Tag Archives: machine learning

DICOM Processing and Segmentation in Python – Radiology Data Quest

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

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

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

New Seizure Prediction Competition, and Why It Matters for Radiology

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

The [machine learning] race is on – Don Dennison

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Machine learning, real opportunites: Dr. Keith Dreyer’s keynote sets tone for ISC 2016

Dr. Keith Dreyer opens with a keynote during the Intersociety Summer Conference (ISC) with description of data science and overview of how machine learning have evolved over time.

He describes that machines and humans inherently see things differently. Humans are excellent at object classification, recognition of faces, understanding language, driving, and imaging diagnostics. Continue reading

Aside

The use of the phrase, “Artificial Intelligence” has exploded within the past few years as the theme of dozens of our most popular movies and television shows, magazines, books, and social media. This is despite the difficulty that many experts … Continue reading

The Paradox of Standardizing Broad Data

Last October, my team started working on a project to bridge the communication gaps between inpatient general medicine and radiology.  Despite having done a full year of internship before starting residency, we quickly realized that as radiologists we knew very little about healthcare is delivered on the wards.  Understanding how well the imaging workflow runs from ordering to reporting, identifying possible delays by systematically analyzing patient data seemed straightforward.

Hypothesized imaging workflow for admitted medicine patients. Source: post author

A 2-hour meeting, eight weeks of delay, and several email exchanges later, we now rely mostly on manual data collection. This blog post is about what happened. Continue reading