentially create a healthy 2D or 3D image of a scan for a person with a tumor to deceive radiologists and oncologists. “All kinds of bad applications can happen from this. We certainly have to find a way around this because it is just too easy to do at this point,” said Garel.

He adds that if used correctly, all of these and other tools available can help speed up data cleaning and ensure faster completion of work orders for workflow and quality patient care.

“Our hypothesis is that we can apply data science to clean up enough of this data so we can actually make it useful to generate results. That’s the goal.”

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