Predicting Patient Triage with the Power of Graphs: A Deep Dive into GNNs

Alright, let’s get real for a sec. Imagine you’re rushing to the ER – you’re freaking out, right? Now imagine the chaos if the nurses didn’t have a system to figure out who needs help first. That’s where patient triage swoops in like a superhero.

The Importance of Patient Triage: No Cap, It’s Life or Death

Patient triage is the ER’s way of sorting patients based on how urgent their condition is. It’s like a priority line, but instead of getting you on the rollercoaster faster, it gets you the medical attention you need, stat. Accurate triage is crazy important – it can literally mean the difference between life and death. But here’s the tea: traditional triage methods can be kinda basic, relying on a few obvious symptoms. They sometimes miss the bigger picture, you know?

Enter Graph Neural Networks: The New Kids on the Block

That’s where our brainy friends, the Graph Neural Networks (GNNs), come in. Think of GNNs as those detectives who can connect the dots that others miss. They’re all about finding hidden relationships in data, like Sherlock Holmes on steroids! In the world of patient triage, GNNs can analyze tons of patient info – think medical history, vital signs, the whole shebang – and find patterns that traditional methods totally overlook. This helps us predict which patients need to be seen ASAP with way more accuracy. Pretty cool, huh?

Our Approach: Turning Patients into Graphs (No, Really!)

So, how did we use these GNNs to up the triage game? We got creative and turned patient data into graphs. Picture this: each patient is a dot (a “node”) on the graph, and we draw lines (“edges”) between patients who share similar symptoms or medical histories.

Data Deep Dive: What We Analyzed

No good detective works without clues, right? We used two awesome datasets for our little experiment:

1. Kaggle Patient Priority Classification Dataset: This dataset was like our playground. It had over 6,900 patient records, each with 16 juicy features like age, gender, symptoms, and of course, their triage level (Red, red, Yellow, or Green). Thanks, Kaggle!
2. MIMIC-IV-ED Dataset (Demo Version): We wanted to get real-world, baby! So we also used a demo version of the MIMIC-IV-ED dataset, which is full of data from actual emergency department visits. We focused on 14 key features and used their acuity level (from 1 to 4) as our target.

But before we unleashed the GNNs, we had to get our data squeaky clean.

Prepping the Data: A Little Scrub Never Hurt

Data preprocessing is kinda like getting ready for a first date – you gotta look your best! Here’s what we did:

• Data Cleaning: We tossed out any duplicate records because nobody likes a copycat. Plus, we handled those pesky missing values by either filling them in or showing them the door (depending on the situation, of course).
• Categorical Feature Encoding: Computers aren’t big fans of words. So, we translated all those text-based features (like residence type or smoking status) into numbers using a trick called label encoding. It’s like giving each category a secret code!
• Data Normalization: Imagine trying to compare an elephant and a mouse – kinda hard, right? That’s why we normalized our numerical features using Min-Max scaling. It’s like putting everything on the same scale, from zero to one. No more elephant-and-mouse situations!

Oh, and remember that Kaggle dataset? It had some class imbalance issues – basically, some triage levels had way more examples than others. Not cool, man. So, we used a technique called SMOTE to even things out. Think of it as giving the underdog a fighting chance!

Building the Patient Network: Connecting the Dots

Okay, now for the fun part – turning patients into graphs! We used different methods to connect the dots (patients) based on how similar or different their features were:

• Cosine Similarity: This one’s all about angles. We drew lines between patients whose feature vectors were super tight (aka, high cosine similarity). The higher the threshold, the more alike the patients had to be to get connected.
• Euclidean Distance: Remember that Pythagorean theorem from school? Yeah, this is kinda like that. We linked patients who were close to each other in terms of distance. The lower the threshold, the closer they had to be to become BFFs (best feature friends, duh!).
• Manhattan Distance: Imagine navigating a city grid – that’s Manhattan distance! We connected patients based on their distance along those gridlines. Again, closer meant a higher chance of friendship.
• Minkowski Distance: This one’s like the cool, customizable cousin of Euclidean distance. We played around with different parameters to see how it affected our patient connections.

Once we had our fancy graphs, we analyzed them like crazy scientists. We looked at the number of nodes (patients), edges (connections), and even those poor, lonely isolated nodes (patients with no connections – sad!).

Unleashing the GNN Powerhouse: Time to Shine

With our patient graphs ready to roll, it was time to bring in the big guns – the GNN models! We trained three different types of GNNs:

• Graph Convolutional Networks (GCNs): These are like the all-around athletes of the GNN world. We used two different GCN architectures, each with its own special sauce of layers and dimensions.
• Graph Attention Networks (GATs): These guys are all about focus! They use attention mechanisms to figure out which connections in the graph are the most important for making predictions.
• GraphSage: This bad boy is built for handling massive datasets. It breaks down the graph into smaller chunks, making it easier to train without crashing your computer.

We fed our GNNs a delicious cocktail of data and let them loose to predict patient triage levels. To make sure they were learning properly, we used cross-entropy loss as our guide. It’s like a teacher grading their work – the lower the loss, the better they were doing.

The Moment of Truth: Did Our GNNs Kill It?

Drumroll, please! It’s time to see how our GNNs performed. We’ll dive into the juicy details in the next section, but spoiler alert: they totally rocked it! We’ll show you how each GNN stacked up against the others and even compare them to those old-school, tabular data models. Get ready for some mind-blowing results!