Linear regression is a powerful data science tool and one you definitely need to be familiar with. If you’re not, that’s okay… Read last weeks article here for a good overview of the topic and its use case.

So, you want to describe the linear relationship between a set of features and an outcome. You decide that linear regression is your plan of attack, and boot up Rstudio, or your favourite Python editor. Next, you import your machine learning libraries and write some code. …


Machine learning without linear regression is like gin without tonic. It’s one of the first weapons in a data scientists arsenal and remains a powerful and popular tool in research as well as commercial applications.

Linear regression was first used in the 1800s to predict planetary movements! It has since been implemented in fields ranging from economics to environmental science successfully describing linear relationships between observations and their outcome. It is robust to different forms of input data, but the outcome data must be continuous. Like most things, it makes more sense with an example and some intuitive graphs…

Lets…


Classification remains the primary role of a physician. For the most part, patients don’t appreciate complicated risk scores and survival rates (particularly the latter). Besides the emotional support, when someone visits the doctors they want to know whether they have a defined disease and what they can do to treat it.

Humans are okay at classification, but computers are better. In the UK, a doctor requires 5/6 years at medical school and a minimum of 5 further years of training (depending on the specialty) before they become fully independent. Most machine learning algorithms require orders of magnitude less time to…


The future of medicine is decision augmentation using novel machine learning algorithms in parallel with expert clinical experience. Once we incorporate the massive advancements in personalised medicine, made possible by Omics association studies, we will have a futuristic healthcare service rivalled only by Star Trek’s “Tricorder”…

Ignoring the cheesy Star Trek reference, it is truly exciting to be on the brink of the next revolution in healthcare (In my humble opinion). Millennial doctors are flocking in their hundreds into the field of Artificial Intelligence in Healthcare and for good reason. …


Decision trees are simple yet powerful. They offer advanced machine learning with relatively high interpretability (contrasting powerful “black box” algorithms such as neural networks). If you’ve ever wondered how computers are able to learn for themselves to address important questions, then stick around for a few.

My particular interest is in how Artificial Intelligence can be applied to Healthcare, hence I’ll be using examples of this to show you how decision trees function in the wild.

The name of the game is to predict, or group, patients with certain health characteristics into those with respiratory disease vs those without.

Decision…


Neural nets have exploded in popularity in recent years. Their use range’s from self driving cars to diagnosing skin cancers. I’m going to try and explain what they are in 5 minutes. To achieve this we need to break them down into their components and tackle these smaller chunks. I’m a medical doctor, so this will be light on the maths. Phew…

Before I start the breakdown, it’s important to know the general use case for this kind of machine learning architecture. Primarily, these kind of AI solutions input image data then process, segment and classify to output useful information…


A Doctors perspective…

I’ve spent 8 years training and studying to become the Doctor I am today. During that time I’ve learnt to diagnose and treat complex disease as well as communicate the situation to my patients. How long would it take a computer to learn to replace me? Thankfully, at the present time this doesn’t seem likely possible. But, like many professions, the way we interact with computers to augment our day job is changing rapidly. This is, in part, due to the implementation of Artificial Intelligence to “automate the boring stuff”, as Al Sweigary puts it. …

James Parkin

Medical Doctor and Data Scientist living in London. I write about novel Machine Learning techniques being used to solve Healthcare’s biggest problems.

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store