Building a Personalized Healthcare Recommendations AI Agent on GCP: A Comprehensive Guide

This article provides a detailed guide to building a Personalized Healthcare Recommendations AI Agent on Google Cloud Platform (GCP). We will explore the necessary GCP services, a comprehensive architecture, sample training data, the implementation of model training using Vertex AI, and the creation of a backend service to serve recommendations via a Vertex AI Endpoint.

Tech Stack:

• Real-time Data Ingestion: Google Cloud Pub/Sub
• Cloud Functions or Dataflow
• Low-Latency Data Store (Short-Term Memory): Cloud Memorystore for Redis
• Scalable & Secure Data Store (Long-Term Memory & Feature Store): Cloud Healthcare API (FHIR Store), BigQuery, Vertex AI Feature Store
• Recommendation Model Hosting & Inference: Vertex AI Endpoints
• AI/ML Frameworks: TensorFlow or PyTorch with Vertex AI Workbench and Vertex AI Training
• Cloud Workflows or Composer
• API Gateway: API Gateway
• Monitoring & Logging: Cloud Monitoring, Cloud Logging, Cloud Trace
• Infrastructure as Code (IaC): Terraform or Deployment Manager
• Security & Compliance: Cloud IAM, VPC Service Controls, Cloud KMS

Conceptual Architecture Diagram:

Sample Training Data for Personalized Healthcare Recommendations:

patient_id,age,gender,condition,prior_treatment,response_to_treatment,recommended_treatment
patient001,65,male,diabetes,insulin,positive,lifestyle_change|new_medication_a
patient002,48,female,hypertension,diet_exercise,neutral,medication_b|monitor_bp
patient003,72,female,arthritis,pain_relievers,positive,physical_therapy|new_pain_med
patient004,33,male,asthma,inhaler,positive,continue_current_treatment
patient005,59,male,diabetes|hypertension,insulin|medication_c,neutral,new_medication_a|medication_d
patient006,29,female,anxiety,therapy,positive,continue_therapy|mindfulness_exercises
patient007,80,male,heart_disease,medication_e,negative,new_medication_f|surgery_consult
patient008,41,female,migraine,triptans,positive,continue_current_treatment|lifestyle_adjustments
patient009,68,male,prostate_cancer,radiation,neutral,hormone_therapy|monitor_psa
patient010,52,female,depression,antidepressants,positive,continue_current_treatment|support_group
    

Code to Implement Model Training (Vertex AI – train.py):

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import MultiLabelBinarizer
from sklearn.metrics import classification_report
import joblib
import os
import argparse

def parse_args():
    parser = argparse.ArgumentParser()
    parser.add_argument('--model_dir', type=str)
    parser.add_argument('--data_path', type=str, default='/gcs/your-bucket/healthcare_data.csv')
    return parser.parse_args()

def load_data(data_path):
    df = pd.read_csv(data_path)
    return df

def preprocess_data(df):
    df['condition'] = df['condition'].str.split('|')
    df['prior_treatment'] = df['prior_treatment'].str.split('|')
    df['recommended_treatment'] = df['recommended_treatment'].str.split('|')
    mlb_condition = MultiLabelBinarizer()
    df_condition = pd.DataFrame(mlb_condition.fit_transform(df['condition']), columns=mlb_condition.classes_, index=df.index)
    mlb_treatment = MultiLabelBinarizer()
    df_prior_treatment = pd.DataFrame(mlb_treatment.fit_transform(df['prior_treatment']), columns=mlb_treatment.classes_, index=df.index)
    df = pd.concat([df, df_condition, df_prior_treatment], axis=1).drop(columns=['condition', 'prior_treatment', 'patient_id']).dropna()
    return df, mlb_condition.classes_.tolist(), mlb_treatment.classes_.tolist(), df['recommended_treatment'].tolist()

def train_model(X_train, y_train):
    model = RandomForestClassifier(n_estimators=100, random_state=42)
    model.fit(X_train, y_train)
    return model

def evaluate_model(model, X_test, y_test):
    y_pred = model.predict(X_test)
    print("Classification Report:")
    print(classification_report(y_test, y_pred, zero_division=0))

def save_model(model, model_dir):
    model_path = os.path.join(model_dir, "model.joblib")
    joblib.dump(model, model_path)
    print(f"Model saved to: {model_path}")

if __name__ == "__main__":
    args = parse_args()
    data_df = load_data(args.data_path)
    processed_df, condition_classes, treatment_classes, target = preprocess_data(data_df)
    X = processed_df.drop(columns=['recommended_treatment'])
    y_single = [item[0] if item else 'unknown' for item in target]
    X_train, X_test, y_train, y_test = train_test_split(X, y_single, test_size=0.2, random_state=42)
    model = train_model(X_train, y_train)
    evaluate_model(model, X_test, y_test)
    save_model(model, args.model_dir)
    print("Condition Classes:", condition_classes)
    print("Treatment Classes:", treatment_classes)
    

Code to Call Trained Model via Vertex AI Endpoint:

from google.cloud import aiplatform
import json

def get_vertex_ai_recommendations(patient_data):
    project_id = "your-gcp-project-id"
    location_id = "us-central1"
    endpoint_name = "your-healthcare-recommendation-endpoint"
    aiplatform.init(project=project_id, location=location_id)
    endpoint = aiplatform.Endpoint(endpoint_name=endpoint_name)
    try:
        prediction = endpoint.predict(instances=[patient_data]).predictions[0]
        return prediction
    except Exception as e:
        print(f"Error calling Vertex AI endpoint: {e}")
        return []

if __name__ == "__main__":
    sample_patient_data = {
        "age": 70,
        "gender": "female",
        "condition": ["hypertension", "arthritis"],
        "prior_treatment": ["diet_exercise", "pain_relievers"],
        'diabetes': 0, 'insulin': 1, 'hypertension': 1, 'diet_exercise': 1, 'arthritis': 1, 'pain_relievers': 1
    }
    recommendations = get_vertex_ai_recommendations(sample_patient_data)
    print("Personalized Healthcare Recommendations:")
    print(json.dumps(recommendations, indent=2))
    

Integrated Backend Service for Healthcare Recommendations (Flask):

from flask import Flask, request, jsonify
from google.cloud import aiplatform
import json
import redis

app = Flask(__name__)

PROJECT_ID = "your-gcp-project-id"
LOCATION_ID = "us-central1"
ENDPOINT_NAME = "your-healthcare-recommendation-endpoint"
REDIS_HOST = "your-memorystore-redis-ip"
REDIS_PORT = 6379

aiplatform.init(project=PROJECT_ID, location=LOCATION_ID)
endpoint = aiplatform.Endpoint(endpoint_name=ENDPOINT_NAME)
redis_client = redis.Redis(host=REDIS_HOST, port=REDIS_PORT)

def get_patient_features(patient_id):
    recent_activity_raw = redis_client.lrange(f"recent_activity:{patient_id}", 0, -1)
    recent_activity = [json.loads(item.decode('utf-8')) for item in recent_activity_raw]
    patient_features = {
        "recent_activity": recent_activity,
        "age": 68,
        "gender": "male",
        'diabetes': 1 if any(act.get('data', {}).get('condition') == 'diabetes' for act in recent_activity if act.get('activity_type') == 'diagnosis') else 0,
        'hypertension': 1 if any(act.get('data', {}).get('condition') == 'hypertension' for act in recent_activity if act.get('activity_type') == 'diagnosis') else 0,
        'insulin': 1 if any(act.get('data', {}).get('medication') == 'insulin' for act in recent_activity if act.get('activity_type') == 'medication_taken') else 0,
        'diet_exercise': 1 if any(act.get('data', {}).get('treatment') == 'diet_exercise' for act in recent_activity if act.get('activity_type') == 'treatment_given') else 0
    }
    return patient_features

def get_vertex_ai_recommendations(patient_features):
    try:
        prediction = endpoint.predict(instances=[patient_features]).predictions[0]
        return prediction
    except Exception as e:
        print(f"Error calling Vertex AI endpoint: {e}")
        return []

@app.route('/recommendations/', methods=['GET'])
def get_recommendations_api(patient_id):
    patient_features = get_patient_features(patient_id)
    recommendations = get_vertex_ai_recommendations(patient_features)
    return jsonify({"patient_id": patient_id, "recommendations": recommendations})

if __name__ == '__main__':
    app.run(debug=True, host='0.0.0.0', port=8080)
    

Important Considerations:

• Data Integration and Feature Engineering: The get_patient_features function needs to be robustly implemented to connect to actual healthcare data sources and perform comprehensive feature engineering that aligns with the training pipeline.
• Multi-Label Prediction: The current training script simplifies the prediction task. A real system might need to predict multiple relevant treatments, requiring a multi-label classification approach or a different recommendation algorithm.
• Data Privacy and Security: Implementing stringent security measures and adhering to healthcare compliance (e.g., HIPAA) is paramount throughout the entire system.
• Model Evaluation and Monitoring: Rigorous evaluation of the model’s performance using appropriate healthcare-specific metrics and continuous monitoring in production are essential.

This comprehensive guide provides a detailed overview of building a Personalized Healthcare Recommendations AI Agent on GCP, covering the essential components from infrastructure to model serving. Remember that building a production-ready system requires careful attention to data integration, security, scalability, and ethical considerations.