Uncertainty-Guided Latent Diagnostic Trajectory Learning for Sequential Clinical Diagnosis

1. 1Model clinical diagnosis as a sequential decision process under uncertainty
2. 2Use uncertainty-guided latent diagnostic trajectory learning to acquire clinical evidence over time
3. 3Learn effective diagnostic trajectories by optimizing the acquisition of evidence
4. 4Evaluate the performance of the diagnostic system using clinical metrics

Data scientists and AI engineers on healthcare teams can benefit from this approach to develop more effective diagnostic systems, while clinicians can use these systems to make more informed decisions

💡 Modeling uncertainty in clinical diagnosis can lead to more effective diagnostic trajectories

🚑 Uncertainty-guided diagnostic trajectories improve clinical diagnosis 📊