Introduction

Large Language Models (LLMs) have revolutionized the way we process sequential data. In healthcare, this means better predictive diagnosis and patient triage.

The Attention Mechanism

At the core of the transformer is the self-attention mechanism, defined as:

Where:

• $Q$ is the Query matrix
• $K$ is the Key matrix
• $V$ is the Value matrix
• $d_k$ is the dimension of the keys

This allows the model to weigh the importance of different tokens in a sequence [1].

Python Implementation

Here is a simplified self-attention implementation in PyTorch:

import torch
import torch.nn.functional as F

def scaled_dot_product_attention(query, key, value):
    d_k = query.size(-1)
    scores = torch.matmul(query, key.transpose(-2, -1)) / torch.sqrt(torch.tensor(d_k, dtype=torch.float32))
    attention_weights = F.softmax(scores, dim=-1)
    return torch.matmul(attention_weights, value)

Clinical Application

When applied to Electronic Health Records (EHR), we treat patient history as a sequence of events $E = \{e_1, e_2, ..., e_n\}$. The probability of the next event (diagnosis) is modeled as:

This probabilistic approach helps in early detection of anomalies [2].