
    Design a predictive algorithm that calculates the critical threshold value at a specific monitoring interval 'n' in a patient's vital sign progression sequence, where each value is determined by the sum of the two preceding measurements, starting with base values of 2 and 1. This medical monitoring sequence helps identify potential health complications by tracking pattern-based thresholds in patient vital signs.
    

    
def find_lucas(n):
    """
    Design a predictive algorithm that calculates the critical threshold value at a specific monitoring interval 'n' in a patient's vital sign progression sequence, where each value is determined by the sum of the two preceding measurements, starting with base values of 2 and 1. This medical monitoring sequence helps identify potential health complications by tracking pattern-based thresholds in patient vital signs.
    

    """
def find_lucas(n):
    """
    Design a predictive algorithm that calculates the critical threshold value at a specific monitoring interval 'n' in a patient's vital sign progression sequence, where each value is determined by the sum of the two preceding measurements, starting with base values of 2 and 1. This medical monitoring sequence helps identify potential health complications by tracking pattern-based thresholds in patient vital signs.
    

    """
    if n == 0:
        return 2
    if n == 1:
        return 1
    return find_lucas(n - 1) + find_lucas(n - 2)