""" Clinical ranges for health metrics with programmatic status and score calculation. """ def parse_blood_pressure(bp_str): """Parse blood pressure string like '120/80' into systolic/diastolic""" try: parts = bp_str.split('/') return int(parts[0]), int(parts[1]) except: return 120, 80 # Default if parsing fails def get_metric_evaluation(metric_name, value): """ Returns (status, score) for a given metric and value. Status: Normal, High, Low, Optimal, Borderline, Critical Score: 0-10 based on clinical ranges """ evaluations = { "heart_rate": lambda v: ( ("Normal", 10) if 51 <= v <= 100 else ("Borderline Low", 7) if v == 50 else ("Borderline High", 7) if 100 < v <= 110 else ("Low", 3) if v < 50 else ("High", 3) ), "breathing_rate": lambda v: ( ("Normal", 10) if 12 <= v <= 20 else ("Borderline Low", 7) if 10 <= v < 12 else ("Borderline High", 7) if 20 < v <= 24 else ("Low", 3) if v < 10 else ("High", 3) ), "oxygen_saturation": lambda v: ( ("Normal", 10) if v >= 94 else ("Borderline Low", 7) if 91 <= v < 94 else ("Low", 4) if 88 <= v < 91 else ("Critically Low", 0) ), "blood_pressure": lambda v: ( (lambda s, d: ( # Get individual statuses (lambda sys_status, dia_status, score: ( f"Systolic {sys_status}, Diastolic {dia_status}", score ))( # Systolic status "Crisis" if s > 180 else "Stage 2 High" if s >= 140 else "Stage 1 High" if 130 <= s < 140 else "Elevated" if 120 <= s < 130 else "Normal", # Diastolic status "Crisis" if d > 120 else "Stage 2 High" if d >= 90 else "Stage 1 High" if 80 <= d < 90 else "Normal", # Score (use worst case) 0 if s > 180 or d > 120 else 3 if s >= 140 or d >= 90 else 6 if 130 <= s < 140 or 80 <= d < 90 else 8 if 120 <= s < 130 else 10 ) ))(*parse_blood_pressure(v)) if isinstance(v, str) else ("Systolic Normal, Diastolic Normal", 10) ), "stress_level": lambda v: ( ("Normal", 10) if v in ["Low", "Normal"] else ("Mild", 7) if v == "Mild" else ("High", 4) if v == "High" else ("Very High", 2) ), "heart_variability": lambda v: ( ("Normal", 10) if 50 <= v <= 100 else ("Borderline Low", 7) if 30 <= v < 50 else ("Borderline High", 7) if 100 < v <= 150 else ("Low", 4) if v < 30 else ("High", 4) ), "prq": lambda v: ( ("Optimal", 10) if 80 <= v <= 100 else ("Normal", 7) if 60 <= v < 80 else ("Low", 4) ), "activity": lambda v: ( ("Optimal", 10) if v >= 7 else ("Good", 7) if 5 <= v < 7 else ("Fair", 4) if 3 <= v < 5 else ("Low", 2) ), "sleep": lambda v: ( ("Optimal", 10) if 7 <= v <= 9 else ("Good", 7) if 6 <= v < 7 or 9 < v <= 10 else ("Poor", 4) ), "equilibrium": lambda v: ( ("Optimal", 5) if v >= 7 else ("Good", 4) if 5 <= v < 7 else ("Fair", 3) if 3 <= v < 5 else ("Low", 1) ), "metabolism": lambda v: ( ("Optimal", 10) if v >= 7 else ("Good", 7) if 5 <= v < 7 else ("Fair", 4) if 3 <= v < 5 else ("Low", 2) ), "relaxation": lambda v: ( ("Optimal", 10) if v >= 7 else ("Good", 7) if 5 <= v < 7 else ("Fair", 4) if 3 <= v < 5 else ("Low", 2) ), "cardiovascular_age": lambda v: ( ("Optimal", 10) if v <= 35 else ("Good", 7) if v <= 45 else ("Fair", 4) if v <= 55 else ("High", 2) ), "hemoglobin": lambda v: ( ("Normal", 10) if 13.5 <= v <= 17.5 else ("Borderline Low", 7) if 12 <= v < 13.5 else ("Borderline High", 7) if 17.5 < v <= 19 else ("Low", 3) if v < 12 else ("High", 3) ), "cholesterol": lambda v: ( ("Normal", 10) if v < 200 else ("Borderline High", 6) if 200 <= v < 240 else ("High", 2) ), "a1c_risk": lambda v: ( ("Normal", 10) if v < 5.7 else ("Prediabetic", 6) if 5.7 <= v < 6.5 else ("Diabetic", 2) ), "cholesterol_risk": lambda v: ( ("Normal", 10) if v < 5 else ("Borderline High", 7) if 5 <= v < 7.5 else ("Intermediate", 4) if 7.5 <= v < 20 else ("High", 2) ), "wellness_score": lambda v: ( ("Optimal", 10) if v >= 8 else ("Good", 8) if 6 <= v < 8 else ("Fair", 5) if 4 <= v < 6 else ("Poor", 2) ), "atrial_fibrillation": lambda v: ( ("Normal", 10) if v == "NSR" else ("Abnormal", 3) if v == "AFib" else ("Error", 0) ), "hba1c": lambda v: ( ("Normal", 10) if v < 5.7 else ("Prediabetic", 6) if 5.7 <= v < 6.5 else ("Diabetic", 2) ), "cardiovascular_bmi": lambda v: ( ("Normal", 10) if 18.5 <= v <= 24.9 else ("Underweight", 7) if v < 18.5 else ("Overweight", 7) if 25.0 <= v < 30.0 else ("Obese", 3) ), "a1c_range": lambda v: ("Normal", 10), # Display range, default to Normal/10 if present "cholesterol_range": lambda v: ( ("Normal", 10) if v == "Normal" else ("Borderline High", 6) if v == "Borderline High" else ("High", 2) if v == "High" else ("Normal", 10) ), } # Get the evaluation function for this metric eval_func = evaluations.get(metric_name.lower()) if eval_func: try: return eval_func(value) except: return ("Normal", 5) # Default on error return ("Normal", 5) # Default for unknown metrics def evaluate_all_metrics(data_dict): """ Evaluate all metrics from input data. Returns dict of {metric_name: {"status": str, "score": int}} """ results = {} for key, value in data_dict.items(): if value is not None: status, score = get_metric_evaluation(key, value) results[key] = {"status": status, "score": score} return results