Editing AI for Clinical Trials (section)

== <span style="color: #FFFFFF;">Applying</span> ==
'''EHR-based patient eligibility matching for clinical trials:'''
<syntaxhighlight lang="python">
from transformers import pipeline
import pandas as pd
import re

# NLP-based eligibility criteria parsing + patient matching

# Step 1: Parse eligibility criteria using LLM
criteria_text = """
Inclusion Criteria:
1. Age 18-75 years
2. Confirmed diagnosis of Type 2 Diabetes Mellitus (HbA1c 7.0-10.0%)
3. BMI 25-40 kg/m2
4. eGFR >= 45 mL/min

Exclusion Criteria:
1. Current use of GLP-1 receptor agonist
2. History of pancreatitis
3. Severe renal impairment (eGFR < 30)
4. Pregnancy or lactation
"""

def parse_criteria_to_rules(criteria_text):
    """Parse criteria text into structured rules using LLM."""
    from openai import OpenAI
    client = OpenAI()
    response = client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[{"role": "user",
                   "content": f"Parse these clinical trial criteria into JSON with fields: criterion_type (inclusion/exclusion), measurement, operator, value, unit.\n\n{criteria_text}"}]
    )
    return response.choices[0].message.content

# Step 2: Match patients from EHR structured data
def check_patient_eligibility(patient: dict, criteria: list) -> dict:
    """Check if a patient meets trial eligibility criteria."""
    results = {}
    for c in criteria:
        # Apply each parsed criterion to patient data
        field = c['measurement']
        val = patient.get(field)
        if val is None:
            results[field] = 'MISSING_DATA'
            continue
        ops = {'>=': val >= c['value'], '<=': val <= c['value'],
               '>': val > c['value'], '<': val < c['value']}
        meets = ops.get(c['operator'], False)
        if c['criterion_type'] == 'exclusion':
            meets = not meets
        results[field] = 'ELIGIBLE' if meets else 'INELIGIBLE'
    eligible = all(v == 'ELIGIBLE' for v in results.values())
    return {'eligible': eligible, 'criteria_results': results}

# Step 3: Predict dropout risk for enrolled patients
from sklearn.ensemble import GradientBoostingClassifier
# Train on historical trial data: demographics, travel distance, # comorbidities,
# prior trial completion, insurance status → probability of dropout
dropout_model = GradientBoostingClassifier(n_estimators=100)
# Use predictions to target retention interventions at high-risk patients
</syntaxhighlight>

; Clinical trial AI tools
: '''Patient matching''' → Trinetx, Deep 6 AI, Mendel.ai, TrialSpark
: '''Trial management''' → Medidata Rave (with AI), Veeva Vault Clinical, Oracle Clinical One
: '''Adaptive design''' → FACTS (Berry Consultants), Cytel East, ADDPLAN
: '''Safety monitoring''' → Oracle Argus + AI, BioClinica Signal Detection
: '''Synthetic controls''' → RWE.ai, Aetion, Flatiron Health
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