AI in Pharmacy

Pharmacokinetic Modeling

import numpy as np
from scipy.integrate import odeint

class PharmacokineticModel:
    def __init__(self, ka, ke, vd, f):
        self.ka, self.ke, self.vd, self.f = ka, ke, vd, f

    def predict_concentration(self, dose, times):
        c0 = (dose * self.f) / self.vd
        def model(y, t):
            return [-self.ke * y[0]]
        return odeint(model, [c0], times)[:, 0]

    def optimize_dose(self, target_conc, times):
        dose = 100
        for _ in range(50):
            peak = np.max(self.predict_concentration(dose, times))
            if abs(peak - target_conc) < 0.1 * target_conc:
                break
            dose *= target_conc / peak
        return dose

Pharmacy Inventory

Economic Order Quantity

class PharmacyInventoryAI:
    def calculate_reorder_point(self, drug_id, lead_time=3):
        history = self.demand_history.get(drug_id, [])
        avg_daily = np.mean(history) if history else 0
        std_daily = np.std(history) if history else 0
        safety_stock = 1.65 * std_daily * np.sqrt(lead_time)
        return avg_daily * lead_time + safety_stock

Counterfeit Drug Detection

Spectral Analysis