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?
- Can new hires get set up in the environment to run analyses on their first day?
- Can data scientists utilize the latest tools/packages without help from IT?
- Can data scientists use on-demand and scalable compute resources without help from IT/dev ops?
- Can data scientists find and reproduce past experiments and results, using the original code, data, parameters, and software versions?
- Does collaboration happen through a system other than email?
- Can predictive models be deployed to production without custom engineering or infrastructure work?
- Is there a single place to search for past research and reusable data sets, code, etc?
- Do your data scientists use the best tools money can buy?
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
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.
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.
They are taking medical data science rather seriously. The folks at Stanford Medicine are onto something.
Source: Biomedical Data Science Initiative @ Stanford Medicine
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
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
Radiologists have a day job (or a night job, depending on your precise definition of “radiologist.”) Many people want to learn the syntax of a computer language, while some want to keep up on existing skills.
If your goals are similar to mine, these might help. Now these are not ways to learn to write code (I’ll write about that later), but ways to brush up on existing skills.
Here are five things to help keeping up your coding skills:
Work on a Project
Most radiology practices can be improved by better use of technology Continue reading
Not long ago, the ability to create smart data visualizations, or dataviz, was a nice-to-have skill. For the most part, it benefited design- and data-minded managers who made a deliberate decision to invest in acquiring it. That’s changed. Now visual communication is a must-have skill for all managers, because more and more often, it’s the only way to make sense of the work they do.
A June 2016 Harvard Business Review article by Scott Berinato discusses the four types of data visualization, in their traditional “boil complex stuff down to a 2×2 matrix” method no less. In short, what works depends on the level of details necessary to convey the purpose.
Two axes of data visualization – what works best depends on the purpose
The overall concepts are reminiscent of concepts by Edward Tufte and his many, excellent, books on visualization.
The HBR article is worth a read for anyone interested in business intelligence, data analytics, or data visualization (which, as Berinato says, is probably a misnomer – it’s not the visualization that matters, but the question it seeks to answer).