Algorithm validation typically involves several steps: 1. Data Collection: Gathering a large and representative dataset that includes various neonatal conditions. 2. Training and Testing: Training the algorithm on a portion of the dataset and testing it on another to evaluate its performance. 3. Performance Metrics: Using metrics like sensitivity, specificity, accuracy, and area under the curve (AUC) to assess the algorithm's performance. 4. Clinical Validation: Comparing the algorithm's predictions with actual clinical outcomes through retrospective studies or prospective clinical trials.
