Offline Rheumatology Autoimmune Arthritis & Biologic Response Predictor

Introduction

Rheumatoid arthritis (RA) and psoriatic arthritis management requires continuous evaluation of Disease Activity Scores (DAS28) to adjust disease-modifying antirheumatic drugs (DMARDs) and biologic agents (bDMARDs like TNF-alpha inhibitors). In this cookbook, we demonstrate RheumaTrack-Graph-v1, a local graph neural network (GNN) model that predicts 12-week joint disease activity trajectories and biologic treatment response entirely offline on edge clinic hardware.

Prerequisites

• Python 3.9+
• PyTorch & PyTorch Geometric
• openphr-cli
• Local patient joint examination telemetry & anti-CCP serology data

Step 1: Ingest Joint Telemetry & Serology

Prepare structured local joint examination matrices (swollen & tender joint counts out of 28 standard joints) along with power Doppler ultrasound synovitis grades and inflammatory lab titers (anti-CCP, RF, ESR, CRP).

import json
import torch
import torch.nn as nn
from torch_geometric.data import Data

# Load local joint map telemetry
with open('joint_exam.json', 'r') as f:
    joint_data = json.load(f)

# Edge representation for joint graph (28 anatomical joint nodes)
edge_index = torch.tensor([
    [0, 1, 1, 2, 2, 3, 4, 5],
    [1, 0, 2, 1, 3, 2, 5, 4]
], dtype=torch.long)

node_features = torch.tensor(joint_data['joint_scores'], dtype=torch.float)
graph = Data(x=node_features, edge_index=edge_index)
print("Joint graph initialized with", graph.num_nodes, "nodes.")

Step 2: Execute Local Prediction Pipeline

Run the openphr-cli command to compute 12-week DAS28 trajectories and evaluate TNF-alpha response probability:

openphr-cli run rheumatrack-graph-v1 \
    --input-joint-map ./joint_exam.json \
    --serology ./anti_ccp.json \
    --output-das28-prediction ./das28_trajectory.json

Expected Outcome

A structured JSON output containing 12-week predicted DAS28 scores, remission probability metrics, and recommendations for biologic agent titration without cloud data exposure.