
    Develop a function to assess patient readmission risk by analyzing historical medical records across decreasing time intervals. Starting with the full record length of n days, calculate the sum of risk assessments at progressively shorter intervals, where each interval decreases by 2 days. The analysis should continue until the interval reaches zero or becomes negative, combining risk scores from longer-term patterns (n days) with medium-term (n-2 days) and short-term patterns (continuing the reduction by 2 days each time). For example, with a 30-day record, the analysis would evaluate readmission patterns at 30, 28, 26 days, and so on, summing the risk scores from each interval to generate a comprehensive risk assessment value. This temporal analysis helps identify critical periods where readmission risk may be elevated.
    

    
def sum_series(n):
    """
    Develop a function to assess patient readmission risk by analyzing historical medical records across decreasing time intervals. Starting with the full record length of n days, calculate the sum of risk assessments at progressively shorter intervals, where each interval decreases by 2 days. The analysis should continue until the interval reaches zero or becomes negative, combining risk scores from longer-term patterns (n days) with medium-term (n-2 days) and short-term patterns (continuing the reduction by 2 days each time). For example, with a 30-day record, the analysis would evaluate readmission patterns at 30, 28, 26 days, and so on, summing the risk scores from each interval to generate a comprehensive risk assessment value. This temporal analysis helps identify critical periods where readmission risk may be elevated.
    

    """
def sum_series(n):
    """
    Develop a function to assess patient readmission risk by analyzing historical medical records across decreasing time intervals. Starting with the full record length of n days, calculate the sum of risk assessments at progressively shorter intervals, where each interval decreases by 2 days. The analysis should continue until the interval reaches zero or becomes negative, combining risk scores from longer-term patterns (n days) with medium-term (n-2 days) and short-term patterns (continuing the reduction by 2 days each time). For example, with a 30-day record, the analysis would evaluate readmission patterns at 30, 28, 26 days, and so on, summing the risk scores from each interval to generate a comprehensive risk assessment value. This temporal analysis helps identify critical periods where readmission risk may be elevated.
    

    """
    if n < 1:
        return 0
    else:
        return n + sum_series(n - 2)