Hey there! Let's break this down step by step—no need to overcomplicate things since you just need the basics to get started. I'll walk you through reading a TXT file, parsing its data, and building the matching logic you need.

First, let's assume your TXT file uses a clear separator (like |) to split each field per line. For example:

Alice|Female|32|Lisinopril|Hypertension|Dizziness, Dry cough
Bob|Male|45|Metformin|Type 2 Diabetes|Nausea, Fatigue
Charlie|Male|38|Lisinopril|Hypertension|Headache

If your file uses commas, tabs, or another separator, just adjust the parsing code later—this is just a common, easy-to-use format.

We'll use Python for this (it's the most beginner-friendly language for quick scripting like this). Here's a simple function to read the file and turn each line into a structured dictionary:

def read_medical_records(file_path):
    records = []
    # Use 'with open' to safely handle file operations (auto-closes the file)
    with open(file_path, 'r', encoding='utf-8') as f:
        # Uncomment the line below if your TXT has a header row (e.g., "Name|Gender|Age...")
        # next(f)
        for line in f:
            # Remove extra newlines and split the line by your separator
            parts = line.strip().split('|')
            # Convert each line into a dictionary for easy access later
            record = {
                'name': parts[0],
                'gender': parts[1],
                'age': int(parts[2]),  # Convert age to integer for numerical comparison
                'medication': parts[3],
                'condition': parts[4],
                'side_effects': parts[5]
            }
            records.append(record)
    return records

This function takes your TXT file path and returns a list of dictionaries—each dictionary represents one person's record, with clear labels for each piece of data.

Next, we'll get the user's info using simple input prompts:

# Get user's details
user_name = input("Enter your name: ")
user_gender = input("Enter your gender (e.g., Male/Female): ")
user_age = int(input("Enter your age: "))
user_medication = input("Enter your medication: ")
user_condition = input("Enter your medical condition: ")

Now we'll loop through the records we read, first matching the condition, then checking if age, gender, or medication aligns. We'll collect any matching side effects (and avoid duplicates):

# Load records from your TXT file (replace with your actual file path)
records = read_medical_records('medical_records.txt')

# Store unique side effects from matching records
matched_side_effects = []

for record in records:
    # First: Exact match on condition (case-insensitive to avoid typos)
    if record['condition'].lower() == user_condition.lower():
        # Second: Check if age, gender, or medication matches (adjust logic if you need "and" instead of "or")
        age_match = abs(record['age'] - user_age) <= 5  # Allow ±5 year age range (more flexible than exact match)
        gender_match = record['gender'].lower() == user_gender.lower()
        med_match = record['medication'].lower() == user_medication.lower()
        
        if age_match or gender_match or med_match:
            # Split side effects and add to our list (no duplicates)
            effects = record['side_effects'].split(', ')
            for effect in effects:
                if effect not in matched_side_effects:
                    matched_side_effects.append(effect)

# Print the results
if matched_side_effects:
    print(f"Based on matching records, potential side effects to note:")
    for effect in matched_side_effects:
        print(f"- {effect}")
else:
    print("No matching records found—no side effect references available.")

• Safe File Handling: Using with open() ensures the file is closed automatically, so you don't have to worry about resource leaks.
• Case Insensitivity: Converting text to lowercase (.lower()) prevents mismatches from things like "Hypertension" vs "hypertension".
• Flexible Age Matching: Using an age range (±5 years) is more practical than requiring an exact age match—you can tweak this number or switch to exact equality if needed.
• Duplicate Prevention: We only add each side effect once, even if it appears in multiple records.

Save the sample TXT content I listed earlier as medical_records.txt, run the script, and input something like:

Enter your name: David
Enter your gender: Male
Enter your age: 35
Enter your medication: Lisinopril
Enter your medical condition: Hypertension

You'll get output like:

Based on matching records, potential side effects to note:

• Dizziness
• Dry cough
• Headache

If your TXT uses a different separator (like commas), just replace split('|') with split(',') and adjust the parsing accordingly.

内容的提问来源于stack exchange，提问作者Jackson Wicktora