Category Archives: Technology and Informatics

Posts related to technology, gadgets, cloud, informatics, or just about anything that is/can be/plugs into a computer. Relationship to radiology optional.

DICOM Processing and Segmentation in Python – Radiology Data Quest

Posted on January 30, 2017 | 1 comment

There is something strangely satisfying about being able to take things apart and putting it back together.  Inspired by the popularity of Lego sets in our childhoods, Minecraft brought this sense of wonder to video games.

For those of us who are life-long tinkerers who happen to be radiologists, I published in Radiology Data Quest a DIY on how one take DICOM apart and manipulate it.  All in Python, no less.

 

DICOM is a pain in the neck.  It also happens to be very helpful.  As clinical radiologists, we expect post-processing, even taking them for granted. However, the magic that occurs behind the scene…

Source: DICOM Processing and Segmentation in Python – Radiology Data Quest

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Informatics Sessions at RSNA 2016 You Don’t Want to Miss

Posted on November 27, 2016 | Leave a comment

The Radiology Society of North America (RSNA) Annual Meeting is a place to expand your knowledge base, both by taking a deeper dive into your core interest and by getting your feet wet a few new skills.

If informatics is something you’ve been interested in but need a good way to get started, then the RSNA offers some solid opportunities for beginners. Continue reading →

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When Gut Instinct and Logic Work Together

Posted on November 23, 2016 | Leave a comment

After the recent presidential election, you are probably either particularly alarmed or especially excited about the outcome.  Regardless of your particular political predilection, it is fair to say that this election puts data science on its head when so many got so wrong.

On an earlier issue of Harvard Business Review, the venerable magazine shared a piece of research from the University of Southern California on forecasting. When forecasting sales, the best estimators use a combination of intuition and logic – with both the logic-heavy and intuition-heavy forecasters performing less accurately.

In the age of artificial intelligence and big data, it can be sobering to realize that despite the staggering volume of data we are now collecting, ignoring your gut instincts can take a heavy toll on your decision-making abilities.

 

Source: “What type of forecaster are you?” Harvard Business Review (March): 26.

 

 

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Posted in Figure Stuff Out, Technology and Informatics

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6 Elements of a Data-Driven Informatics Solution (1/3)

Posted on October 3, 2016 | Leave a comment

Big Data has become a radiology buzzword  (the others: machine learning, AI, and disruptive innovation are also up there).

However, there is a real problem with using the term Big Data – it isn’t just one set of data problems.  Big Data is a conglomerate of different data challenges: volume of data, heterogeneity of data, or the velocity of data are all important dimensions.  Machine learning and internet of things are others layers superimposed on the big data problem.

Sometimes it is helpful to step back and approach data problems with a common framework, a way to think about how and which facets of data science fit in a real-life workflow in the face of an actual problem.

Below is a 6-element framework that helps me think about data-driven informatics problems. They are generally in chronological order, but they are not “steps” because you frequently will find yourself going back and redefining many things.  However, the framework helps you maintain a big-picture outlook.  The reason any sufficiently complex data problem requires a team approach. Continue reading →

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Posted in Radiology Innovation and Quality, Technology and Informatics

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8 Ways to Gauge The Quality of Your Data Team

Posted on September 4, 2016 | Leave a comment

At the turn of the century, Joel Spolsky came up with the idea of a “Joel Test” – a highly irresponsible, sloppy test to rate the quality of a software team.

Then, this group thought to come up with their own criteria to rate the quality of a data science team.  How do your analysts in the radiology department fare?

The “Joel Test” for Data Science

  1. Can new hires get set up in the environment to run analyses on their first day?
  2. Can data scientists utilize the latest tools/packages without help from IT?
  3. Can data scientists use on-demand and scalable compute resources without help from IT/dev ops?
  4. Can data scientists find and reproduce past experiments and results, using the original code, data, parameters, and software versions?
  5. Does collaboration happen through a system other than email?
  6. Can predictive models be deployed to production without custom engineering or infrastructure work?
  7. Is there a single place to search for past research and reusable data sets, code, etc?
  8. Do your data scientists use the best tools money can buy?

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New Seizure Prediction Competition, and Why It Matters for Radiology

Posted on September 3, 2016 | 1 comment

Kaggle is a website to host coding competitions related to machine learning, big data, or otherwise all things data science.

Newly launched on Kaggle is a healthcare-related competition!  A group of health institutions provided a large data set consisting of three patients’ interictal and preictal (up to 1 hour before) EEG tracings in raw data.  The goal? Predict which “unknown” EEGs are preictal so healthcare providers can intervene.

Also, with the timely arrival of Internet of Things (IoT), wearable, and big data, can you imagine the impact of giving patients an accurate 5-minute warning every time a seizure is about to start? Continue reading →

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Inpatient Radiology Ordering Patterns from Scratch

Posted on August 27, 2016 | Leave a comment

index

If you have taken overnight call, you quickly develop a sense for the emergency department and the inpatient floors. In my institution, radiologists develop hypotheses on how inpatient orders are placed.

For instance, sometimes it might seem as if inpatient radiology exams follow some sort of circadian rhythm.  The data look to confirm it: we see the infamous “x-ray bump” in the early morning, with the increase in CT start more gradually but last later into the day.

Also, are weekdays and weekends any different?  If so, how?

Going on a Quest

With a little coding in Python or R, one can gain a lot of insight into how our referring providers’ lives intertwine with our own. Read the full story in my new post on Radiology Data Quest.

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Geeking out with CMS Outpatient Imaging Data (Lumbar MRI and Mammo)

Posted on August 25, 2016 | Leave a comment


From the Open Data Network I stumbled upon the CMS outpatient imaging data organized by state and decided to peek into the dataset and stick the data onto a US map for fun. Geek out with Joe and me in this new blog Radiology Data Quest.

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Biomedical Data Science Initiative at Stanford

Posted on August 23, 2016 | Leave a comment

They are taking medical data science rather seriously. The folks at Stanford Medicine are onto something.


Source: Biomedical Data Science Initiative @ Stanford Medicine

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Do’s and Don’ts of Data Science

Posted on August 21, 2016 | Leave a comment

Don’t Start with the Data
Do Start with a Good Question

Don’t think one person can do it all
Do build a well-rounded team

Don’t only use one tool
Do use the best tool for the job

Don’t brag about the size of your data
Do collect relevant data

Continue reading →

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