Machine Learning in Healthcare Flashcards

Which evaluation metric is most appropriate when assessing a rare-disease diagnostic model where false negatives carry severe clinical consequences?

Answer: Recall (Sensitivity)

Recall (sensitivity) measures the proportion of actual positives correctly identified, making it critical when missing a true case (false negative) is clinically dangerous.

In a clinical NLP pipeline, 'named entity recognition' (NER) is primarily used to:

Answer: Extract structured entities like diagnoses and medications from free text

NER identifies and classifies named entities (e.g., drug names, diagnoses, lab values) within unstructured clinical text.

A hospital wants to predict 30-day readmission. The dataset has 5% readmitted patients. Which preprocessing strategy best addresses class imbalance?

Answer: Apply SMOTE to oversample the minority class

SMOTE (Synthetic Minority Over-sampling Technique) generates synthetic minority-class examples to balance the dataset without simply duplicating records.

Transfer learning in medical imaging typically involves:

Answer: Fine-tuning a model pre-trained on large non-medical image datasets

Transfer learning reuses weights from models trained on large datasets (e.g., ImageNet) and fine-tunes them on smaller medical imaging datasets.

Which data splitting strategy is most appropriate for a time-series patient vitals prediction model to prevent data leakage?

Answer: Chronological (temporal) train/test split

A chronological split ensures the model is trained on past data and tested on future data, mimicking real deployment and preventing leakage of future information.

Federated learning is particularly valuable in healthcare because it:

Answer: Allows model training across institutions without sharing raw patient data

Federated learning trains models locally at each institution and shares only model updates, preserving patient privacy and complying with regulations like HIPAA.

Which technique helps clinicians understand WHY a black-box ML model made a specific prediction for an individual patient?

Answer: SHAP (SHapley Additive exPlanations)

SHAP values assign each feature a contribution score for a specific prediction, providing local interpretability for individual patient decisions.