Finding relationships through convoluted data

Data is a window into something you actually care about. If you’re listening to a patient’s heart, the sound waves are data. But you really care about the heart valves you can’t see or touch directly.

So data is useful, but it can be misleading. This is unavoidable. The best way to avoid being misled is to think carefully around your data: what other explanations might explain what I saw?

“Doesn’t look like anything to me…”

Here I’ll talk about one major alternative explanation: your variables aren’t linear but you’re analysing them like they are. …


A view from engineering: Part 1

Surgery’s a vividly visible part of medicine — every medical television show has an arrogant surgeon somewhere in the mix. It makes for compelling television, and it definitely makes an impression, but what’s surgery really like?

The best person to tell you what surgery is really like is, you know, a real surgeon. But they don’t really get time to write stuff like this. So you’ll just have to tag along with me!

Here are a few things I came away with from my third/fourth year surgery rotations as a medical student. Uniquely, I’m still an engineer at heart (my medical school grades prove that) so I’ll try to take a systems-approach to my perspective.

Surgery is engineering

Surgery is not like engineering, it is engineering. The central drive is to achieve a goal, whether the science is there or…


Two very different visions for science in medicine

Medicine is surprisingly effective. So much of it has evolved from anecdotes and experience, not science, so it can be hard to explain why it works when it works.

Still, we’ve all decided to make medicine more scientific— make it more effective, efficient, and ethical. We agree on the destination, but not really on the route.

In this post I’ll outline two broad approaches to bringing evidence to medicine: a data-driven approach and a data-congruent approach. I’ll end with some suggestions about a balanced path forward that prioritizes patients.

But first, what is evidence?

We all have an intuition for data: it’s that thing we…


Training and Testing your Understanding

Every physician asks questions to their patients and tries to understand what’s behind the patient’s response.

Turns out, these questions provide some of the first bits of data physicians work with. Our goal is to then understand what’s causing the symptoms from the data we have.

Follow along with our MedML Workshop (video) and Python Notebook (colab).

In this post we’ll talk about two ways we can do inference on our data

  1. Standard Approach — ‘Evidence-based Medicine’ style focusing on experiments
  2. Training/Testing Splits — ‘Machine Learning’ style focusing on analysis

Standard Approach

We think heart rate and cardiac output are related to…


Data is a window for understanding your patient

Observation is at the core of a physician’s job. From the questions we ask when we first meet, to the electrolytes we collect from the patient — it’s all observation towards the goal of healing.

Data is a fancy word for ‘structured observations’, a way to consistently and systematically assess. Machine learning (ML) is all about finding patterns, whether simple or complex, in data using modern computers.

Follow along with our MedML Workshop (video) and Interactive Notebook (https://bit.ly/3hhXqCB).

In this article we’ll give a brief introduction to data, the ecosystem it lives in, and where ML helps us as physicians.

Vineet Tiruvadi

Neuroengineer, Control Theorist, Medical Student at Emory/GeorgiaTech

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