Clinical Trial Matching

Patient Eligibility Prediction

class TrialMatcher:
    def __init__(self):
        self.criteria_models = {}

    def predict_eligibility(self, patient, trial):
        scores = []
        for criterion in trial['criteria']:
            model = self.criteria_models.get(criterion['type'])
            if model:
                scores.append(model.predict_proba(patient)[0, 1])
        return np.mean(scores) if scores else 0

    def rank_trials(self, patient, trials):
        scored = [(t, self.predict_eligibility(patient, t)) for t in trials]
        return sorted(scored, key=lambda x: x[1], reverse=True)

Trial Outcome Modeling

Sample Size Estimation

Adaptive Trial Design

Protocol Optimization

Endpoint Selection