This year I’ve had the pleasure of participating in a team at MIT identifying characteristics that make patients likely to be intensive care unit “Frequent Fliers” - patients with multiple ICU admissions in a short time span. This series will explore the implementation of text-based patient phenotyping we use as …

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I’ve recently been upgrading my tool set to the latest versions of Python, Keras, and Tensorflow, all running on a docker-based GPU-enabled deployment of Jupyter with CUDA 8 installed. In this notebook I walk through a quick test to verify that the GPU is recognized and is working …

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We saw in the previous post that there is promise to using ICD9 codes for pre-classifying encounters more likely to have our concepts of interest. In this post we’ll walk through building simple logistic regression classifiers based on a training data set, and will evaluate their performance on a …

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With the background work done in the previous posts, let’s get to the meat of the problem. I’m currently working with a team from an excellent MIT course, Secondary Analysis of Health Records, led by Dr. Leo Anthony Celi. My team is composed of 4 clinicians and 3 …

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In the previous post I showed a convenient way to navigate the ICD9 hierarchy with Python, now let’s use that to extract the full taxonomy of ICD9 codes for patients who we’ll using to train a classifier. In this post we’ll be extracting the original ICD9 codes …

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ICD9, the International Statistical Classification of Diseases, is one of the common coding systems often used in medical databases for indicating patient conditions. A handy feature of it is that it’s a hierarchical system. Just as the Dewey Decimal System lets someone quickly find a book on geometry (library …

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One tricky thing to deal with in the transition from MIMIC II to MIMIC III has been that IDs have changed and are sometimes missing, so work that a collaborating team did to annotate patient notes in MIMIC II is not translatable to MIMIC III. This notebook shows one way …

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