AI in Pediatric Healthcare

Growth Chart Analysis

CDC Growth Percentiles (LMS Method)

import numpy as np
from scipy.stats import norm

class GrowthChartAnalyzer:
    def __init__(self, reference_data):
        self.L = reference_data['L']
        self.M = reference_data['M']
        self.S = reference_data['S']

    def percentile(self, measurement, age_months):
        L = np.interp(age_months, self.L['age'], self.L['values'])
        M = np.interp(age_months, self.M['age'], self.M['values'])
        S = np.interp(age_months, self.S['age'], self.S['values'])
        z = ((measurement / M) ** L - 1) / (L * S) if L != 0 else np.log(measurement / M) / S
        return norm.cdf(z) * 100

    def detect_abnormal_growth(self, measurements, ages):
        percentiles = [self.percentile(m, a) for m, a in zip(measurements, ages)]
        velocity = np.diff(percentiles) / np.diff(ages)
        return [(ages[i], percentiles[i], v) for i, v in enumerate(velocity) if abs(v) > 2]

Neonatal Monitoring

Heart Rate Variability

Apnea Detection

class NeonatalMonitor:
    def detect_apnea(self, ppg_signal, window_sec=15, fs=250):
        window_size = int(window_sec * fs)
        detections = []
        for i in range(0, len(ppg_signal) - window_size, window_size // 2):
            window = ppg_signal[i:i + window_size]
            if np.max(window) - np.min(window) < 0.1 * np.std(ppg_signal):
                detections.append((i / fs, 'apnea'))
        return detections

Evaluation

• Growth percentile accuracy vs manual assessment
• Screening sensitivity for true positives
• False positive rate for referrals
• Alert response time from detection to notification