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19d6abb | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 | from core.llm import ask_claude
def process_data(patient_dict: dict) -> dict:
"""
Validates patient data against realistic clinical ranges,
flags abnormalities, and generates an AI summary of the baseline profile.
"""
flags = []
# 1. Clinical Boundary Validation & Flagging
if patient_dict["blood_pressure_systolic"] >= 130 or patient_dict["blood_pressure_diastolic"] >= 80:
flags.append("Elevated or High Blood Pressure (Hypertension Risk)")
if patient_dict["blood_sugar_fasting"] >= 100:
flags.append("Elevated Fasting Blood Glucose (Prediabetes/Diabetes Risk)")
if patient_dict["bmi"] >= 25.0:
flags.append("BMI indicates Overweight or Obese range")
if patient_dict["cholesterol_total"] >= 200:
flags.append("High Total Cholesterol")
if patient_dict["hdl_cholesterol"] < 40 and patient_dict["sex"].lower() == "male":
flags.append("Low HDL (Good) Cholesterol for Males")
if patient_dict["hdl_cholesterol"] < 50 and patient_dict["sex"].lower() == "female":
flags.append("Low HDL (Good) Cholesterol for Females")
if patient_dict["sleep_hours"] < 6:
flags.append("Suboptimal sleep duration (Chronic sleep deprivation risk)")
if patient_dict["stress_level"].lower() == "high":
flags.append("High perceived stress levels affecting cortisol balance")
if patient_dict["cortisol"] > 20:
flags.append("Elevated cortisol levels (Potential chronic stress marker)")
# 2. Package the clean profile data
clean_profile = patient_dict.copy()
clean_profile["flags"] = flags
# 3. Use LLM to write a plain English medical summary
system_prompt = (
"You are an expert clinical triage assistant. Review the provided patient vital signs "
"and lifestyle metrics. Write a professional, plain English 3-sentence summary of their "
"current baseline health status. Highlight any severe metrics or explicit combinations "
"of risk factors (like smoking alongside elevated blood pressure) without diagnosing them."
)
user_msg = f"Patient Health Metrics:\n{clean_profile}"
# Generate summary using our OpenRouter wrapper
summary = ask_claude(system_prompt, user_msg)
clean_profile["profile_summary"] = summary
return clean_profile |