symptom_assessment.py

from sklearn.feature_extraction.text import TfidfVectorizer

class SymptomAssessment:
    def __init__(self):
        # Example disease-symptom mapping dictionary.
        # In practice, replace this with a robust dataset.
        self.disease_symptoms = {
            "Flu": ["fever", "cough", "sore throat", "fatigue"],
            "Migraine": ["headache", "nausea", "sensitivity to light"],
            "COVID-19": ["fever", "cough", "shortness of breath", "loss of taste"]
        }
        # Prepare vector space for diseases
        self.vectorizer = TfidfVectorizer()
        self.diseases = list(self.disease_symptoms.keys())
        symptom_texts = [" ".join(self.disease_symptoms[d]) for d in self.diseases]
        self.vectors = self.vectorizer.fit_transform(symptom_texts)
    
    def assess(self, symptoms_list):
        """
        Given a list of reported symptoms, determine the best matching disease
        and identify which expected symptoms are missing.
        """
        input_text = " ".join(symptoms_list)
        input_vector = self.vectorizer.transform([input_text])
        similarities = (self.vectors * input_vector.T).toarray().flatten()
        best_match_index = similarities.argmax()
        best_disease = self.diseases[best_match_index]
        missing_symptoms = list(set(self.disease_symptoms[best_disease]) - set(symptoms_list))
        assessment = (f"Based on the input symptoms, {best_disease} is suspected. "
                      f"Missing symptoms for improved diagnosis: {missing_symptoms}")
        return missing_symptoms, assessment