Clinical Decision Support Systems

Bayesian Diagnostic Models

Bayes' Theorem

Naive Bayes

import numpy as np

class BayesianDiagnosticModel:
    def __init__(self):
        self.disease_priors = {}
        self.likelihoods = {}

    def train(self, X, y, diseases):
        for disease in diseases:
            mask = y == disease
            self.disease_priors[disease] = np.mean(mask)
            self.likelihoods[disease] = np.mean(X[mask], axis=0)

    def predict(self, x, diseases):
        posteriors = {}
        for disease in diseases:
            log_prior = np.log(self.disease_priors[disease])
            log_likelihood = np.sum(
                x * np.log(self.likelihoods[disease] + 1e-10) +
                (1 - x) * np.log(1 - self.likelihoods[disease] + 1e-10))
            posteriors[disease] = log_prior + log_likelihood
        total = sum(np.exp(v) for v in posteriors.values())
        return {d: np.exp(v) / total for d, v in posteriors.items()}

Treatment Recommendation Systems

Multi-Armed Bandit

Personalized Treatment

Clinical Alert Systems

Early Warning Score

Evaluation